improved coordination of the production cycle in the paper...

Improved coordination of the production cycle in the paper industry

To manage a wide product range and sequence-dependent changeovers Förbättrad koordinering av produktionscykeln inom pappersindustrin För att hantera ett brett produktsortiment och sekvens-beroende omställningar Carl Johansson Erik Strandberg

Faculty: Health, Science & Technology Subject: Industrial Economics and Managament Points: 30 ECTS Supervisors: Dan Nordin and Leo de Vin Examiner: Mikael Johnson Date: 2015-06-09 Serial Number:

Preface

This thesis is the final part of our education for Industrial Engineering and Management at Karlstad University. We especially want to thank our supervisors Dan Nordin and Leo de Vin for their guidance during this thesis; we also want to thank all of the people at the case company that have helped us with information and guidance. The work and text should be considered to be done and written by both the authors.

Carl Johansson and Erik Strandberg

Karlstad June 05, 2015

Abstract Today the manufacturing industry is faced with the challenge to stay competitive in an era of shorter product lifecycles and increased product variety. This has forced manufacturing companies to deal with an increased amount of products and more and faster changes to the product line. The same trend can also be seen in the paper industry where bulk products decreasing and more diversified products in smaller lot-sizes increasing, with a greater focus on customer specific products. Traditionally the paper industry has focused on a volume-based strategy, which is not adapted to the new situation. This has led to problems with coordinating manufacturing, to manage a wide product range and sequence-dependent changeovers. Because of the sequence-dependent changeovers the production is often scheduled through a production cycle. This reduces the waste between grade changes and gives high machine utilization, but also leads to problem with producing the right quality at the right time. This leads to high inventory levels and difficulties to react and meet changes in demand. Therefore there is a need for paper producers with a wide range of products to find a balance between high machine utilization and the ability to react to changes in demand.

The aim of this thesis is to investigate how paper producers can coordinate their production cycle and by that manage a wide product range with sequence-dependent changeovers to achieve mix flexibility and improve the scheduling at the plant level. To coordinate the production cycle on a paper machine the product wheel concept has been used, which is a technique for production scheduling, developed and modified from the lean tool heijunka to fit in the process industry. In this thesis a single case study has been done on a producer of colored tissue. Findings from this thesis show a shorter production cycle with fewer products in each cycle, which results in the opportunity to be more flexible in the production, as there are more opportunities to produce a specific product during a year. The product wheel concept is also shown to take in to account the key factors that are important for mix flexibility. The results from the case study show that the product wheel concept is an appropriate model for scheduling problems in the context of managing a wide product range on a paper machine. The production wheel concept is though a heuristic model and will require knowledge and experience about the observed object.

Sammanfattning

Idag så står tillverkningsindustrin inför utmaningen att förbli konkurrenskraftiga i en tid präglad av kortare produktlivscykler och ökad produktutbud. Detta har tvingat tillverkande företag att ta itu med en ökad mängd produkter och fler och snabbare förändringar i sortimentet. Denna trend kan även ses i pappersindustrin där vi ser mindre bulkprodukter och mer diversifierade produkter och i allt mindre partistorlekar, med ett ökat fokus på kundspecifika produkter. Traditionellt har pappersindustrin fokuserat på en volymbaserad strategi, vilket inte är anpassad till den nya situationen. Detta har lett till problem med att samordna tillverkningen, hantera ett brett produktsortiment och skevensberoende kvalitetsändringar. På grund av de sekvensberoende omställningarna schemaläggs produktionen genom en produktionscykel. Vilket minskar spill mellan kvalitetsändringarna och ger ett högt maskinutnyttjande, men leder också till problem med att producera rätt kvalitet i rätt tid. Detta leder till höga lagernivåer och svårigheter att reagera och möta förändringar i efterfrågan. Därför finns det ett behov för pappersproducenter som har ett brett produktsortiment att hitta en balans mellan hög maskinutnyttjande och förmågan att reagera på förändringar i efterfrågan.

Syftet med detta examensarbete är att undersöka hur papperstillverkare kan samordna sin produktionscykel och hantera ett brett produktsortiment med sekvensberoende omställningar för att uppnå mix flexibilitet och förbättra schemaläggningen på fabriksnivå. För att samordna produktionscykeln på en pappersmaskin har ”Product wheel” konceptet använts, som är en teknik för produktionsplanering, vilket är utvecklat och modifierat från lean verktyget heijunka för att passa in i processindustrin. Resultaten visar en kortare produktionscykel med färre produkter i varje cykel, vilket resulterar i möjligheten till en flexiblare produktion, eftersom en specifik produkt kan tillverkas vid fler tillfällen per år. ”Product wheel” konceptet tar även hänsyn till de viktigaste faktorerna som påverkar ett företags mixflexibilitet. Resultaten från fallstudien visar att ”Product wheel” konceptet en lämplig modell för schemaläggnings problem i samband med hantering av ett brett produktsortiment på en pappersmaskin. ”Product wheel” konceptet är dock en heuristisk modell och kommer att kräva kunskap och erfarenhet om det observerade objektet.

Terminology Base weight - A way to classify how much material there is in a

paper product, the mass per square meter (g/m2). Board - A thick paper-based material can also be called

paperboard or cardboard. Calender - Smoothing the dried paper under high load and

pressure Color bleeding - The color is transferred to other objects when

subjected to fluids, can be dependent on what kind of fluid.

Doctor blade - Blade that is scraping the dry paper off the Yankee cylinders surface to achieve creping.

Grade - One specific type of paper product with specified characteristics.

Grade change - Changing the production from one grade to another. Headbox - The pressure chamber where turbulence is applied to

break up fibre clumps in the pulp slurry before being applied on the wire in the paper machine.

Heijunka - A technique for reducing unevenness in production. MTO - Producing only what and when something is ordered

by a customer. MTS - Producing to keep up a high inventory level to be able

to deliver to the customers at once. Pulp - A lignocellulosic fibrous material prepared chemically

or mechanically that is used to make paper. Stock - A pulp slurry that has been processed in the stock

preparation to it have the right properties that is needed for the grade of paper being produced.

Tambour - A big roll of paper, which is rolled up on a tambour reel at the end of the paper machine.

Tissue - A lightweight paper or, light crêpe paper. Wire - The woven mesh fabric that is used for draining the

pulp slurry from the headbox and forming the paper. Yankee Cylinder - Giant drying cylinder that is used to dry certain types

of paper, including tissue.

Table of Contents 1. Introduction ................................................................... 13

1.1. Background ............................................................................................ 13

1.2. The Case ................................................................................................. 15

1.3. Problem analysis ..................................................................................... 16

1.4. Aim ......................................................................................................... 17

1.5. Research questions ................................................................................. 17

1.6. Delimitations .......................................................................................... 17

2. Theoretical framework ................................................. 19

2.1. Introduction of mix flexibility ................................................................ 19

2.2. Uncertainty ............................................................................................. 20

2.3. Product variety ....................................................................................... 20

2.4. Resource configuration ........................................................................... 21

2.5. Mix flexibility in the process industries ................................................. 22

2.6. Lean in the process industries................................................................. 23

2.7. The product wheel concept ..................................................................... 27

2.8. Cyclic production ................................................................................... 35

2.9. Production planning and scheduling ...................................................... 37

2.10. Make-to-Order / Make-to-Stock ......................................................... 38

2.11. Inventory ............................................................................................. 39

2.11.1. Tied up capital ............................................................................. 39

2.11.2. Inventory carrying cost ................................................................ 39

2.12. Motivation of choice ........................................................................... 41

3. Method ............................................................................ 43

3.1. Methodology .......................................................................................... 43

3.2. Research design ...................................................................................... 44

3.2.1. Primary data .................................................................................... 45

3.2.1. Secondary data: ............................................................................... 47

3.2.2. Data analysis ................................................................................... 47

3.3. Reliability and Validity .......................................................................... 48

4. Paper manufacturing .................................................... 49

4.1. The paper machine ................................................................................. 49

4.2. Stock preparation .................................................................................... 49

4.3. Short circulation ..................................................................................... 50

4.4. The main differences between Tissue manufacturing and paper/board manufacturing .................................................................................................... 50

5. Empirical findings/Analysis .......................................... 53

5.1. Changeover procedures at the case company ......................................... 53

5.2. Using the Product wheel to set up a production cycle ............................ 55

5.3. Testing the new cycles ............................................................................ 61

6. Discussion ....................................................................... 65

6.1. The case company’s different changeover types .................................... 65

6.2. RQ1: How can paper producers coordinate the scheduling of the production cycle in an efficient manner, in order to improve the flexibility? ... 65

6.3. RQ2: How will an improved flexible production cycle occur compered to the current situation, for the case company? ..................................................... 66

6.4. RQ3: What will be the effects of the suggested production cycle, in terms of cost and flexibility for the studied company’s perspective? ............... 67

6.5. General Discussion ................................................................................. 67

7. Conclusion ...................................................................... 71

8. References ....................................................................... 73

9. Appendix 1: Interview questions ................................. 77

10. Appendix 2: Demand and Demand variability ........... 78

11. Appendix 3: Production cycles created with the product wheel ...................................................................... 80

12. Appendix 4: Total cost diagrams of for the different cycles not shown in chapter 5 and inventory carrying rates. ..................................................................................... 83

List of Figures Figure 1: Visualization of the product wheel concept (Adapted from King (2009) ............................................................................................................................... 28 Figure 2: Decision matrix for MTO and MTS products (King 2009)................... 29 Figure 3: The EOQ concept. ................................................................................. 32 Figure 4: Monthly demand for the different colors produced. .............................. 55 Figure 5: Decision matrix for classified colors as either MTS or MTO. .............. 57 Figure 6: Total cost for 25 colors per cycle and inventory carrying cost (r = 15%), depending on how many cycles per year. ............................................................. 62 Figure 7: Total cost for 17 colors per cycle and inventory carrying cost (r = 15%), depending on how many cycles per year and compared with today’s total cost for 25 colors per cycle. ............................................................................................... 63 Figure 8: Total cost diagram for 16 color / cycle, r = 20 %. ................................. 83 Figure 9: Total cost diagram for 18 color / cycle, r = 10 %. ................................. 83

List of Tables Table 1: Various lean tools and their applicability in the process industry (Abdulmalek & Rajgopal 2007; Abdulmalek et al. 2006) .................................... 26 Table 2: Demand variability for a sample of the products. ................................... 56 Table 3: EOQ, optimum and recommended frequency for a sample of the colors (r = 15%). .................................................................................................................. 59 Table 4: Color distribution over the cycles during one year. ................................ 60 Table 5: Demand and Demand Variability for the different colors ...................... 78 Table 6: Cycle from the product wheel r = 10 % .................................................. 80 Table 7: Cycle from the product wheel r = 15 % .................................................. 81 Table 8: Cycle from the product wheel r = 20 % .................................................. 82

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1. Introduction

1.1. Background

The manufacturing industry is today facing challenges to satisfied customer demand, due to shorter product lifecycles and increasing product variety. The dynamic and variation in demand, is forcing manufacturing companies to be flexible in their production system in order to retain a competitive position in the marketplace (Metternich et al. 2013). Thereby, a production system needs to be able to adapt to changing demands from customers in the fastest way as possible (Metternich et al. 2013). From an economical point of view, high flexibility leads to higher costs. Therefore a balance between flexibility and costs is needed, and the production system should not be more flexible than is necessary (Metternich et al. 2013). The main reasons for manufacturing firms to have manufacturing flexibility at the plant level are uncertainty and product variety (Wilson & Platts 2010).

Today we see a decline in the production of bulk products in the paper industry and the market are demanding for more diversified products in smaller and smaller lot-sizes (Hameri & Lehtonen 2001). This trend has been especially clear when looking at Western Europe and Sweden. It has become more important to develop a broader product range with products that adds a higher value in the production process (Sörensson & Jonsson 2014). Recently, a dramatic change has occurred in the paper industry; which is the sharp decline of the demand and profitability of graphic paper that have happened in recent years. This has led to that previous producers of this type of products, fine paper and newsprint; have changed their production to different board products, which have a better market outlook (Sörensson & Jonsson 2014). Sweden is the third largest exporter of forest products, and the pulp and paper industry is extremely important for Sweden from several aspects. It gives a strong contribution to Sweden’s current account and is important for the employment both directly and indirectly, especially in sparsely populated areas. It will also be important for Sweden's ability to achieve the national and global environmental objectives.

There has also been a decline in the profitability due to increased competition from emerging countries with low wages and raw material costs, which also highlighted the importance of having a high value adding production (Sörensson & Jonsson 2014).

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Traditionally, the production management strategy in paper manufacturing is based on a volume-intensive approach. This is characterized by measuring the overall performance or productivity, in terms of high level of capacity utilization and minimum waste levels. This approach is well suited for producing high volumes with a limited product range. However, changes in demand have led to a new situation, where this volume-based approach is no longer suitable for producers that meeting an increase in their product variety. Today, paper industry products have become more tailored to customer-specified dimensions and quantities, which has broaden the product range. Therefore production needs to be controlled by a flexible approach, that’s considering the inventory performance (Hameri & Lehtonen 2001). This would have been no problem if the production technology of paper products would be geared to short production series and thousands of tailor-made products. This is not the case, and modern paper production technology have been built according to different principles, where large and fast paper machines is used that is best suited for producing standard products in large volumes, that results in large production sequences (Björk & Carlsson 2007). In most paper mills the bottleneck is often the paper machine. Therefore it is important with efficient production planning that optimizes the resource utilization and minimizes setup and inventory costs (Bouchriha et al. 2007). In traditional process manufacturing industries, like the paper industry, production planning is particularly important, due to very high cost of raw material, energy consumption and product line maintenance (Feng et al. 2011). According to previous studies in the paper industry and the process industries (Feng et al. 2011; Björk & Carlsson 2007; Bouchriha et al. 2007), there is a challenge with production planning for firms in this field, the cause for this are the combination of high capital costs and a wide product range (Figueira et al. 2013), high capital costs are obtained from high investment costs, capital-intensive production and costly setup times. Due to this the production process are running 24/7 and often in long production series (Björk & Carlsson 2007), which is repeated over time in a production cycle (Bouchriha et al. 2007). Usually, the production cycle is, optimized according to a grade-change schedule in consideration of base weight (g/m2) and pulp type, which promote simple changeovers with reduced waste and set up time. Normally a production cycle is between three and five weeks (Hameri & Lehtonen 2001). The major disadvantage of this production strategy is the lack of production flexibility, which results in a higher level of finished inventories. Longer cycle times will also lead to higher amount of material among the supply chain. A shorter cycle

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length brings greater production flexibility, which in turn contributes to a more order oriented production (Hameri & Lehtonen 2001).

1.2. The Case

The case company is a producer of deep-color tissue. As in the rest of the paper industry the production is done in long production series that is repeated in a cycle (Björk & Carlsson 2007; Bouchriha et al. 2007). The cycle is optimized according to simple changeovers. On their paper machines they produce during 2014 about 20 different grades (different base weight and different pulps used), 50 different colors and in total 200 different articles (grade+ color). During one production cycle 25 different colors are produced, and the length of the cycle is about one month. Within a specific color there are different grades, which differ in either or both, base weight and pulp type. A changeover between two different colors takes about 25 minutes. In this specific case the color is the large factor that makes it important to produce in a production cycle, as changing color has a longer changeover time than grade changes. The greater the differences are in color, the longer will the changeover time be. In broad terms, the production cycle starts with lighter colors and ends with darker colors.

A paper machine is producing paper in a continuous process, where one product is produced at the time. Changing the product (type of paper, paper grade or color) will lead to a transition period or grade change time, during which paper of poor quality is produced and this is considered waste. The amount of paper that is produced during this change depends on the technical characteristics of the machine and the sequence of products (Correia et al. 2012). The changeover time depends on how different the two products are, larger differences in base weight, pulp types and color will result in increased changeover time and by that increased waste production during the changeover. All paper producers have this kind of problem in a varying degree depending on how many different products they produce and how much the products vary in base weight, pulp types etc. The waste material that is produced during a changeover can in most cases be recovered and used as raw material again, but it is still a waste of machine capacity in making this waste material (Correia et al. 2012).

The case company uses one machine that produces colored-tissue in a production cycle that is set up to minimize the time it take to change from one color to the next. The cycle length is about one month and this means that a

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specific color can only be produced at a specific time, as all the colors have a given place in the cycle where they can be produced. In this thesis we will investigate the tissue machine producing colored-tissue in a production cycle.

1.3. Problem analysis

There is a challenge for paper producers to coordinate their manufacturing between a wide product range and costly sequence-dependent changeovers. Therefore, the production is often planned through a production cycle, with considerations of simple changeovers as possible (Hameri & Lehtonen 2001; Björk & Carlsson 2007). Thus, the changeovers in the paper industry are sequent dependent, and by that need to be coordinate in a schedule that’s reduce losses between changeovers to achieve an effective utilization. Manufacturing on a paper machine occurs in a continuous process, where it is a need to produce at a constant rate to achieve efficiency in the plant (Correia et al. 2012). The technical constraints on the paper machine has an embedded inflexibility, for example a changeover between two grades, where the differences are too large in base weight or/and used pulp, the longer changeover time is needed, and due to the continuous process, longer changeover time will increase the waste production during the changeover. Thus, it is important to minimize changeover time, both from a cost- and a sustainable perspective. There are also an inflexibility in the production cycle itself, due to one specific product only could be produced once during the cycle time. The changeover time is mainly depending on the paper machines technical performance or the system’s ability to change (Correia et al. 2012). Scanners and flow meters are equipment that has a subnational role in changeovers, a scanner measures different quality parameters and can adjust the parameters to approved quality, through control of flow meters. The advantages with the volume-based strategy are reduced waste between changeovers and high machine utilization (Hameri & Lehtonen 2001), but there are also some disadvantages, due to the possibility of only being able to produce a specific grade at a specific time in the fixed production cycle, leading to production planning difficulties to manufacture the right quantity at the right time, which in turn leads to high level of finished goods inventory (FGI), and difficulties to react and meet changes in demand. Therefore, paper producers with a wide product range need to find a balance in terms of high machine utilization and ability to react on changes in demand. A better coordination of the production cycle, considering FGI costs, changeover costs,

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demand and variability of the products, can lead to improved inventory performance in terms of lower level of finished inventory goods and better flexibility to react on customer demand.

1.4. Aim

The aim of this thesis is to investigate how paper producers can coordinate their production cycle, to manage a wide product range and sequence-dependent changeovers on a paper machine, to achieve flexibility and improve the scheduling, at the plant level.

1.5. Research questions

RQ 1: How can paper producers coordinate the scheduling of the production cycle in an efficient manner, in order to improve the flexibility?

RQ 2: How will an improved flexible production cycle occur compared to the current situation, for the case company?

RQ 3: What will be the effects of the suggested production cycle, in terms of cost and flexibility for the studied company’s perspective?

1.6. Delimitations

This study will investigate how paper producers can coordinate a wide product range and sequent-dependent changeovers by studying one company. Focus will be on the manufacturing on one paper machine, and the afterward production such as, rollers, converting etc. will not be considered. Considerations will only be taken for the finished goods inventory and not to the raw material inventory or the work in progress inventory. The study will be on the plant level, and not on how flexibility can be achieved with closer and better cooperation within the supply chain.

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2. Theoretical framework

This section contains of the study’s theoretical framework, presenting relevant theories and concepts for this study.

2.1. Introduction of mix flexibility

In the literature, flexibility has been described as a useful and important tool to achieve competitive advantages in the marketplace (Jain et al. 2013). There have been a lot of research over the years, on why companies need manufacturing flexibility; the reasons can be divided into four main categories: products, manufacturing processes, competitors and customer (Wilson & Platts 2009). Commonly reasons are: increased product variety (Vokurka & O'Leary-Kelly 2000), increased product customization (Aggarwal 1997), fast-changing consumer tastes and low-cost competition (Dreyer & Grønhaug 2004).

In manufacturing industries, mix flexibility is the manufacturing type that allows firms to produce their product range. Mix flexibility can be referred to a firm’s ability to change or react to change, related to the product range (Wilson & Ali 2014). In many cases, products need to be manufactured on shared resources, which require coordination mechanisms for an effective utilization (Wilson & Ali 2014). There is no generally agreed term for mix flexibility or accepted definition (Wilson & Platts 2009). Bateman et al. (1999) define mix flexibility as the ability to change between the current ranges of products that are manufactured by a company, which we use as the definition in this thesis.

Because of different definitions and terms, it has been difficult to study mix flexibility (Wilson & Platts 2009). Nevertheless, Wilson and Platts (2010) means that there are four fundamental issues about mix flexibility that those researchers have agreed on. The first issue is that mix flexibility represents the system level flexibility, which means that mix flexibility is affected of resource levels or basic level flexibility types. Mix flexibility is therefore related to other flexibility types, such as machine flexibility, where higher machine flexibility will contribute to a higher level of mix flexibility. The second issue is that mix flexibility could be divided into two dimensions, range and response (Wilson & Platts 2010). The dimension of range can in turn be described in range-number and range-heterogeneity, were range-number is referred to the number of options (tasks, operations and products) and range-heterogeneity is the

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heterogeneity of options (differences between tasks, operations and products) within a manufacturing system (Koste & Malhotra 1999).

The response dimension is linked to the cost and time it takes to change between different products (Slack 2005). The third issue, when study mix flexibility it is important to identify what is investigated and analyzed, since mix flexibility could be achieved in different ways, among different machines and plants. The fourth issue, mix flexibility is viewed to gain different advantages, for example lowering inventory costs and batch sizes (Parker & Wirth 1999).

Wilson and Platts (2009) argue for three main factors that have an impact on the mix flexibility: uncertainty, product variety and resource configuration. Uncertainty and product variety has been well studied (Wilson & Platts 2009), and usually these variables are used to explain how much mix flexibility that is needed at the plant level (Wilson & Platts 2009). Wilson and Platts (2010) identified a research gap related to the manufacturing flexibility type mix flexibility. The gap is of how mix flexibility is achieved during day-to-day operations. In their research, they found that mix flexibility on an operational level is achieved through the use of coordination mechanisms to manage dependencies in the manufacturing, which is related to mix flexibility requirements on system and resource levels.

2.2. Uncertainty

Uncertainty is often viewed from two environmental perspectives, external and internal, which both affect the organization. There are several factors that influence external uncertainty and internal uncertainty (D'Souza 2002). Example of some external factors that contribute to uncertainties are resource and supplier-based, manufacturing technology-based, product-based, competitors and demand-based. According to D’Souza (2002) there are three main factors that influence the internal uncertainty, which are machine breakdowns, variations in processing time and quality problems (i.e. problems regarding quality in delivery, product and process).

2.3. Product variety

MacDuffie et al. (1996) identified two dimensions of product variety: fundamental variety and peripheral variety. The first one is referred to intrinsic

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different models, which make up the product mix, while peripheral variety refers to different options with the same core design of the product. Kotler and Keller (2012) classified four dimensions of product variety: width, length, depth and consistency. Width is referred to the amount of product lines, length to the number of items in the product mix, whilst depth is referred to the variants of each product and consistency as the relations between products. The broad view of product variety in MacDuffie et al. (1996) can be seen to include Kotler and Keller (2012) more detailed view, were the fundamental variety include product lines (width) and items in the mix (length), and the peripheral variety includes product variants (depth) and relationship of products (consistency) (Al-Zu’bi et al. 2007).

2.4. Resource configuration

Wilson and Platts (2010) investigate the role that resources play in manufacturing flexibility achievement on the operational level. Dependencies and coordination mechanisms were investigated in flour filling mills, by using the coordination theory. An identification of three dependencies from the studied case companies was made. The three dependencies types were shared resource, flow dependency and simultaneity constraints. Shared resource can be described as involving organizational resource sharing (Wilson & Platts 2010). Flow dependency is the relationship between two activities, where the output from one activity is the input in another activity. Simultaneity constraint can be described as when activities have to occur simultaneously or when activities cannot occur at the same time (Frayret et al. 2004). The dependency types can be coordinate by different mechanisms. A number of coordination mechanisms were identified by Wilson and Platts (2010) in the flour filling industry, the coordination mechanisms related to the dependency type shared resources were direct communication and notification, rules-of-the-game and market-based/bidding. All of these three coordination mechanisms can be used to manage shared resources. The coordination mechanism rules-of-the-game includes minimization, sequencing and changeover routine. Wilson and Ali (2014) argue for a similarity between the sequencing mechanism and the product wheel concept. For the seconded dependency type flow dependency, the coordination mechanisms were coordination by plan, notification and inventory management. The last dependency type, simultaneity constraints, consists of the coordination mechanisms synchronization and scheduling (Wilson & Platts

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2010). Findings show that sequencing and minimization where generally applicable from the coordination mechanism rules of the game, while the other seems to be more specific.

2.5. Mix flexibility in the process industries

There have been a few studies on manufacturing flexibility in the process industries, with different focus. In this section we briefly present why and what have been studied by researchers in the field of manufacturing flexibility in the process industries. Berry and Cooper (1999) studied the impact of product variety on the performance in process industries, from a manufacturing flexibility perspective. Product variety is often related to a competitive advantage in the market, were different products or services can be tailored to specific market segments. This strategy is supposed to lead to an increase in total sales volume and/or higher prices, and by that increase the profit by meeting more specialized demands. In order to success in such a strategy with high product variety, there is of high importance to aligned market strategy and manufacturing strategy. Berry and Cooper (1999) shows that adding product variety can have a negative impact of cost and margins when market and manufacturing strategies are miss-aligned. Therefore they argue for the importance of a clear understanding of the process choice to support the planned range of product volumes, to achieve competitive advantages with increased product variety.

Wilson and Ali (2014) studied mix flexibility by the use of King’s (2009) product wheel concept for the process industries. The product wheel is a modification for the process industries, and is based on the theory of lean manufacturing. The product wheel concept is developed from the lean tool heijunka, which is used for smoothing production. Wilson and Ali (2014) used this concept for production scheduling, when they studied how mix flexibility could be achieved on a packaging line, were the products need to share the same resource. Findings from the research show that the product wheel concept could be used to group together similar products in a production schedule, were reduction of time-intensive changeovers could be reduced without loss of product availability to satisfy customer demand. The product wheel concept is a heuristic method and requires knowledge and judgment of the studied manufacturing process.

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Findings from Wilson and Ali (2014) shows that the product wheel concept is applicable in the process industry, specifically regarding packaging of chemical bleach. The research also support the use of coordination mechanism argued by Wilson and Platts (2010), in order to manage a shared resource and to realize the achievement of mix flexibility. Another link between the product wheel concept and achievement of mix flexibility that is concluded in Wilson and Ali (2014) is the product variety, uncertainty and resource configuration, which is argued by Wilson an Platts (2010) having an impact on a firms operational mix flexibility. Wilson and Ali (2014) means that this is taken into account in the use of the product wheel, where uncertainty and product variety are manage in the determination of product demand and demand variability, in the classification of MTO- and MTS products. Resource configuration is considered in the determination of the production sequencing, when the production cycle is design in a workable sequence on a shared resource to achieve a leveled production without loss of product availability (Wilson & Ali 2014).

2.6. Lean in the process industries

Lean is a strategy for achieving continuous improvements through the elimination of waste, both waste of resources and time. The basic idea of lean is to minimize the use of resources that do not add any value to a product. In this case value is equal to added values activities that’s the customer is willing to pay for (Abdulmalek et al. 2006).

Lean can be described in terms of its clear aims, guiding principles and tools. The basic aims for lean as a production system are cost reduction, quality improvement and faster delivery. To reach this, lean focus on guiding principles as employee empowerment, utilize less to create more and elimination of non-value added activities (Abdulmalek et al. 2006). Lean manufacturing takes in all aspects of the value stream, and by that eliminate waste to achieve a lower manufacturing cost, free up capital, generate more sales and strengthen the position in a competitive global market. To achieve a resource-efficient production and eliminate waste there are a number of different lean tools that helps companies to guide and correcting elimination activities in their production system (Abdulmalek et al. 2006).

There has been a slow adoption of lean in the process industries compared to the discrete manufacturing. In the literature there have been a great focus on

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lean adoption in the discrete manufacturing, while the process industries has been argued for not fitting the lean thinking.

The discrete manufacturing has widely been adapted different lean tools and techniques with success, to achieve waste improvements of resources and time (Abdulmalek et al. 2006). According to Abdulmalek and Rajgopal (2007), the difficulty for process industries to implement the lean concept is the characteristics and nature of process industries, with inflexible machines, long setup times and the difficulty in manufacturing small batches. Therefore, some of the lean tools and techniques are difficult to use in the process industries. However, Abdulmalek et al. (2006), Abdulmalek and Rajgopal (2007), King (2009) have studied the adoption of lean tools and techniques in the process industries, finding that a number of lean tools with some modifications can be used to achieve waste elimination.

In Abdualmalek et al. (2006) they built up a classification scheme for the process industries, and address the applicability of implementation of different lean tools. For the discrete manufacturing, different lean tools have been used and adapted in a successful way. Discrete materials are materials that preserve its solid form, and non-discrete materials are a material that’s need to be stored in containers or packaged because of their non-solid form, these materials are common in the process industries. Typical non- discrete materials are liquids, pulp and gases. The process industry is often seen as a homogeneous industry, but in fact there are differences that are needs to be consider in order to classifying the applicability of lean tools. The applicability of lean tools in the process industry is determined through process characteristics, product characteristics and when the product becomes discrete in the process.

Process characteristics are linked to the flow of materials within the plant environment. Generally the material flow system in the process industry is of a flow shop type, where the materials are transported in a continuous flow through different automated equipment’s. In actuality the manufactures within the process industry have their own material flow system based on their equipment layout and flexibility, thereby some process industries could have a combination of different systems as job shop, batch shop and flow shop. The equipment can then be divided into general- or specific purpose, which in turn for these two can be divided in dedicated and non-dedicated. For example, general-purpose and non- dedicated equipment provides the highest flexibility, while dedicated specialized equipment affords the least flexibility. The equipment type and plant layout are the main factors that affects the flexibility

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in the manufacturing process. This also determines which lean tools that are applicable (Abdulmalek et al. 2006).

The product characteristics are based on its material variety and product volume. Depending on high or low variety in material use and product volume, some lean tools are more adaptable than others. Within the process industries the material variety and product volume differs between industries. In the pulp and paper industry, both product volume and material variety are fairly high.

During the manufacturing process the products at some point goes from non-discrete to discrete. When this point occurs in the manufacturing process will affect which lean tools that may be adaptable (Abdulmalek et al. 2006).

Abdulmalek and Rajgopal (2007) investigate the opportunities to adopt various lean techniques in the process industries through value stream mapping and Abdulmalek et al (2006) classified the applicability of various lean tools in the process industries. Table 1 present and describe the applicability from the two studies.

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Table 1: Various lean tools and their applicability in the process industry (Abdulmalek & Rajgopal 2007; Abdulmalek et al. 2006) Lean tools: Short description: Applicability: Cellular manufacturing The manufacturing

occur in a cell or a line, were a family of parts is produced.

Inapplicable

Setup reduction Reduction of the setup time.

Partially applicable

5S A waste elimination process, which comprises of sweeping, systematization, standardization, sorting, straightening and cleaning

Universally applicable

Value stream mapping An identification technique of value added and non-value added processes.


Just-in-time Reduction of waste through producing and ordering the needed amount, when it is needed.


Production leveling A process to smoothing the production level, in order to keep the production at a constant rate as possible.


Total productive maintenance

A tool for preventive maintenance and corrective maintenance.


Visual systems A technique, which provide visual hints for the production process.


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2.7. The product wheel concept

The product wheel concept is a technique for production scheduling, developed and modified from the Lean tool heijunka, to fit the process industry (King 2009). The concept is a ten-step approach, which is described below.

1. Decide which assets would benefit from product wheel concept.

2. Analyze product demand variability (consider MTO and MTS). 3. Determine the optimum production sequence. 4. Calculate the shortest wheel time based on time available for changeovers. 5. Estimate the economical optimum wheel time based on EOQ model.

6. Determine the basic wheel time; determine which products get made on every

cycle and the frequency for others; balance low-volume products assigned to each product wheel cycle; provide empty spokes for MTO products.

7. Calculate inventory levels to support the wheel (cycle stock and safety stock)

8. Repeat steps 3 through 7 to fine-tune the design.

9. Reverse all scheduling processes, as appropriate.

10. Create a visual display (heijunka) to manage the leveled production.

Figure 1 shows the product wheel concept in a detailed manner. The wheel showed in figure 1 consists of ten products, numbered from one to ten. Each portion of the products in the cycle is called a spoke, and this cycle consists of ten spokes. The spoke length indicates takt value for every product, and the empty spaces between the spokes indicate changeovers.

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Figure 1: Visualization of the product wheel concept (Adapted from King (2009)

Step 1: Decide which assets would benefit from the product wheel concept

The product wheel concept is suitable for process systems were changeover losses and time is dependent on the scheduling order. Thus, the product wheel grouping similar products together and help to set the optimum quantity lengths for all products. If any process step or equipment part is sequent dependent, it will be an advantageous candidate for the product wheel concept, due to the sequent determining part of the concept (King 2009).

Step 2: Analyze product demand variability

In this step, average demand and demand variability for each product will be examined. In order to decide which products are best made to order (MTO) or made to stock (MTS).

For analysis of product demand a Pareto chart can be used to illustrate the average demand for products made on the wheel. Based on the Pareto chart, products can be classified after demand volume. To analyze the demand variability, the volume of products is analyzed for a period of time. Calculation of the coefficient of variation (CV) is determined through formula 1, which describes the variation for a product relative to average demand. Where σD = Standard variation of demand, DAVG= average demand (King 2009).

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𝐶𝑉 =𝜎𝐷

𝐷𝐴𝑉𝐺 (1)

When products are analyzed by product demand and product variability, a decision matrix (figure 2) can help to guide decisions on MTO versus MTS. The decision matrix is constructed of product demand and product variability, products with low demand and high variability can be seen as MTO products, and products with high demand and low variability can be seen as MTS, because of the low risk of carrying cost since the products are soon to be sold. If the MTO products with low demand and high demand variability were manage as MTS, it would increase the inventory levels. To consider products as MTO requires a total manufacturing lead-time, i.e. from order to delivery at the customer, to be shorter than the agreed customer delivery time. In those cases, when the manufacturing lead-time is shorter than the customer delivery lead-time MTO is an appropriate alternative. If this is not the case and MTO seems as beneficial, work should be done in order to reduce the lead-time and realize MTO in practice (King 2009).

Figure 2: Decision matrix for MTO and MTS products (King 2009).

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Step 3: Determine the optimum sequence

In this step of the wheel concept a determination of the optimum sequence will be made, in which all the products should be made. In order to determine the optimum sequence, parameters that affect the changeovers need to be considered. Therefore, it is important to understand the studied object, to determine the optimum sequence. For effective sequencing products should be grouped together by their similarities, for example raw material types. In some cases, could it be difficult to find the optimum sequence, because of the complexity of the parameters affecting changeovers (King 2009).

Step 4: Calculate shortest wheel time possible (Available time model)

When the sequence of products has been established, the time of how long a cycle should be needs to be decided. Two methods can help to guide the decision on optimizing wheel time:

1. Available time model

2. Economic order quantity (EOQ)

Different answers will be given from the two methods, and there is no best answer. Each of the methods gives useful perspectives, which need to be considering when the wheel time should be determined. The available time model consider the perspective of some periods of time, it could be one week, one month etc. The model calculates the amount of time required to produce all the demanded products for the chosen period, and subtracts it from the total available time. Then the total amount of time for changeovers is determined through the difference between total available time and total production time. Changeover times per cycle is computed by adding all changeovers together, i.e. all changeovers between the products, the sum is then divided by the total time available for changeovers to indicate how many cycles that could be performed during the time period. This is obtained from formula (3). The shortest possible wheel time is addressed by formula (4), which computes the wheel time by dividing total available time with number of wheel cycles per period (King 2009).

𝑊ℎ𝑒𝑒𝑙 𝑐𝑦𝑐𝑙𝑒𝑠 𝑝𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑 =𝑇𝑜𝑡𝑎𝑙 𝑎𝑣𝑎𝑖𝑙𝑖𝑏𝑙𝑒 𝑡𝑖𝑚𝑒 − 𝑇𝑜𝑡𝑎𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒

∑ 𝐶ℎ𝑎𝑛𝑔𝑒𝑜𝑣𝑒𝑟 𝑡𝑖𝑚𝑒𝑠 𝑝𝑒𝑟 𝑐𝑦𝑐𝑙𝑒 (3)

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𝑊ℎ𝑒𝑒𝑙 𝑡𝑖𝑚𝑒 =𝑇𝑜𝑡𝑎𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑡𝑖𝑚𝑒

# 𝑜𝑓 𝑊ℎ𝑒𝑒𝑙 𝑐𝑦𝑐𝑙𝑒𝑠 𝑝𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑 (4)

Step 5: Estimate Economic optimum wheel time (EOQ model)

The EOQ model takes another perspective into the decision of wheel time, the economics. It approximates the balance between inventory carrying cost and changeover cost. Larger runs with the same products require higher levels of inventory to supply customers or operations downstream. On the other hand, small lot sizes require more changeovers (King 2009).

𝐸𝑂𝑄 = √2 ∗ 𝐶𝑂𝐶 ∗ 𝐷

𝑉 ∗ 𝑟 (1 − 𝐷𝑃𝑅)

(5)

𝐸𝑂𝑄 = √2 ∗ 𝐶𝑂𝐶 ∗ 𝐷𝑉 ∗ 𝑟

(6)

Equation (5) is the specific equation of the EOQ-model gives the lowest total cost. Where COC= changeover cost, D= demand per time period, V= unit cost of the material, r= % carrying cost of inventory per time period, PR= production rate in units per time period. Equation (6) is a simpler variant of equation (8). Equation (8) take account of, that produced material is being consumed during the production, so inventory levels will be less than the sum of the amount produced and starting inventory (King 2009).

The inventory cost increase with a longer wheel time, and the changeover cost decrease with longer wheel time, due to the decreasing number of changeovers. The total cost will first drop and then increase, the minimum is obtained where the cost components of changeover cost and inventory cost intersect. EOQ is an approximation, and highlights the most significant factors. Account to safety stock and inventory, to react on normal random variation in demand is not considered in the model. The EOQ curve is flat in the region of the lowest total cost, which can be observed in figure 3. This means that the wheel time in the region of the optimal will be almost as good. Since EOQ is an approximation,

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a wheel length that is lower or higher could be more beneficial, considerations of what is practical influence the decision (King 2009).

Figure 3: The EOQ concept.

Step 6: Determine the wheel time

Based on the results from both the available time model and the EOQ model, a choice for the wheel time needs to be determined. From a lean thinking perspective, a choice with small lot sizes is advantageously. The demand variability, determined in step 2, affect the determination of wheel time in how often some products need to be produced on the cycle. High volume products usually form the basis of the cycle, while low volume products may be produced every seconded cycle, every third cycle depending on the demand variability (King 2009).

Step 7: Calculate inventory requirements

For products that have been classified as MTO, no inventory is needed to support the wheel. Received MTO products are scheduled onto the wheel, and after the products are manufactured they flow through the downstream steps to packaging and transport. There may be a temporarily stop in a buffer inventory, caused by reasons other than the wheel scheduling. MTS products require

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inventory that is proportional to the cycle time. The MTS products requires downstream inventory as work in progress (WIP) and finished inventory, to support material needs and customer demand during production of other products. In order to provide the scheduled wheel cycle with material, a cycle stock is needed. Because of normal fluctuations in demand, cycle stock is not sufficient to avoid stock outs during periods of high demand. Usually a safety stock is carried to manage this problem. A shorter wheel time requires a less cycle stock, while a longer wheel time requires a larger cycle stock. The safety stock is also depended on the wheel time if the variables follow normal distribution, than a shorter wheel result in a need for a lower safety stock. Calculations of cycle stock are showed in formula 7, were average demand per unit time is multiplied with cycle frequency. For products produced every fourth cycle, the cycle stock needs to hold inventory over four cycles (King 2009).

𝐶𝑦𝑐𝑙𝑒 𝑠𝑡𝑜𝑐𝑘 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑒𝑚𝑎𝑛𝑑 𝑝𝑒𝑟 𝑢𝑛𝑖𝑡 𝑡𝑖𝑚𝑒 ∗ 𝐶𝑦𝑐𝑙𝑒 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 (7)

Step 8: Fine-tune the design

The product wheel is now complete. However, the wheel may need to be adjusted to fit the reality of the process. So a recycle process through the steps are needed to find a feasible design, both the EOQ model and available-time model are approximations that may need to be adjusted. The EOQ curve is also flat in the region of the lowest total cost, which allows adjusting the wheel design. Things that may need to be considered to fit the real process are available inventory space; if it is possible to support a short wheel time, manage an increase of changeovers versus increased production losses (King 2009).

Step 9: Revise the current scheduling process

The current scheduling process should be examined and modified to accommodate the product wheel scheduling. It is beneficial to incorporate the production planners in the development of the product wheel design, due to their knowledge and perspectives of scheduling, which can be valuable.

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Step 10: Create a visual display

The visual display should show the next cycle of the product wheel, and in detail show what products should be made, in which order and in what quantity.

Advantages and disadvantages of the product wheel

King (2009), s. 227) listed nine advantages of the product wheel concept:

x Tend to level production at natural behavior. x Optimize production sequence x Add structure and predictability to high-variety operations. x Provide a basis for informed decisions about MTO and FTO for appropriate products. x Optimize transition cost versus inventory carrying cost. x Provide a structured basis for determining cycle stock requirements x Provide a structure basis for calculating safety stock requirements. x Quantify the benefits available for further SMED activity.

Wilson and Ali (2014) conclude some advantages with the product wheel concept including categorization of similar products to reduce time consuming changeovers and manufacturing of small lot sizes. On the other hand, some disadvantages were obtained including increased changeover time compared to the actual production. In a comparison between the product wheel and the actual production, product wheel suggested a higher runtime for MTO products than the actual demand, which shows that the prescribed design not always give the best results for a time period.

Both King (2009) and Wilson and Ali (2014), outline that the concept need modifications to find the best design related to the real operations. King (2009) describes in step 8 that the wheel need iterations to find a feasible design, and Wilson and Ali (2014) conclude that the product wheel concept is not an optimization tool, but a heuristic technique, where judgment and experience is important factors to find an appropriate design.

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Practical implementation of the product wheel

The product wheel concept is a simple but comprehensive model for application in manufacturing plants. Wilson and Ali (2014) means that the ten-step approach by King (2009) explains the “how” of the product wheel, but lack in the explanation of three other key factors; “who” should develop the wheel, “what” tool should be used in the development of the wheel and “when” should development and redesigning of the wheel be made.

Suggestions from Wilson and Ali (2014) considering the “who” in a team-based approach, with representatives from both the manufacturing and marketing, in order to capture insights of both manufacturing details such as production times, changeover times and marketing details as product demand and demand variability. On the “what”, Microsoft Excel is suggested, which fulfills the requirements of mathematical and networking functions that is needed in the development of the product wheel. The “how” is depending on the need for information updates from manufacturing and marketing functions, which is specific for each individual company. The frequency should be in a sufficient way, to feed the process.

2.8. Cyclic production

In cyclic production are all the products disposed in a cyclical production pattern, which is repeated over time. Items with high volume value or items that of some reasons have a high turnover rate are often produced more frequently in the production cycle than items with lower turnover rate. A difference between cyclic production and other material planning methods are that cyclic production is regarding the available capacity. Which occur when the cyclic production pattern is established (Mattsson & Jonsson 2003).

The information that is needed to generate the cyclic production pattern is capacity and the optimal manufacturing frequency. To determine the manufacturing frequency for every item and planning period, information of ordering costs, carrying cost and forecasted demand is needed (instead of forecasted demand, either consumption statistics or gross requirements calculation of materials could be used). A usual approach according to Mattsson and Jonsson (2003) is described below in a six-step approach.

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1. Compute annual demand (D) for each item, which giving the total capacity

utilization for every production group. This may need to be adjusted, unless the annual capacity is met.

2. Compute order quantity (q) for every item through the Economic order quantity model.

3. Compute the manufacturing frequency, in series per year – D/q 4. The planning period for cyclic production should be chosen based on a series

where the length is equal to production time of the calendar year, divided with either 1,2,4,8 etc. (i.e. from the geometric series 2n). Chose the closest manufacturing frequency for every item from the geometric series 2n series/year.

5. Set up some alternative production schedules with cyclic production and

alternative manufacturing sequences. The planning period needs to cover both setup time and operation time. The production schedules should be revised through comparison with the total costs. Control that throughput time is smoothing variation in demand, if don’t the quantities should be reduced.

6. After the evaluation of different production schedules, determination of suitable

manufacturing frequency and planning periods could be made.

There are two main variants of cyclic production. The first one contains of a fixed sequence of the items in the production patterns, where the items have a fixed place in the cycle within every planning period. There is no time schedule when the different items should be produced; instead the due dates are admit to change. The planning period for a cycle could for example be one month. Thus, the cycle is recurrent every month, but the items in the cycle can be varied. Crucial if an item is recurring or not for different planning periods is, if there is need and/or capacity. The second variant implies that the cycle production pattern is fixed in time, and every item in the recurrent cyclic period is stated with time, when the item should be produced and when it should be delivered. Some items with high turnover rate, is produced in every cyclic period, while some other items with low turnover rate may be produced every second, fourth or eight period etc. (Mattsson & Jonsson 2003).

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2.9. Production planning and scheduling

Production planning and scheduling problems are critical to the efficiency of manufacturing systems (Kang & Choi 2010). The goal with production planning is to determine which activities to perform so that production can be done in time and as cost effective as possible (Kang & Choi 2010; Guimarães et al. 2014). There are some challenges when doing research in production planning. First, planning and scheduling problems are generally belonging to a class of NP-hard problems, to which the optimal solutions usually difficult to find in an efficient way (Kang & Choi 2010; Bouchriha et al. 2007). These kinds of problems are often studied as combinatorial optimization problems. There have been various exact or approximate optimization algorithms that have been developed to calculate optimal or near optimal solutions (Kang & Choi 2010; Bouchriha et al. 2007; Guimarães et al. 2014). Second, manufacturing systems is complex and as a result real-world production planning and scheduling problems often involves many constraints, preferences and multiple conflicting optimization objectives (Kang & Choi 2010; Guimarães et al. 2014; Bouchriha et al. 2007). Even with this complex reality most optimization algorithms tends to focus on taking on the NP-hard property with idealized problems that often overlook these complex practical factors (Kang & Choi 2010). Because of this most of these algorithms cannot usually be directly applied to real-world problems, as they are difficult to model in a rigorous mathematical approach. Third, most manufacturing systems are dynamic and in these systems planning and scheduling are ongoing processes that needs to respond to unexpected and evolving circumstances (Kang & Choi 2010). In daily operations of manufacturing systems there is no real planning and scheduling problems, more like re-planning and rescheduling ones. So in the practical production planning and scheduling the challenge is how to respond fast to changes and still maintain a consistent schedule and avoid ‘nervousness’ that comes by too frequent and drastic changes (Kang & Choi 2010). The traditionally objective of production management have been to minimize the total cost, it have also been seen that some recent studies indicate that production managers also wish to deal with smooth series of production volumes over the planning periods (Karimi-Nasab & Ghomi 2012).

For plants to be able to become more efficient in their production (i.e. reduce production costs, increase profitability and provide a high level of service) improvements to the production planning method is essential (Feng et al. 2011). In order to cope with demand requirements, flexibility and high productivity

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needs to be combined (Figueira et al. 2013). Firms within the process industry cannot plan their production through received orders, instead product quantity, product quality and when the product should be produced needs to be planned in advance, to satisfy and meet future demands. Most common approach for production planning within the process industries is a make-to-stock (MTS) process based on forecasting and/or total demand for make-to-order (MTO) orders (Feng et al. 2011).

2.10. Make-to-Order / Make-to-Stock

Most production system is either characterizes as make-to-order (MTO) or make-to-stock (MTS) (Soman et al. 2004). A MTO system offers a high variety of customer specific products and they are typically more expensive. In a MTO system, the production planning is focused on order execution and the performance measures are order focused, like average response time and average order delay. The way to stay competitive is to prioritize shorter delivery lead-times. The operational issues that you face in MTO systems are capacity planning, order acceptance/rejection and attaining a high due-date adherence (Soman et al. 2004). A MTS system offers a low variety of producer specified products and they are typically less expensive. In a MTS system the focus is on anticipating the demand (forecasting) and planning how to meet this demand. To stay competitive it is important to have a high fill rate, be able to meet as much of the demand as possible. The main operational issues in a MTS are inventory planning, lot size determination and demand forecasting. The performance measures are product focused like line item fill rate and average inventory levels (Soman et al. 2004).

The combined MTO-MTS problem have not been studied to the same extent as the pure cases of MTO and MTS, that have been as extensively studied, and only a handful papers has been found that explicitly deals with the combined problem (Soman et al. 2004). There will be different managerial action that will be required in a mixed system then in in the pure systems. The main production-planning problem in a mixed MTO-MTS system is what products will be made to stock and what products will be made to order. The decision between MTO and MTS is strategically oriented and it is complicated as there are a lot of different factors involved. When deciding this, the tradeoffs between product-process characteristics and the demand from the market need to be considered. Another main decision is to find a suitable production and inventory policy. The

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problem here is how to handle uncertainties by deciding suitable due dates and/or by finding suitable levels of stocks. There is also the decisions capacity allocation, order acceptance lot sizes and inventory policy and last there is also the operational scheduling and control decision, which tackles issues like production sequencing (Soman et al. 2004).

2.11. Inventory

2.11.1. Tied up capital

The material that is held in the finished inventory ties up capital. The cost of capital that is tied up in the finished inventory consists primarily of capital cost, i.e. financing of purchasing of raw materials and other intermediate goods. Add to this cost of inventory area, material handling equipment, employees, insurance of inventory goods and obsolescence etc. are costs that affect the tied capital. When the products are held in the finished inventory they have reached their maximum capital tie, due to the production processing, the capital tie increase during the production lead time when more resources have been used and by that increase the cost (Olhager 2013).

2.11.2. Inventory carrying cost

The inventory carrying cost are mainly comprises of tied capital, but also allocated costs for cassations, obsolescence, insurance, material handling equipment and storage charge. Generally, inventory costs are assumed to be linear dependence on the number of articles that are stored, and the determination of inventory costs is given by inventory carrying charge multiplied with article value (Olhager 2013). In the existing literature carrying cost varies between 12-50 % above the inventory value. This have made it so that carrying cost are often not precisely know and usually its approximated by managers, according to different rules of thumb depending on which type of industry they work in (Azzi et al. 2014).

Stored goods imply locking of resources, therefore goods that are kept in inventory ties capital. Tied capital implies potential earnings, because the free capital could be used for activities that increase the earnings instead of being tied in goods. The loss tied capital is usually calculated to be the capital of cost (Aronsson et al. 2003).

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Besides capital of cost, inventory entails different kind of risks. During inventory, goods could be exposed of damage, through fire, water or in the handling of the goods. The risks depends largely on what products is stored, any vary between different products. However, the inventory carrying cost is mostly dependent on the stored volume, which means that increased volume also increase the inventory carrying cost. In order to compute the inventory carrying cost, a carrying charge needs to be calculated. The carrying charge takes into account of both capital cost and cost of risk. Capital of cost is increasing proportionally with increased inventory value and the cost of risk is usually assumed to react in the same way, even if it is difficult to prove a proportional relationship between cost of risk and inventory level, the cost of risk often are assumed to increase when the inventory levels increase because of the fact that the probability that the different components related to risk of cost increase with higher inventory level. Formula 8 and 9 describe how the carrying charge usually is calculated.

𝐶𝑎𝑟𝑟𝑦𝑖𝑛𝑔 𝑐ℎ𝑎𝑟𝑔𝑒( %) =∑ 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑜𝑓 𝑐𝑜𝑠𝑡

𝑦𝑒𝑎𝑟 + 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑟𝑖𝑠𝑘/𝑦𝑒𝑎𝑟

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝑡𝑜𝑐𝑘 𝑣𝑎𝑙𝑢𝑒∗ 100 (8)

𝐶𝑎𝑟𝑟𝑦𝑖𝑛𝑔 𝑐ℎ𝑎𝑟𝑔𝑒(%) = 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒(%) +∑ 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑟𝑖𝑠𝑘/𝑦𝑒𝑎𝑟𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝑡𝑜𝑐𝑘 𝑣𝑎𝑙𝑢𝑒

∗ 100 (9)

The cost of risk per year is given by adding cost of goods damage, cassations and loss of income together. Factors, as goods damage and cassations can be difficult to obtain, since companies economic systems doesn’t sort the cost in that way. However, companies usually protect themselves against fire, theft etc. through insurance. This clarifies some part of the cost of risk per year. In those cases when the insurance premium is proportional to the inventory value, carrying charge could be compute through the company’s total carrying cost, formula 10 (Aronsson et al. 2003).

Carrying charge (%) =

discount rate(%) + insurance premium(%) +∑ other cost of risk/year

Average stock value∗ 100 (10)

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Inventory carrying cost, for individual products or for the entire product range is calculated by formula 11.

Inventory carrying cost = Carrying charge ∗ Average stock value (11)

Since the risk of obsolescence is different for various products, the carrying charge should be different for various products; but usually a general carrying charge is calculated and used for all of the products.

There have been different thoughts, if the warehousing costs should be taken into account in calculation of the carrying cost. Some author’s means that the warehousing cost is affected by changes in stock volume (especially large changes), and by that reason it should be included in the calculations of the carrying cost. Aronsson et al. (2003) consider changes in warehousing costs as important, but means that warehousing cost should be separated and not included in the carrying charge. The reasons are; there is no clear correlation between warehousing cost and stock volume, decreased stock volume does not automatically mean decreased warehousing cost, many of the warehouse costs are constant during a long period, until an increase occur for example additional of one more warehouse, then the cost increase. It could also result in unclear inventory control if warehousing costs are included; hence increased carrying cost leads to the desire to reduce the stock level.

2.12. Motivation of choice

To sum up the review of the theoretical framework, a motivation of chosen theory for this thesis is presented.

Production planning and scheduling problems are often NP-problems, were they often are studied as an optimization problem. Since manufacturing systems are complex, it could in some way be difficult to use optimization algorithms on a real production planning and scheduling problem (Kang & Choi 2010).

The product wheel concept is a heuristic model for production scheduling, which requires knowledge and experience about the observed object. Factors as changeover time, sequencing of changeovers, and product demand, demand variability, inventory carrying cost and changeover cost are managed in the

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model. Which makes it suitable for the thesis addressed problem in section 1.2. The model also managing uncertainty, product variety and resource configuration, which all has an impact on mix flexibility (Wilson & Ali 2014). In the litterateur, there has been no research on the product wheel concept applicability on a paper machine. Wilson and Ali (2014) studied a filling line in their research, which function is to fill, label and box products. The filling line has two general types of changeover, one involving size and another involving labels, and changeover time is respectively 1-hour downtime and 10 minutes downtime. Compared to a paper machine, there is no downtime during the changeover procedure, since it is a continuous process; instead manufacturing of non-approval paper takes place during the changeover.

Henceforth the theory about the product wheel concept will be used to investigate the aim of this thesis, which also including the theory of mix flexibility, cyclic production MTO/MTS and Inventory.

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3. Method

This section presents the study’s methodological choices regarding aim, implementation and data collection. In the end of the section, a method discussion is kept regarding the study’s validity and reliability.

3.1. Methodology

As the aim of this thesis is to investigate if and how the product wheel framework can be used to coordinate the production cycle, to manage a wide product range and sequence-dependent changeovers for paper producers, to achieve mix flexibility and improve the scheduling. There will be a need to answer questions like:

What are the factors that affect the production? How do they influence each other?

This makes it suitable for a case study approach as they are good to use to investigate how, what and why questions (Voss et al. 2002; Borrego et al. 2009). Not only answer them but also to get a deeper understanding of the phenomenon that the look at in all of its complexity.

This thesis also aims to contribute with empirical validity to the product wheel concept, and validate Wilson and Ali (2014) findings of achieving mix flexibility through the use of product wheels. Since the aim also is to test an already existing model make the case study a suitable methodology as case studies is also suitable for testing and refining existing theory and ideas (Voss et al. 2002). Another advantage of case research is that the study is done in its natural setting.

One of the drawbacks of case studies is that they only look at a limited number of cases and in the case of this thesis only one, due to time constraints. So to draw general conclusions from a case study and especially from a single case study can be a problem, so care is need when drawing general conclusions (Voss et al. 2002). When doing case research it is important that it is conducted well, so that the results are both rigorous and relevant.

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3.2. Research design

As a research design a single case study is chosen. The aim of this study is to investigate how paper producers can coordinate their production cycle to manage a wide product range and sequence-dependent changeovers and by doing so achieve mix flexibility and improve the scheduling, at the operational level. The product wheel concept developed by King (2009) will be used as a model to investigate the aim of the thesis. Empirical data from the case company will be used to analyze the model in the context of paper manufacturing.

Implementation:

At the beginning of the thesis work, an introduction was made of the case studied company’s manufacturing process. The first step, in order to conduct with the complexity of the stated problem, open conversations with personnel at the studied company were made. Different views on the current production cycle were made from unstructured interviews with mill manager, production planner, process engineer and financial manager. Parallel to this, the work with the literature review started, with focus on the process industries in the areas of manufacturing flexibility, lean, and production planning and inventory management, to gather a good understanding about the research areas. Information to the literature study was gathered from research papers and books, to evaluate what has been done in previous studies related to the problem described in this thesis. In the next period of time, the focus was to gather a deeper understanding of parameters that affect the production cycle. In order to understand the production logic, due to possibilities and constraints in the manufacturing process, semi structured interviews were conducted with machine operators, process engineer and production manager. Since the chosen model to study is a heuristic technique, knowledge and experience about the manufacturing logic is important to achieve the optimal design of the production cycle. Plant observations were also made to gain an understanding of the manufacturing process, and on how the procedure during changeovers occurs. Data were also collected from the case company’s business system and from calculation documents. The collected data were analyzed through the approach of the product wheel concept to develop an effective utilization of the production cycle, considering mix flexibility, sequence-dependent changeovers and inventory carrying costs.

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3.2.1. Primary data

Interviews:

According to Hancock & Algozzine (2011) there are some aspects, which are important for a successful interview. When collecting data from interviews it is important to identify key practitioners, who can contribute with knowledge and opinions that provide important insights related to the research questions. The interviews should be prepared with appropriate questions, for example open-ended questions that will gather insights to the research questions. The interview setting should be considered of where the interview will occur, either in a natural setting that may improve realism or in a more comfortable setting that may contribute to increase the probability of high quality data. The best way of collecting data from the interviews is to record the interviews with an audiotape, to avoid missing valuable information. Collecting data by handwritten notes may lead to loss of information. The interviews have to accompany ethical and legal requirements (Hancock & Algozzine 2011).

To get a holistic view of the current production cycle at the case company, interviews were conducted with participants with different insights and knowledge related to the production cycle. The interviewed participants were mill manager, production planner, process engineer, production manager, financial manager and machine operators. All interviews were conducted in the participant’s office, and in the case with the machine operators; the interviews were performed in the machine operator room.

Unstructured interviews:

In the beginning of the thesis work, interviews were conducted in an unstructured form with mill manager, process engineer, production planner and financial manager. The information from these interviews formed the based to the semi-structured interviews. During the unstructured interviews, handwritten notes were taken. The interviews were conducted in a conversational mode, were each interview took between 45 minutes – 1 hour.

Semi-structured interviews:

Semi-structured interviews allow gathering data from the participant’s self-perceived experience, where they get the opportunity to speak freely from their perspective. There is also room to ask follow up questions to get a deeper insight into interesting areas (Hancock & Algozzine 2011). In a later part of the thesis

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work, semi-structured interviews were conducted with process engineer, production manager and machine operators. The interviews has been done in order to gain an understanding of the production- logic, the procedure of changeovers, the technical limitations and where the cost occurs - related to the production cycle. Choices of interviewees have been based on getting different perspective on the production cycle, with focus on the changeovers; all of the semi-structured interviews were audiotaped. The interviews have been done in a mixed form of structured and qualitative interviews (Yin 2011); the structured part has taken form of prepared questions to collect data for evaluating procedures and limitations concerning changeovers in current production cycle. Based on prepared questions, the interviews have been given space to take their own form in a conversational mode.

By mixing the interview forms, an understanding of both the production logic procedure and the participant’s own experiences about the production cycle, has been taken into account (Yin 2011). The interviews intend to provide empirical data to understand the production logic, which is important to understand in order to design the production cycle. Open-ended questions were prepared before the interviews were made. During the interviews, follow up questions were made in order to get more detailed information. Three interviews were made with machine operators, one with the process engineer and one with the production manager. Questions can be seen in appendix 1. The interviews with the machine operators took around 15 -25 minutes, 30 minutes for process engineer, and 45 minutes for the production manager.

Observations:

To get a deeper understanding of the manufacturing logic and the procedure during changeovers, unstructured observations were made. The machine operators showed and explained how different process steps affect the changeover procedure, and differences between different types of changeovers. Two unstructured observations were made, at two different occasions, with two different machine operators.

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3.2.1. Secondary data:

Data have also been collected through the case company’s business system and from administrative documents. In case study research, documents are a useful way to collect data, but the documents should be treated carefully and not been taken for giving accurate descriptions. On the other hand, it can provide details that can be confirmed by other sources (Yin 2009). The administrative documents that have been evaluated in this thesis consisted of production logs, product data sheets and production calculations, which describing produced volume for different grades, specific requirements for the different grades, the cost situation of changeovers, cost of waste, which types of wastes that occur during changeovers and cost for produce non approved tissue paper. The collected data were from the manufacturing year 2014. Archival records is a collecting method where data usually is collected from stored computer files or records of various kind, these kind of data could be useful in case studies, and in some cases it can become the object for a comprehensive examination and quantitative analysis (Yin 2009). From the case company’s business system, production data have been gathered in form of where and when different products have been manufactured and in what volume. Inventory levels have also been gathered from the business system.

3.2.2. Data analysis

In the analysis of the product wheel concept production logs (historical data) were used for determination of cycle length, product demand and demand variability and manufacturing quantities. For the designing part of the wheel (i.e. in which order the products should be produced) data from interviews and product data sheets were used, in order to find a possible schedule of the production cycle.

Based on the proposed production cycle we discuss the effects of the new cycle in terms of flexibility and cost referred to the literature, and addresses the consequences of the new approach.

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3.3. Reliability and Validity

A valid study is one that have both collected and interpreted its data in a proper way, so that the conclusions correctly reflects and represents the real world (Yin 2011). According to Voss et al (2002) it is particularly important to pay attention to reliability and validity when doing case study research. They also list a number of dimensions of reliability and validity. Construct validity, are the study setup in a correct way to measure the concepts that is studied. Internal validity to what extent can causal relationships be establish, to show that certain condition leads to other conditions. External validity is to know if a study’s findings can be generalized beyond the immediate case study. Reliability is to what extent the study’s operations can be repeated, with the same results. Since this is a single case study that will limit the construct validity and external validity. As a good way to achieve construct validity it is good to use multiple sources of evidence (Voss et al. 2002; Yin 2009), in this case all of the sources will come from the company that is looked at in the case study, but within the company interviews and other data have been gathered from people at different positions within the company, from operators to higher management. This is done to collect data from different sources from the studied case. Yin (2009) also says that establishing a chain of evidence could be a tactic to insure construct validity. Since the technical setting is almost the same for all paper machines the results could to some extent be generalized to other settings. In this case there is also an attempt to validate a previous study in a new setting so some external validity could be reached if the same findings will be found in this study, as Yin (2009) says there is always a need to test a theory in a different setting and be able to replicate the result to be able to completely be generalize the findings. To make sure that the study has a high reliability it is important to have a good documentation of the data collected (Voss et al. 2002), so the semi-structured interviews have been transcribed. High reliability is also reached when the study is repeatable (Yin 2009; Voss et al. 2002) and to make that possible, how the research have been conducted have been explained as thoroughly as possible. Also the results of the calculations done with the collected data will be shown in the appendix. It would also have been optimal to show the data used for the calculations, but they have been left out due to confidentiality reasons.

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4. Paper manufacturing

This section gives a brief description of the objectives in paper manufacturing to gain a better understanding of the manufacturing process, its starts with a presentation of the paper machine different parts, the stock preparation and ends with describing the main differences between tissue manufacturing and paper/board manufacturing.

4.1. The paper machine

The paper machine consists of a number of components with different objectives. The headbox, which is placed in the beginning of the paper machine, dilutes the fiber suspension evenly on the moving wire. At this moment the paper web is formed. The wire is built up by woven cloth with a mesh size, which allows water to be drained while the fibers are retained. This part of the paper machine that’s including the head box and the wire is called the wire section. Next section is the press section, where the paper web is pressed between pressing rolls that remove water from the paper web. The paper web has then been transported from the wire onto a press felt, so when the pressing occur the paper web is transported by the press felt. These two sections, the wire section and the press section belong to the part of the paper machine called the wet end. The remaining part is called the dry end, where the first section is the drying. In the drying section the remaining water is removed by steam heated cylinders. Many paper grades are then calendared, which means that the paper web passes through nips between rolls, to improve the surface properties of the paper. If the paper quality demands a very even surface, it usually needs to be coated. The paper web is coated through applying of a coating mixture, that even out the paper surface. In the end of the paper machine, the paper web is reeled (Ek et al. 2009a)

4.2. Stock preparation

Before the fiber suspension reaches the headbox on the paper machine it needs to be prepared to meet the requirements for the specified quality. The stock preparation contains of operations as mixing components or pulp slushing, refining, adding of chemicals and cleaning (Ek et al. 2009a; Ek et al. 2009b). The main objectives can be summarized in to prepare fibers, ensure a homogenous and clean pulp. The stock components consist of chemical pulp, mechanical

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pulp, recycled fibers and broke. Broke are rests that occur during the manufacturing on the paper machine, it could also come from the winding or converting. The pulp is slushing in a pulper, were the pulp is diluted and dispersed with white water to a pumpable consistency. The pulp is than cleaned from impurities through screeners and high-density cleaners. In the next step the fibers are refined in a refiner where the fibers are treated mechanically to increase the bonding potential. The prepared stock is than pumped to a machine chest.

4.3. Short circulation

When the fiber suspension is distributed on the moving wire, a large amount of water is passing through the wire. The passing water is then taken up in a flume in order to be reused; the water is then used to dilute the incoming fiber suspension to a concentration of 0.2 %. The circulation of water will result in excess; the excess is used to control the consistency in the stock preparation and pulp slushing. The reused water is called white water (Ek et al. 2009b).

4.4. The main differences between Tissue manufacturing and paper/board manufacturing

In chapter 5.1 a description of the paper machine where outlined, the described parts of the paper machine are largely the same for both tissue machines and paper/board machines, the main differences are in the structure of the components. The forming section of a paper/board machine, are often consisting of more than one headbox, to produce different layers. Two typical forming sections are fourdriner forming and twin-wire forming. Dewatering in a fourdriner former takes place through a horizontal wire supported by number of rolls where water is removed by gravity and vacuum. In a twin-wire former, the fiber suspension is dewatered between two wires (Ek et al. 2009b). There are different concepts for the forming section on a tissue machine, for standard tissue paper, the cresent former is most common, where the forming is done between a wire and a felt in a converging nip (Holmark 2011). In tissue manufacturing multi-layer headboxes can be used, this means that different types of stock (pulp mix) can be fed out from one headbox. Compared to the headboxes used in paper/board manufacturing, where different headboxes are used for each layer. However, there is one board product type called Liner where the two-layer technique can be applied (Ek et al. 2009b). Since the tissue paper

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is a light weighted paper (8-40 g/m2), it is dried over one giant cylinder called the Yankee cylinder, the paper is also dried through high temperature air that is fed with high impingement velocity from the Yankee hood (Holmark 2011). For paper and board grades, which have a higher base weight than tissue, drying occur through a multi-cylinder, where the paper is dried over steam heated roll (Ek et al. 2009b). A main difference between tissue and paper/board is that tissue is a creped paper. Which means that the paper is separated from the Yankee cylinder by a doctor blade, this creping process gives softness to the final product (Holmark 2011).

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5. Empirical findings/Analysis

The first section 5.1, presents the empirical data from interviews, to describe the production logic. Then in section 5.2 the product wheel concept is tested with real production data. The last part 5.3, the developed production cycles from the product wheel is examined through a cost analysis.

5.1. Changeover procedures at the case company

The case studied company has three main types of changeovers that occur in the current production cycle. Since, the studied company is a producer of colored tissue, the main differences in changeovers is related to the handling of colors. The first type of changeover starts with white uncolored pulp, which is prepared and mixed in the pulper. The color is then added before the headbox, i.e. late in the process. In this kind of changeover, edge stripes from the creping process on the Yankee cylinder goes back to the pulp tank, which adds a small amount of color to the tissue paper. The changeover includes an empty run of the last pulp tank, when it is empty and clean, the color pumps are adjusted and the new color is set to start. Before actual changeover, empty run of the pulp tank is prepared when the tissue machine is running at full speed. When the new color is started, it needs to be adjusted to the right quality, i.e. within the approved limits for the specific quality. The second changeover could be divided into two types, A and B. Equal for these two types, is that color is added early in the process, i.e. in the pulper. In the case of type A, the same white water can be used in the changeover. For example when producing green tissue paper and then change to brown tissue paper, same white water could be used, and there is no need for clean water. When the last batch is producing, preparation starts for the new color, and the second pulper is than cleaned and refilled with new pulp and color. When the color is adjusted during the changeover, the system will be in disequilibrium since the system is changing fiber. This will cause waste production. In the case of type B, white water could not be used and need to be shifted, this is done before the changeover through diluting the white water with fresh water. The reason for not using the same white water is because it causes problem during the changeover, where the used white water will add color to the new quality, which will lead to increased losses. At the changeover, the white water will not circulate. Instead, it runs to the drain, which will lead to fiber waste. After the system reaches equilibrium the white water can start to

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recirculate. For both type A and B, color is adding in the pulper and adjusted later in the process from the color pumps.

In the first type of changeovers there will be no color waste as it is in the seconded type. This is a result of that the rewinding section after the paper machine, need similar size of the tambours (diameter and length) from the paper machine, the rewinder rewinds the tissue into 2- and 3 layers and also into different sizes for the converting. In the seconded type this could lead to some waste because of the difficulties with getting the exact similar tambours. For the first type this will be no problem, since the added color comes from the color pumps later in the process and the same pulp is used for the next quality.

The main cause for long changeover time and waste production is the adjustment of color. This critical moment could vary in time, the color pumps are adjusted manually, and the adjustment is based on the samples that are taking during the changeover. Reasons for variation in time, is depended on what color were produced before the change. A changeover, which contains of a color change, is taking about 25 minutes.

There is also another type of changeover, which is in the same color, and the differences are in base weight and pulp. These kinds of changeovers are quite simple, compared to the color changes.

A cleaning stop is made before a new cycle is started, where the paper machine is chemically cleaned. The cleaning stop starts directly after the last color quality is produced.

During a color changeover, some different costs occur. Costs can be derived from waste production, which consists of production of wrong color (i.e. production of not approved quality, which mainly takes place during the transition period of the changeover) bleeding of color (quality problems that occur as a reason of changeover), and fiber losses (which occur as a reason of changeovers). Not all bleeding is due to color changes, but it do increases with more color changes so some of it will be associated with color changes. From most of the waste production the fiber material can be recycled and used again in the production but when white water is shifted some fibers go with the water to the drain.

The first kind of changeover occur early in the production cycle since it starts with white pulp and color is added late in the process, later (i.e. after the first type) in the production cycle the seconded type of changeover occur (adding

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color early in the process), where type A is more common than B due to avoid of changing white water, which can be problematic. The sequencing (i.e. in which order the colors should be produced) step of the product wheel is done after these three kinds of changeover types.

5.2. Using the Product wheel to set up a production cycle

The product wheel concept will be analyzed for scheduling the production cycle on a paper machine, as all after lying production depends on the production on it. Today the case company has the ability to produce tissue with about 70 different colors and during 2014 they produced around 50 of them. Within each color they produce between 1 and 15 different grades. Changing from one color to another is the type of changeover that takes the longest time, and because of this it has the highest cost. The color changes are also the one factor that limits the production cycle the most, as the sequence of witch colors are run in affects the changeover time the most. Because of these two reasons the product wheel will be used to set up a cycle based on colors.

Since we have decided which part of the production line to be investigated, next step is to analyze the product demand variability. For demand, the monthly production data for each color for the year 2014 have been used, since the case company did not have any clear numbers on how much they ship out of the different colors per month. In figure 4, the monthly demand of all the different colors can be seen and a few color accounts for most of the demand.

Figure 4: Monthly demand for the different colors produced.

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To analyze the demand variability, the standard deviation of the monthly demand and the average demand per month is needed and from this the coefficient of variation (CV) is calculated through formula 1.

𝐶𝑉 =𝜎𝐷

𝐷𝐴𝑉𝐺 (1)

CV shows how much the demand varies in relation to the average demand. To show how this is done for all colors, table 2 shows average monthly demand, standard deviation, CV and how we classified them as either MTS or MTO by the decision matrix in figure 5. A sample is presented as showing it for all would lead to very large tables, but the full tables is presented in the appendix.

Table 2: Demand variability for a sample of the products.

Color nr Demand/month

(1000 kg) σD (1000 kg) CV MTS/MTO 34 217,8 68,4 0,3 MTS 17 139,6 48,9 0,3 MTS 26 74,1 19,9 0,3 MTS 1 57,1 24,1 0,4 MTS 46 48,5 39,7 0,8 MTS 30 39,5 38,5 1,0 MTS 2 13,2 12,4 0,9 MTS 63 12,7 14,2 1,1 MTS 56 8,0 12,1 1,5 MTS 13 7,4 11,5 1,6 MTS 67 5,3 12,5 2,3 MTS 52 5,1 12,2 2,4 MTS 22 3,2 7,6 2,3 MTS 47 1,1 4,0 3,5 MTO 49 1,0 3,4 3,5 MTO 14 0,6 2,2 3,5 MTO 54 0,6 2,2 3,5 MTO

From the decision matrix in this case, the decision is made that only the colors that have very low demand and very high variability will be classified as MTO, and be thought as being produced once a year.

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Figure 5: Decision matrix for classified colors as either MTS or MTO.

The next step is to decide how long a cycle should be. That is done first by calculate the shortest possible wheel time by using formula 3 and 4. The total available time is 349 days, the number of days per year the machine is running, and it takes 333,2 days to produce the demanded amount of tissue, total demand divided by average production rate. With 349 production days the production month will be 29 production days and this will be used as the length of a month in the calculations. The total changeover time per cycle depends on how many colors there are in each cycle, the average change over time per color, including changes of different grades with in the color and time producing wrong quality due to bleeding associated with color change, are 0,027 days and a cleaning stop that is needed per cycle is 0,29 days. If all 47 colors should be produced it gives the flowing result:

𝐶𝑦𝑐𝑙𝑒𝑠𝑃𝑒𝑟𝑖𝑜𝑑⁄ =

𝐴𝑣𝑎𝑖𝑙𝑖𝑏𝑙𝑒 𝑡𝑖𝑚𝑒 − P𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒∑ 𝐶ℎ𝑎𝑛𝑔𝑒𝑜𝑣𝑒𝑟 𝑡𝑖𝑚𝑒𝑠 𝑝𝑒𝑟 𝑐𝑦𝑐𝑙𝑒

= 349 𝑑𝑎𝑦𝑠 − 333,2 days

1,6 𝑑𝑎𝑦𝑠

= 9,9 cycles𝑦𝑒𝑎𝑟 ⁄ (3)

𝑊ℎ𝑒𝑒𝑙 𝑡𝑖𝑚𝑒 =𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑡𝑖𝑚𝑒

# 𝑐𝑦𝑐𝑙𝑒𝑠 / 𝑝𝑒𝑟𝑖𝑜𝑑 =

349 𝑑𝑎𝑦𝑠9,9

= 35,3 𝑑𝑎𝑦𝑠𝑐𝑦𝑐𝑙𝑒⁄ (4)

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So if all colors should be made in each cycle the shortest cycle length would be 35,3 days (1,2 months) and 9,4 cycles per year, but that all colors would be produced in all cycles is not that likely as the demand for many of them is very low. If we instead look at how they produce today with 25 colors in each cycle, the total changeover time would be 1 day and then you get a shortest cycle of 21,5 days ( 0,74 months) and 16,2 cycles per year. When the shortest possible wheel time is known, determination of the economic optimum wheel time needs to be made. This is done with the EOQ model. EOQ is calculated for each color, and this is done with formula 6.

𝐸𝑂𝑄 = √2 ∗ 𝐶𝑂𝐶 ∗ 𝐷

𝑉 ∗ 𝑟 (1 − 𝐷𝑃𝑅)

(6)

EOQ model gives the quantity that is the most economical to produce for each color. The unit cost (price), changeover cost and production rate are taken from the case company’s internal documentation and is a calculated average for all grades within each color. The changeover cost here is only the one that is from changing colors, so the cost of producing the wrong color during the change and half of the cost for producing tissue that has color bleed. The largest cost here is producing tissue with the wrong color. The reason only half of the cost for color bleed is used because that not all bleeding is due to the color change. To get a more complete picture of the cost for the grade changes within the colors could also be used, but it is not done here as how many grades there is per color varies between cycles. To calculate the EOQ the cost of carrying inventory (r) need to be known. This can be calculated with formula 9.

𝐶𝑎𝑟𝑟𝑦𝑖𝑛𝑔 𝑐ℎ𝑎𝑟𝑔𝑒(%) = 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒(%) +∑ 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑟𝑖𝑠𝑘/𝑦𝑒𝑎𝑟𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝑡𝑜𝑐𝑘 𝑣𝑎𝑙𝑢𝑒

∗ 100 (9)

The case company’s discount rate is 10% but they have no information about the risk costs. Therefore 3 different carrying charge will be used, 10 %, is the company’s discount rate, 15 %, is the carrying charge used for calculating the current production cycle and will be good for comparing the new cycle with their current setup, and 20 %, is to see what a higher carrying charge have for effect but to at the same time being conservative and not using an even higher

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charge. There is no clear motivation why they use 15 %. When the EOQ for each color is known the optimum frequency is calculated by dividing the EOQ with the demand for each color. How this look like for the same sample colors as above can be seen in table 3, calculated with r = 15 %. The next step is now to select how many cycles that should be made per year. This is done so that as many colors as possible get close to its optimum frequency and a higher concern have been taken to the colors that has an optimum of being produced frequently. For the three different carrying cost the cycle times of 12 times per year for r = 10 %, 16 times per year for r = 15 % and 18 times per year for r = 20 % were selected. The number of cycles per year was also chosen so that there would be an even amount of cycles. For the 16 and 18 cycles per year there will be a need for them to be less than 25 colors per year to be able to produce the demanded amount of tissue.

Table 3: EOQ, optimum and recommended frequency for a sample of the colors (r = 15%).

Color nr

Demand / month (1000 kg) EOQ

Optimum Freq. (Months)

Recommended Freq. (Months)

Recommended Freq. (Cycles)

34 217,8 180,4 0,8 0,75 1

17 139,6 135,5 1,0 0,75 1

26 74,1 95,3 1,3 0,75 1

1 57,1 83,2 1,5 1,5 2

46 48,5 77,3 1,6 1,5 2

30 39,5 75,7 1,9 1,5 2

2 13,2 37,0 2,8 3 4

63 12,7 44,7 3,5 3,75 5

56 8,0 32,3 4,0 3,75 5

13 7,4 31,0 4,2 4,5 5

67 5,3 23,9 4,5 4,5 5

52 5,1 24,6 4,8 4,5 5

22 3,2 19,5 6,1 6 8

47 1,1 11,9 10,8 MTO

49 1,0 11,9 11,9 MTO

14 0,6 8,4 14,0 MTO

54 0,6 9,5 15,8 MTO

The different colors where then distributed over the cycles, with the recommended frequency between them and so that the production was close to be evenly spread over all the cycles. Each of MTO colors are considered to be produced during one cycle per year. No account is taken to seasonal demand

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that can vary over the year. When this was done three different yearly production plans where created. For when r = 10 % the number of colors per cycle varied between 17 and 19 with a mean at just above 18 colors. For when r = 15 % the number of colors per cycle varied between 15 and 18 with a mean of 17 colors per cycle. Last when r = 20 % the number of colors per cycle was also between 15 and 18 but with a mean of just above 16 colors. How a full yearly production set up could look like can be seen in table 4, this is how it looked like for the r = 15 %. The colors are set up to be produced in the order they use today, as they have not that long ago gone through all colors and made sure that color that is close to each other are produce after each other. This is done so the difference between the colors that follow each other is as small as possible. This has been done with the apparatus that they use to measure colors. Each number in the table represents a color and its order in the preferred order to manufacture it.

Table 4: Color distribution over the cycles during one year.

Sequence Nr: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Cycle 1: 5 6 7 12 15 17 20 29 30 34 37 39 41 45 51 53 56 65 Cycle 2: 1 2 4 6 17 18 20 23 26 29 33 34 45 46 51 52 53 Cycle 3: 6 12 15 17 18 20 22 29 30 34 38 39 41 45 53 56 62 67 Cycle 4: 1 6 17 20 24 26 29 34 37 43 46 48 51 53 63 Cycle 5: 5 6 15 17 20 29 30 34 35 39 41 45 51 53 54 60 61 63 Cycle 6: 1 2 6 13 17 18 20 23 26 29 31 34 46 51 52 53 56 Cycle 7: 3 4 6 15 17 20 29 30 34 36 37 38 39 41 51 53 62 Cycle 8: 1 4 6 17 20 24 26 29 33 34 45 46 51 53 59 65 Cycle 9: 5 6 7 15 17 18 20 29 30 34 36 39 41 48 51 53 59 67 Cycle 10: 1 2 6 12 17 20 26 29 34 37 46 47 51 53 62 63 Cycle 11: 6 14 15 17 20 22 29 30 34 38 39 41 45 51 53 56 62 Cycle 12: 1 6 13 17 18 20 23 24 26 29 34 46 51 52 53 56 61 Cycle 13: 1 2 6 15 17 20 29 30 34 35 37 39 41 51 53 62 Cycle 14: 1 2 6 17 20 26 29 31 33 34 45 46 48 51 53 59 61 Cycle 15: 6 15 17 18 20 29 30 34 38 39 41 48 49 51 53 63 65 67 Cycle 16: 1 6 13 17 20 24 26 29 34 36 37 46 51 53 62 66

It is worth to note though that all of the cycles in all three cases will vary in length as the amount of tissue that needs to be produced will vary between them. But for the 16 cycle per year the period would be:

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349 𝑑𝑎𝑦𝑠16

= 21,81 𝑑𝑎𝑦𝑠

The change over time for one cycle including the cleaning stop would be 0,75 days. That would make it a total of 21,1 days to produce 1368 t, the total average demand per cycle. The production rate needed would then be 64,8 t/day and for our calculations an average production rate of 65,7 t/day have been used, so there is the capacity to produce the needed amount. Then you also need to make sure you can handle the proposed inventory levels that is needed for the production wheel, the case company have today the capability to handle large amount of inventory so we will not look in to that any further.

The production wheel concept is having some more step but they will not be done as they have no impact on how the production cycle will look like and is more how to use it in the everyday production.

5.3. Testing the new cycles

To compare how this new cycle stand up economical to the current set up, the total, inventory and changeover cost were calculated for each set up, and how those cost change depending on the number of production cycles per year. All the three different cycles gave a lower total cost then the current when using the carrying cost that was used to create the new cycles. The cost is even lower if you increase the number of colors per cycle with a couple.

When looking at the cost for the current, 25 colors per cycle and an r = 15 %, the number of cycles that would give the lowest cost would be between 10 and 11, see figure 6. This is lower than the 12 cycles per year they use now.

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Figure 6: Total cost for 25 colors per cycle and inventory carrying cost (r = 15%), depending on how many cycles per year.

Comparing this to how the new 17 color cycle would look like, figure 7, the number of cycles that gives the lowest total cost would be between 12 and 13. Comparing the total cost of today’s cycle, 25 colors 12 cycles per year and the total cost of the new, 17 color cycle 16 cycles per year the cost would be about 11 % lower. The proposed number of cycles for the 17 color cycle is even farther away from the number of cycles that gives the lowest total cost then what the current setup is.

One factor for this could be that the new cycle is that the cleaning stop needed for each cycle is not taken in to account when the EOQ was calculated as it is hard to split that cost on specific colors, when you do not know how many colors there will be in the cycle. Also here the changeover cost for changing between different grades was used, as the number of these changeovers varies less when looking at a whole cycle then when looking at how they varies in a specific color in a cycle.

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Figure 7: Total cost for 17 colors per cycle and inventory carrying cost (r = 15%), depending on how many cycles per year and compared with today’s total cost for 25 colors per cycle.

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6. Discussion

In this section the findings of the study will be discussed. First, a discussion about the different changeover types that the case company has on their tissue machine will be done. Then the answers to the research questions will be discussed, and at last there will be a general discussion.

6.1. The case company’s different changeover types

In order to determine the optimum sequence, i.e. in what order the products should be made in the production cycle, an understanding of the manufacturing process is important (King 2009). The case studied company’s current production cycle is scheduled after three main changeovers, which involve changes of colors. Within one color there are also different grades consisting of various base weights and pulp types. The grade changes are simple, and differ markedly to the color changeovers. A color changeover is considerably more costly compared to the grade change, due to the production waste and changeover time. For effective sequencing, similar products should be grouped together (King 2009), the case company’s current production cycle, is based on simple changeovers, were the difference in color, pulp and base weight, between a changeover is as small as possible. When there are large differences between the products this will results in long changeover times with wasted production. Therefore, the design of the production cycle will be affected of the changeover types. Colors have a significant role in changeovers for the studied company, and affects the cycle in which order the products can be produced. In the scheduling procedure of the wheel, considerations have been taken to the changeover types. The second type of changeover is most common in the production cycle, since most of the products are deep colored.

6.2. RQ1: How can paper producers coordinate the scheduling of the production cycle in an efficient manner, in order to improve the flexibility?

Since most paper manufacturing occurs in production cycles, the product wheel could be a good method to use in the paper industry. It is also have a clear step-by-step approach, but it is a heuristic technique that requires the use of judgment and experience (Wilson & Ali 2014). In cases where paper producers need to

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manage a wide product range, similar to the case company, it is beneficial to group similar products together, since it shortens the changeover times. Then only the longer and more complex changeovers can be viewed in the wheel, as it was done for the case company where grades of the same color where grouped together. The product wheel also considers the changeover time/cost, which form a good basis for decision on how flexible the manufacturing system should be related to costs. For the case company it was hard to take into account the cost of the cleaning stop that is needed once per cycle, this is though specific for this case since the pulp is colored in the manufacturing process, so this is not a common feature for the paper industry. The product wheel concept also supports the common approach to set-up cyclic production that Matsson and Jonsson (2003) describe. Both models take account of available production time and the EOQ concept. One thing the model does not handle well is when the production goes wrong outside of the changeovers, like extra maintenance and repairs that needs to be conducted. The product wheel can be an appropriate tool to use in the paper industry, if the flaws of the model are taken in to account.

6.3. RQ2: How will an improved flexible production cycle occur compered to the current situation, for the case company?

All three new production cycles have fewer colors per cycle than what the case company uses today. Matsson and Jonsson (2003) recommend that number of cycle per year follow the geometric series 2n. This is only true for one of the created cycles, the one where 16 cycles per year is made. The other two is on the other hand, 1 cycle per month and 1,5 cycles per month. How the different cycle looks like depends a lot on what inventory carrying cost that is used. When using a carrying cost of 10 %, the cycles include fewer colors but with longer production length for each color, since having a low carrying cost makes it so that changeover cost is relatively higher. The carrying cost of 20 % gives more cycle per year but with even fewer colors and much shorter production runs. The cycle created with the carrying cost of 15 % gives 16 cycles per year, four more than the current situation, and with 17 colors per cycle, 8 less than what they do today. This is the cycle that gives the best comparison with the current cycle, as both have been created with the same carrying cost, even if they have not been created with the same method. The production wheel model generated a cycle with fewer colors in each cycle but with more cycles per year.

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6.4. RQ3: What will be the effects of the suggested production cycle, in terms of cost and flexibility for the studied company’s perspective?

What can be seen is that the created cycles give a lower total cost than the current cycle. Which indicates that more cycles with fewer colors in them, compared with today’s setup, would be the way to go when setting up the production for the case company from a cost perspective. The reason for this is in large from that the total number of color changes per year is lower than 300 they produce per year today, but when looking at the 16 cycles per year set-up and even if you increase the amount of colors per cycle to 21 (336 total change overs per year) it will still give a lower total cost then the current model. When looking at number of cycles, all set-ups created have their most optimal number of cycles, from a lowest costs perspective, lower than the number that they got from the production wheel model. Part of this is due to that the cleaning stop once per cycle was not included when creating the new cycles. But also with their current set up they run a couple of more cycles per year than what gives the lowest cost. This can indicate that the case company wants to have a higher flexibility, and as Wilson and Ali (2014) took up, they want to be able to change or react to change, and allows the company to manufacture the product range that is demanded by the customers. This is something more cycles per year give an increased opportunity to do. Also since the EOQ curve is flat in the region of the lowest cost, the change in total cost is not that large if the wheel time is somewhat lower or higher (King 2009), so having more cycles per year can be more beneficial since it leads to an increased flexibility and therefore the more cycles per year the new cycles had could be better from a flexibility perspective.

6.5. General Discussion

Production planning and scheduling are critical to the efficiency of a manufacturing plant, and the goal with production planning is to determine what activities to perform so that the production can be done in time and as cost efficient as possible (Kang & Choi 2010; Guimarães et al. 2014). One of the challenges of production planning and scheduling is that this kinds of problems are generally NP-hard (Kang & Choi 2010; Bouchriha et al. 2007). This is the reason that there are heuristic techniques, like the product wheel, to help to find an adequate solution to these problems. A heuristic technique

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requires use of judgment and experience (Wilson & Ali 2014). According to Mattsson and Jonsson (2003) there are two main variants of cyclic production. One with a fixed sequence of items in the production patterns, where the item have a fixed place in the cycle, but all the items are not necessary to be produced in each cycle, thus the items within the cycle may vary from time to time. The other one where the production pattern is fixed in time, all items have a fixed time when they are produced. For the case company and how the product wheel is applied there the letter variant is used, which makes planning the sequence easier as you do not need to have a 100 % fixed time for each cycle, they different cycles can vary somewhat in length, so there is no need to schedule the amount of products, produce in each cycle to be equal. How the production cycle, created with the product wheel concept, looks depends highly on what inventory carrying cost used. Since the exact carrying cost was not known three different carrying cost was used and three different cycles where created. In the existing literature carrying cost varies between 12-50 % (Azzi et al. 2014). Of the three values used, 10 %, 15 % and 20 %, the first one is outside the range in the literature and the two others is also in the lower end of the range. Since the carrying cost has a large impact on the cycle, knowing more exact what the carrying cost is would give a better cycle for the specific situation. But relative low values have been used to get a conservative result.

The product wheel can lead, as it did for the case company, to a mixed MTO-MTS system and for this kind of system the main production planning problem is what to make to stock and what to make to order (Soman et al. 2004). This would for most paper producers leads to a change in strategy as it will be a move from a volume-based approach that is most common in the paper industry (Hameri & Lehtonen 2001; Björk & Carlsson 2007). The possibility of managing MTO products largely depends on the requirements made on lead-times (Soman et al. 2004; King 2009). If the total manufacturing lead time is longer than the agreed customer lead time, MTO is not appropriate (King 2009), which results in that the products needs to be stored to fulfill the lead-time requirements. Which then affect the inventory levels and thus the inventory cost. This is something that a producer will need to take in to account when choosing to change part of its production to MTO and in for the case company that means that the agreed customer lead time must be at least as long as the cycle length for it not to affect the inventory levels, so shorter cycles will make it so that producers can deliver MTO products faster. The decision of what product to produce either as MTO or MTS is complex as many different factors are involved (Soman et al. 2004). The product wheel model uses the average

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demand and the demand variability to guide a decision on which products that should be MTO or MTS (King 2009). All products are classified in one of the four groups in the decision matrix, low demand/low variability, low demand/high variability, high demand/low variability and high demand/high variability. Products classified as low demand/high variability should be managed as MTO and products with high demand/low variability should be manage as MTS. For the two other groups can both be either MTO or MTS depending on the situation (King 2009). There are also other factors that needs to be consider when selecting MTO and MTS products, for example, what the product looks like, how to handle uncertainty, inventory levels, due dates and other factors that also can impact if you are able to respond fast enough to the demand of the market (Soman et al. 2004). Because of this and as what is low demand or low variability depends on in what situation the product wheel is used in, there will be a need to have a good knowledge of the situation where the product wheel is applied. In the case where the product wheel were applied only the colors with very low demand and very high variability was classified as MTO. This have been partly done since the demand and the variation of the demand might not be 100 % correct as the monthly production during 2014 have been used as the basis for it. This can of course be a source for error for the cycles produced. The calculated average demand per month is not as affected by this as the demand variability and the impact of this is especially high on the colors that are produced few times per year. Because we used the monthly production for the different colors and as this was net production numbers this could also have an effect on the results but exactly what this effect is not known.

The big question though is if the product wheel approach leads to increased mix flexibility for paper producers and if it could be used in a wider context. The mix flexibility is the ability to change between products in the current product range that a company produces (Bateman et al. 1999). The results shows that the mix flexibility increased for the case company through the use of the product wheel, in comparison with the current situation, consisting of 12 cycles per year, with each 25 colors, while the developed cycle consists of 16 cycles, with each 17 colors. This results in that a specific color may be manufactured at 16 times instead of 12 times during one year, which also increases the ability to react on changes in demand. This will also have the effect that more products can be MTO, as the needed customer lead time can be lower.

Is the product wheel a good concept to handle mix flexibility? According to Wilson and Platt (2009) there are three main factors that have an impact on mix

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flexibility, product variety, uncertainty and resource configuration. When looking at the product variety, Kotler and Keller (2012) classified four dimensions, width, length, depth and consistency. Width is referred to the number of production lines and is not something the product wheel takes in to account as you would have to use it separately if you have more than one production line. Length is the number of items in the product mix this is something that the product wheel handles as it look at each product/product group, but the more items in the mix the more complex the situation becomes and the harder it will be to create the production cycles. Depth is the number of variants of each product this is something the product wheel takes into account in as if there are variants the changeover time between them is probably shorter. Consistency that is the relationship between the products and this is also taken in to account with the product wheel in that the closer the relationship is between the products the shorter changeover time. This also have effects on the waste production, since shorter changeover times will lead to reduced waste production (Correia et al. 2012), which contribute to a sustainable manufacturing. The product wheel handles uncertainty in the way that it looks at the demand and the demand variability. Resource configuration is the one thing you trying to look at when using the product wheel, but at a specific point in the production chain, as in how to best utilize the manufacturing resources, but it do not look at how it affects the rest of the production line. As seen that the product wheel takes in to account the three main factors of mix flexibility (Wilson & Platts 2009) and when looking at the case company it did help in coordinating the production sequence. The product wheel can be a tool to help dealing with mix flexibility when having a large number of different products, even though if you have very large number like for the case company it can be good to bunch them together in product groups. That the product wheel can be a tool to help dealing with mix flexibility is also in line with Wilson and Ali (2014) findings when they looked at the packing at a chemical plant and with significantly fewer products.

When looking at the daily operation of a manufacturing system there is no real planning and scheduling problems, it’s more like re-planning and re-scheduling ones (Kang & Choi 2010). This created a need for a framework for planning and scheduling, which deal with changes without completely changing the production plans. This is something that could be managed in the production schedule from the product wheel, even if the products follow a specific order there is ability in the planning to add in extra colors and different grades without breaking the order products are manufactured in.

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7. Conclusion

The product wheel concept have been studied in order to investigate how paper producers with a wide product range and sequence-dependent changeovers can coordinate the production cycle to achieve flexibility and improve the scheduling at the plant level. The results from the case study show that the product wheel concept is an appropriate model for scheduling problems in the context of managing a wide product range on a paper machine. The generated production cycle indicates an increased flexibility by scheduling fewer products per cycle and through that more cycles per year, compared to the current production cycle. From the cost analysis, all the generated production cycles has lower total cost than the case company’s current cycle, due to the decrease of total changeovers during a year. In a broader sense, the results of this study shows that paper producers can strengthen their profitability through improved coordination of the production cycle.

The production wheel concept is though a heuristic model and will require knowledge and experience about the observed object.

Findings from this thesis support previous studies that the product wheel concept functions as a coordination mechanism for achieving mix flexibility at the operational level in the process industries. The product wheel concept is also supported by the results of this study, in the extent as an appropriate scheduling technique for the paper industry.

For further research we suggest to study the product wheel in a real world setting to investigate a deeper analysis of the product wheel concept, and also how it affect companies supply chains.

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9. Appendix 1: Interview questions

Interview questions machine operators and process engineer:

1. How does the procedure of a color changeover occur from start to finish? 2. What are the different operations in a changeover? 3. Which working operations, do you consider takes the longest time? 4. What are the limiting factors that affect a changeover? 5. Is there any preparatory operations before the changeover get started? 6. What are the differences in changeover times, between the changeover types? 7. How does a cleaning stop process occur?

Interview questions production manager:

1. How do you perceive the current production cycle? 2. What are today’s bottlenecks? 3. What are the possibilities to reduce changeover times? 4. How do you experience today’s changeovers? 5. How do you experience the production scheduling? 6. Which production disruptions occur during operation, are they recurring?

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10. Appendix 2: Demand and Demand variability

Table 5: Demand and Demand Variability for the different colors

Color nr Demand/month (1000 kg) σD (1000 kg) CV MTS/MTO

6 276,9 197,2 0,4 MTS

34 217,8 68,4 0,3 MTS

29 193,8 95,8 0,5 MTS

17 139,6 48,9 0,3 MTS

51 136,9 85,3 0,6 MTS

53 109,9 56,7 0,5 MTS

20 109,5 32,7 0,3 MTS

26 74,1 19,9 0,3 MTS

41 60,1 47,0 0,8 MTS

1 57,1 24,1 0,4 MTS

46 48,5 39,7 0,8 MTS

30 39,5 38,5 1,0 MTS

15 38,5 14,3 0,4 MTS

39 35,2 17,5 0,5 MTS

37 35,0 15,9 0,5 MTS

45 31,2 29,0 0,9 MTS

62 23,5 23,4 1,0 MTS

18 19,7 28,2 1,4 MTS

38 19,5 21,2 1,1 MTS

24 17,0 21,2 1,2 MTS

2 13,2 12,4 0,9 MTS

63 12,7 14,2 1,1 MTS

5 12,1 18,6 1,5 MTS

48 9,7 10,8 1,1 MTS

59 8,1 10,5 1,3 MTS

56 8,0 12,1 1,5 MTS

13 7,4 11,5 1,6 MTS

4 7,3 13,5 1,8 MTS

23 6,4 9,6 1,5 MTS

33 6,1 9,4 1,5 MTS

67 5,3 12,5 2,3 MTS

52 5,1 12,2 2,4 MTS

36 4,5 8,2 1,8 MTS

61 4,2 7,6 1,8 MTS

12 4,2 7,6 1,8 MTS

65 4,0 7,2 1,8 MTS

35 3,3 6,0 1,8 MTS

22 3,2 7,6 2,3 MTS

31 3,2 5,8 1,8 MTS

7 3,1 5,7 1,8 MTS

43 2,4 5,6 2,4 MTO

66 1,6 5,4 3,5 MTO

3 1,4 5,0 3,5 MTO

60 1,1 4,0 3,5 MTO

47 1,1 4,0 3,5 MTO

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49 1,0 3,4 3,5 MTO

14 0,6 2,2 3,5 MTO

54 0,6 2,2 3,5 MTO

8 0,0 0,0

9 0,0 0,0

10 0,0 0,0

11 0,0 0,0

16 0,0 0,0

19 0,0 0,0

21 0,0 0,0

25 0,0 0,0

27 0,0 0,0

28 0,0 0,0

32 0,0 0,0

40 0,0 0,0

42 0,0 0,0

44 0,0 0,0

50 0,0 0,0

55 0,0 0,0

57 0,0 0,0

58 0,0 0,0

64 0,0 0,0

80

11. Appendix 3: Production cycles created with the product wheel

Table 6: Cycle from the product wheel r = 10 %

81


82


83

12. Appendix 4: Total cost diagrams of for the different cycles not shown in chapter 5 and inventory carrying rates.

Figure 8: Total cost diagram for 16 color / cycle, r = 20 %.

Figure 9: Total cost diagram for 18 color / cycle, r = 10 %.

improved coordination of the production cycle in the paper...

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