supply model design for a dairy company: a case studyieomsociety.org › dc2018 › papers ›...

Proceedings of the International Conference on Industrial Engineering and Operations Management

Washington DC, USA, September 27-29, 2018

Supply Model Design for a Dairy Company: A Case Study

Carlos A. Machado Orges, Diego J. Haro Morales, Leandro L. Lorente Leyva,

Yakcleem Montero Santos and Israel D. Herrera Granda

Facultad de Ingeniería en Ciencias Aplicadas

Universidad Técnica del Norte

Ibarra, Ecuador

[email protected], [email protected], [email protected],

[email protected], [email protected]

Ivet Challenger Pérez

Facultad de Ingeniería Informática y Matemática

Universidad de Holguín, Cuba

[email protected]

Abstract

This research was developed in a dairy company, with the purpose of providing tools that allow the

continuous improvement of supplies necessary for daily production, to ensure the quality of the finished

product and customer satisfaction. A Case study was carried out in the dairy company in Cayambe, Ecuador.

In order to determine the supply management model, a survey was applied to identify the deficiencies and

weaknesses that directly affect the management system within the company. With the help of the

management the historical data of 3 years for the classification of the inputs was collected through the ABC

method, giving a total of 45 inputs and a total cost of USD 126,351.54, within this group of inputs 8 types

of yogurt containers were found. Using the FORECAST PRO TRAC software, the best forecast models

were determined. Through the forecast, the variability coefficient (CV) was calculated, an indicator that

allows us to choose the inventory management model to be used; obtaining as a result the use of the EOQ

model. With the application of this model, it has reached the decrease of orders per month and reducing

supply costs.

Keywords Supply Model, Inventory, Logistics, Store, Dairy Company

1. IntroductionLogistics is nothing more than planning, operating, controlling and detecting opportunities to improve the flow of

materials (inputs, products), services, information and capital. It is the function that normally operates as a link

between the sources of improvement and supplies and the final customer and its distribution. Its objective is to

permanently satisfy the demand in terms of quantity, opportunity and quality at the lowest possible cost for the

company (Carro and González, 2001; Chung, 2008; Hyun, 2013; Heizer et al.,2016). It is an approach that allows the

management of an organization based on the study of material flow, the flow of information and the financial flow

associated with it from suppliers to customers; Taking as objective, deliver the product at the precise moment, the

desired quantity, under the required conditions, all this at the lowest possible cost (Croston, 1972; Hernández, 2008;

Huang, 2003; Armstrong, 2005).

Supplying or improvement is the logistic function by which a company is provided with all the necessary material for

its operation (Silver and Meal, 1973; Gómez and Acevedo, 2000). It is a process in which the supplier sends the

product in response to the customer's orders. Managers must decide on the supply structure of direct and indirect

materials, as well as strategic and managerial materials. In each case, it is important to identify the critical mechanism

to optimize the chain's profitability. For example, the firm must establish the supply of direct materials to ensure good

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coordination between the supplier and the buyer (Chopra & Meindl, 2008; Chung and Liao, 2009).

2. Materials and Methods Several procedures and models were analyzed in terms of supply systems and inventory management, where authors

such as Ballou, 1991; Schoroeder et al., 2011; Gómez and Acevedo, 2000; Fernández, 2006, Dyntar and Gros, 2006,

among others, present significant and important contributions that we will take into account for the development of

our research.

In Figure 1, a flow diagram is shown that describes the general procedure for inventory management and selected

procurement. Phase 1 consists of an analysis of the current situation of the inventory system. Phase 2 drafts the

development of the inventory management system and Phase 3 is the evaluation and result.

Figure 1. Flow diagram of the procedure for the design of the inventory management system (Sánchez, 2011).

Phase 1: Analysis of the current situation of the inventory system This stage considers inputs for production, transformation in the process of elaboration and the departure of the

different finished products. The aspects to be taken into account are shown in Table 1.

Table 1. Organization Characterization Sheet (Fernández, 2006).

Organization Characterization Sheet

Name:

Date Created:

Location:

Subordination:

Social object:

Mission:

Vision:

Main resources available:

• Structure

• Institutions

• Technology

• Human Resources

Classification according to different existing criteria:




Among the potential techniques to be used to comply with this first phase are: interviews, courses, lectures, surveys,

review of records and documents and direct observation.

Phase 2: Design of the Inventory management system The analysis of the portfolio and inputs is made through the ABC method or known as the principle of Pareto, where

establishes that there are few crucial articles and many trivial.

Forecasts. it allows to determine with some slack as the future demand will behave. For this study it is necessary to

know the classification of the models of forecasts and to verify which model is applicable, either qualitative or

quantitative (Fernandez, 2006).

Decision of which inventory model to use

Continuous (Q) review system. The parameters to be used and calculated are the following:

a) Amount to request (Q)

b) Security Inventory (SS)

c) Re-order point (RPP)

Periodic (P) review system. The parameters to be used and calculated are the following:

a) Revision frequency (P)

b) Security Inventory (SS)

c) Inventory objective (T)

d) Quantity to be requested at the time of design (Q)

Phase 3: Development and evaluation of the system ABC method

The dairy company under study presents a wide of portfolio product, analyzed the inputs needed for the manufacture

of the products, these products were classified by the ABC method according to their influence on sales. The data taken

were framed over a three-year period between January 2015 and December 2017.

In Table 2, a summary of the ABC classification is presented, for the classification (A) there is a total of 10 items, that

represent the 22.2% of all the total, they have the 78.12% of participation and generate a total cost of 98,707.10 USD.

In the classification (B), we have a total of 13 items, that represent 16.27% of all the total, have the 16.27% of

participation and generate a total cost of 20,557.66 USD. In the classification (C), we have a total of 22 items, that

represent 48.9% of all the total, have the 5.61% of participation and generate a total cost of 7,086.78 USD. In Figure

2, a Pareto diagram is shown that allows to have a better visualization of the obtained results.

Table 2. ABC Classification Summary.

Classification Items

Number % Items

%

Participation

% Accumulated

participation

Categorization

costs

0-80% A 10 22,2% 78,12% 78,12% 98707,10

80%-95% B 13 28,9% 16,27% 94,39% 20557,66

95%-100% C 22 48,9% 5,61% 100,00% 7086,78

Total 45 100,0% 100,00% 126351,54




Figure 2. Summary of ABC classification using the Pareto diagram.

For the development of the research we worked with the necessary inputs for the products of A and B Group, and

also that they have presented problems at the time of completing an order, like delays in the production. Management

reports that there are many delays and conflicts for not having the containers on time, generating delays in the delivery

of the final product. With these supplies for the packaging of yogurt we will carry out the supply management system.

Forecasts In order to forecast future production, we collect the historical information of the production of 3 years in terms of

raw material milk and yogurt production. Information processed through the FORECAST PRO TRAC 4.1 software.

In Figure 3 the total container is represented for the product of 2 liters, we can visualize the behavior of the demand

of the 3 years, its future forecast from the expert selection of the software the best model of exponential softening

method was chosen.

Figure 3. Forecasts of the demand of liters for the container of 2 liters knob-Expert selection (exponential softening)

In Table 3, forecast errors can be observed such as Bayesian Information Criterion (BIC), mean absolute percentage

error (MAPE), mean absolute deviation (MAD), quadratic mean square error (RMSE), when applying expert selection

to the Historical data of the yogurt sample for the 2-liter knob container.

Table 3. Sample determination for 2 liters container, expert selection (exponential softening method)

Sample determination

Sample size 36 Parameters Number 2

Average 2864.88 Standard deviation 1115.11

78.12%

16.27%5.61%

78.12%

94.39%100.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

10 13 22

% p

arti

cip

atio

n

Number of Items




R-squared Aj. 0.29 Durbin-Watson 1.64

Ljung-Box (17) 16.6 P= 0.52 Forecast Mistake 940.77

BIC 1028.25 MAPE 0.2207

RMSE 900.72 MAD 614.17

In Table 4, the forecasts generated by the software and the exponential softening model are observed when applied in

the yogurt sample for the 2-liter knob container. The analysis was performed on all input items to have the forecasts.

Table 4. Forecasting the demand for 2-liter yogurt knob-expert selection (exponential softening method)

Forecast data

Date (2017) 2,5 Inf Forecast Quarterly Annual 97,5 Sup

January 2609 5687 8764

February 3799 7047 10295

March 4667 8076 20810 11485

April 3990 7553 11117

May 3462 7173 10884

June 2807 6660 21387 10514

July 2723 6714 10704

August 3294 7417 11540

September 3654 7905 22036 12156

October 1845 6221 10597

November 2120 6617 11114

December 2023 6638 19477 83709 11254

Total 83709

Average 6976

Minimum 5687

Maximum 8076

Determining which inventory model to use To determine which inventory control system to use, the variability coefficient (CV) is calculated, where the variance

is calculated (standard deviation elevated to the square, 𝜎2) and the obtained result is divided for the average demand

elevated to the Square (�̅�2), this data is compared to the decision variables as specified in Table 5.

Table 5. Decision Variables (VC) (Vidal, 2010)

After calculating the variability coefficient, values lower than 0.2 was obtained, which should be used classical

techniques, choosing the EOQ inventory model, being the best model that fits, because the demand is known and

stable every month, in the Table 6, the results obtained are presented. The dairy company has a production system on

request and its elaboration process is not automated.

Table 6. Determining which inventory model to use

Si VC < 0.2 Use the EOQ method, P or Q

Si VC ≥ 0.2 Use Silver-Meal heuristic method

N.º Code Description

Coefficient of variability

σ^2

(Variance)

Average

demand

Variability

coefficient

(VC)

Inventory

model to use

1 LECH Milk 469362,47 155097965 0,00302623 EOQ Model

2 YORT Yogurt 710392,74 234653889 0,00302741 EOQ Model

3 YO4P Yogurt for 4 liters knob 6768,75 4568906,25 0,00148148 EOQ Model

4 YO4B Yogurt for 4 liters bucket 49059,972 14985931,4 0,00327374 EOQ Model




Design and implementation of the continuous Review System (Q) In Table 7, you have the result when applying the continuous review System (Q) of all the items predicted, which is

adjusted to a lapse of time of one month, for that reason, the wineries have capacity in terms of space for all yogurt

containers. It should be emphasized that the liters of yogurt are taken to containers, this way we know the total of

containers to be used in a month.

Table 7. Implementation of the Continuous Review System (Q)

Dairy product-Continuous review System (Q)

Ni Description Unit

Cost C Forecast

Variables

Total

Demand

(D)

Cost

Unit

C

Cost for

placing an

order (S)

Cost of

Inventory

maintenan

ce (i) (5%)

Lead

Time

(L)

(days)

Cost of

maintaining

inventory

(H)

Q*

Re-

Ordering

Point R

Total Monthly Cost

Orders

Number

Time

between

orders

(days)

Purchas

e cost

Order

cost

Cost to

maintain

inventory

Total

Cost

1

Yogurt for

4 liters pack

knob

0,34

600 600 0,34 3,00 5% 1 0,02 457 23 206,40 3,93 3,93 214,3 1,31 19,82

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

600 600 0,34 3,00 5% 1 0,02 460 23 204,00 3,91 3,91 211,8 1,3 19,94

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

600 600 0,34 3,00 5% 1 0,02 460 23 187,00 3,91 $

3,91 211,8 1,3 19,94

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

550 550 0,34 3,00 5% 1 0,02 441 21 187,00 3,74 3,74 194,5 1,25 20,83

The model displays a security inventory that is shown in Table 8. The system of continuous revision (Q) will allow to

lower the costs of orders per month, a better management of inventories, to respond to the orders of the clients daily

without delays of production.

Table 8. Security Inventory

5 YO2P Yogurt for 2 liters Knob 440622,56 48659925,4 0,00905514 EOQ Model

6 YO2B Yogurt for 2 liters bucket 127,16667 2277081 0,0000056 EOQ Model

7 YO1B Yogurt for 1 liter bucket 303,1875 912502,563 0,0003323 EOQ Model

8 YO1P Yogurt for 1 liter knob 0 40000 0 EOQ Model

9 YOMINI Yogurt for 175 cc Minis 0 171396 0 EOQ Model

10 YOOSO Yogurt for Bear Container 0 18496 0 EOQ Model

Dairy Product-Security inventory

N° Description Unit

Cost C

Forecasts From

liters to

containers

Variable

Security Inventory (SS)

Z (95%) σ 𝛔𝐿

Security

Inventory

(SS)

Re-order

Point R Month

1

Yogurt for 4

liter pack

0,34

USD

january-17 600 1,645 1,64 1,64 26 49

february-17 550 1,645 1,377 1,377 23 45




3. Result and Discussion With the application of the supply model for the dairy company, it was possible to detect the weaknesses that directly

affected the supply management in yogurt containers.

Having an adequate management of inventories, it was possible to reduce the production and machinery stoppages

generated by not having the yogurt containers in time.

The production time was reduced in 15 minutes, due to the quantity of containers needed at the appropriate time, as

well as having a security stock that guarantees the continuity of the productive flow. The costs of ordering and

inventory maintenance were reduced in one month. With the fulfillment of the orders to the clients, the demand of the

products was increased by 5% in a month strengthening the image of the company.

A summary is presented in Table 9, a comparison when applying the Q model and without applying.

Table 9. Data Comparison of the year 2016

Without applying the continuous review system (Q)

Application Number of

containers

Maintenance

costs (USD)

Orders

Number Cost per month

Total Cost

(USD)

Savings

per year

January

8 40

28 2219,52

28988,81

566,9

February 30 2773,4

March 27 2529,66

April 25 2424,78

May 26 2393,15

June 28 2334,95

July 24 2355,73

August 26 2409

September 25 2497,59

October 22 2379,76

November 25 2343,26

December 26 2328,01

Applying the continuous Review system (Q)

Application Number of

containers

Maintenance

costs

Orders

Number Cost per month Total Cost

January

8

33 11 2175,41

28421,91 February 33,81 11 2776,21

March 34,31 11 2480,5

knob

march-17 600 1,645 1,64 1,64 26 49

april-17 550 1,645 1,377 1,377 23 45

may-17 550 1,645 1,377 1,377 23 45

june-17 550 1,645 1,377 1,377 23 45

july-17 550 1,645 1,377 1,377 23 45

august-17 550 1,645 1,377 1,377 23 45

september-17 550 1,645 1,377 1,377 23 45

october-17 600 1,645 1,64 1,64 26 49

november-17 550 1,645 1,377 1,377 23 45

december-17 550 1,645 1,377 1,377 23 45




April 33,85 11 2393,19

May 33,62 11 2343,12

June 33,32 11 2273,77

July 33,36 11 2281,54

August 33,78 11 2376,36

September 34,09 11 2445,66

October 33,74 11 2331,08

November 33,32 11 2270,79

December 33,34 11 2274,28

System implementation feasibility analysis The implementation of the continuous Review System (Q) model to the dairy company is feasible, resulting in savings

of 566.9 dollars per year and reducing maintenance costs and packaging orders per month, as shown in Figure 6. The

proposal is profitable and easy to use for the store manager of the inputs for production. The system is implemented in

the Enterprise Excel software package.

The provisioning system allows guidance in terms of orders per month of yogurt packaging, thereby reducing supply

costs. The system has security stock and ordering reorder points, allowing to have all the containers on time and avoid

production delays. The implementation of the system of continuous revision (Q) allows to visualize the total expenses

in which they are incurred in a month, both in the present and the estimate in the future, where the administration can

carry out analysis and financial projections to not have Risks in delays or economic losses when purchasing

unnecessary inputs for production.

4. Conclusions The theoretical foundations were compiled from different sources such as books, scientific journals, scientific articles

both printed and electronic, which analyzed the information necessary to carry out the appropriate design of the

management model of supply. For the design of the supply management model of inputs, more detailed description of

the methodological tools used by different sources was made, allowing to be established in 3 Phases for the

development of the work and in this way evaluate all the elements that compose the inventory system. For its

calculation and the evaluation of the implementation to be carried out.

In the situational diagnosis of the dairy company, tools and methodologies were used, which allowed evidence of

weaknesses that directly affected the procurement management system. In the ABC classification of the portfolio

product and inputs of the organization, it was determined that items A have a share of 40.30% of sales and a total cost

of 50,921.76 USD, in items B there is a share of 30.39% of sales and an associated cost with 38,402.92 USD, items C

has a 29.30% share of sales value and a total associated cost of 126,351.54 USD. With the help of management, it was

possible to identify 8 types of yogurt containers that used to generate problems when making an order and delays to

production.

Through the FORECAST PRO TRAC 4.1 software, the information was processed, and the best forecasting models

were determined, by means of the expert selection and the Croston model. The forecasts obtained were for the

following 12 months, which takes into account the BIC error, which allows to see which model of forecasts is adjusted.

The forecasted data were of 8 items, where the calculation of the variability coefficient (CV) was obtained, which

allowed to determine the use of the continuous Review System (Q). The implementation of this model in the dairy

company is feasible and profitable, having a savings of 566.9 USD a year and a constant of 11 orders for yogurt

containers per month.

References

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of Econometrics and Business Statistics. Camberra: Scott Armstrong and Kesten Green.

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Biographies

Carlos A. Machado Orges is Industrial Engineer and Master an Industrial Engineering. Computer Science Specialist

and Assistant Professor of the Department of Industrial Engineering, Universidad de Holguín, Cuba. Researcher

Professor of the Industrial Engineering Career, at the Universidad Técnica del Norte, Ibarra, Ecuador.

Diego J. Haro Morales is Industrial Engineering at the Faculty of Engineering in Applied Sciences of the Universidad

Técnica del Norte, Ibarra, Ecuador.

Leandro L. Lorente Leyva is a Researcher Professor of the Industrial Engineering Career, at the Universidad Técnica

del Norte, Ibarra, Ecuador. Holds a Mechanical Engineering degree and a Master of Computer Aided Design and

Manufacturing (CAD/CAM) degree from Universidad de Holguín, in Cuba. He has published journal and conference

papers. Has participated in numerous projects and completed research in several areas. Specialist in computer-assisted

design, planning and manufacturing.

Yakcleem Montero Santos is a researcher-professor of the Industrial Engineering carrer at the Universidad Técnica

del Norte, Ibarra, Ecuador. He holds the title of Industrial Engineer and Master’s in Industrial Engineering: Production

Mention, degree from Universidad de Holguín, in Cuba. Specialist in Logistics and Organization and planning of

production.

Israel D. Herrera Granda is a Researcher Professor of the Industrial Engineering Career, at the Universidad Técnica

del Norte, Ibarra, Ecuador. Holds a Automotive Engineering degree and a Master in operations and logistics (MOL)

degree from Escuela Superior Politécnica del Litoral. He has published conference papers and chapters of regional


https://www.sciencedirect.com/science/journal/09255273/114/1

https://doi.org/10.1016/j.ijpe.2008.02.010

https://www.sciencedirect.com/science/journal/03772217

https://www.sciencedirect.com/science/journal/03772217/196/2



books. Has participated in numerous projects and completed research in several areas. Specialist in Operational

Research, Logistics, and Transport research.

Ivet Challenger Pérez is Computer Engineer and Master in Applied Mathematics and Computer for Management.

Instructor Professor of the Department of Informatic Engineering, Universidad de Holguín, Cuba.


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