demand forecasting approach for optimizing inventory

The Journal of Global Business Management Volume 15* Number 2 * October 2019 issue 65

Demand Forecasting Approach for Optimizing Inventory Volume in Warehouses

Dr. Rolando Pena-Sanchez, Associate Professor, Texas A&M International University, USA

Carmen Gabriela Gallegos-Cortez, Graduate Student, Texas A&M International University, USA

ABSTRACT

The main purpose of this project is to analyze the volume levels of inventory in warehouses to

identify the areas of opportunity present in the forecasting techniques for planning the demand for

maintenance, repair and operation materials in a business scheme B To B, so that this planning is

focused on the optimization of spaces through the correct identification of those materials or articles that

have a greater need within the competent market and represent a greater income in amount for the

business model of the host company.

Keywords: Demand, Forecasting Methods, Predictive and Prescriptive Analysis, Inventories, Linear Regression

INTRODUCTION

In this project, an analysis was carried out by family of materials within the model company,

differentiating those that had a higher percentage of participation in the total sale of the business, which

would consequently result in a greater opportunity area in terms of demand review. It was identified that

the articles belonging to the families of Hand Tools and Safety Tools are the ones with the highest

demand. The complete historical sales data of the year 2016 and part of 2015 were collected to

subsequently perform a sampling and statistical analysis by submitting them under the forecast methods

of Moving Averages and Exponential Smoothing, so that it was possible to establish in terms of monetary

amount and volume in cubic meters, which of these, it would be more convenient to manage inventory

availability based on demand histories to provide a more effective forecast for the company, finding a

balance between money, volume and greater availability of materials to improve the company's service

level. The main limitation of this project was to access to 2018 and 2019 inventory information due to the

host company restrictions.

We had been planning to investigate how the Safety Tools group presents a greater participation in

sales for the business; in turn, that the exponential smoothing method represented a higher percentage of

average assertiveness with respect to the historical sales of all the materials analyzed and a greater benefit

in monetary amount and volume of inventory to be managed in warehouses, generating an improvement

of Great relevance for the host company comparing the actual inventory levels that were held at the end

of January 2017, which was taken as a monitoring and forecast test sample in the mentioned methods.

Therefore, an economic benefit is tangible as the final result of the project carried out.

The following aspects are within the scope of this project:

(a) Current volume dimension in an MRO industry model warehouse both in cubic meters, and in

monetary value of stocks.

(b) Study of inventory design methods currently used to achieve a target percentage of assertiveness

against fluctuating demands in an annual range.

The Journal of Global Business Management Volume 15* Number 2 * October 2019 issue66

(c) Development of calculation algorithm for optimal order points of materials by category A, B, C.

(d) Financial proposal model, showing potential economic benefits for the company.

Combination of predictive and prescriptive analysis for decision making

High-impact decision making within companies includes a daily action, which must be duly based

theoretically, numerically and economically. For this, manufacturing, logistics and transportation

companies (Ballou, 2004), financial, food and retail services, among others, have chosen to rely on the

synthesis of the results from the predictive and prescriptive analysis, which provide the opportunity to

evaluate the different existing options according to the information or past history of behavior of demands

on the part of the final clients, that allow to mark a trend of predictive consumption for future events.

The results from the predictive analysis will be subsequently evaluated by prescriptive models,

which addresses the previously given definition. Once the prediction establishes what is likely to happen,

based on a statistical analysis of demand behavior patterns, the prescriptive part will provide the tools to

make the decision on what to do or how to proceed with this information using heuristics. . All of the

above includes a fundamental basis that provides added value to companies to make decisions proactively.

OBJECTIVE: The main objective of this study is to analyze the volume levels of inventory in

warehouses to identify the areas of opportunity present in the forecast methods for planning the demand

for maintenance, repair and operation materials that are predictable in a B To B business scheme.

The following describes a case study in which the application of said analysis model was carried

out.

Case: A European company in charge of the raw material supply chain(Lord, 2017), determined

that it required a redesign of its process to follow, starting from the way in which demand forecasting,

replenishment and supplier management methods were implemented and implemented. The solution

method was the rejection of high purchase volumes, the construction of new distribution centers or

warehouses and three different ERP purchasing tools that worked independently. The main purpose

would be to find a balance between the inventory requirement in the purchase orders that truly cover the

demand and a balanced cost between storage and inventory transport. The main objective would be to

obtain a more centralized supply chain (Payne, 2016, Ballou, 2004).

The company used the following methodology to solve problems Predictive analysis: Demand forecasts by SKU (Stock Keeping Unit, a scanable bar code printed on

product labels in a retail store, these label allows vendors to track the movement of inventory),

incorporation of temporary or extraordinary demands, demand peaks, promotional activities and other

events.

Prescriptive analysis: Software that related events with specific temporalities, objectives, forecasts,

costs and problems was implemented through various algorithms, so that it was possible to simulate

scenarios in which costs were minimized, always meeting the required levels of service (Dickhersbach,

2006).

Among the benefits obtained are listed:

(a) Inventory reduction, maintaining optimal levels of inventory availability.

(b) Daily monitoring of purchase orders for continuous improvement of the ERP system.

The previous case exemplifies the indispensable coordination of both aspects, identifying demand

peaks through statistical forecasting methods and applying optimization in the purchasing processes based


on this information; that is to say, both disciplines, the predictive and prescriptive analysis (Herschel and

Idoine, 2016) must go hand in hand for the correct solution of any problem of this nature, which will lead

to a considerable reduction of costs, finding a balance with the appropriate levels of volume in inventory

required to weigh the demand of a business.

Forecasting is a required tool for an inventory planning system (Payne, 2016). A forecast with a

high level of assertiveness (Armstrong, 1982) provides the necessary information for the correct decision

making towards any event. There are different methods that are adjusted according to the situations to be

evaluated, from extrapolation methods to observations and deductions carried out by experts in specific

areas. After an accurate forecast of the demand is made, an action plan that involves all the areas of a

company must be used to measure the possible economic impact it could have.

The first factor that influences demand is time; for what is called "temporary demand" that which

presents oscillations in short time ranges by multiple factors. This forces to establish estimates of both

space and time; that is, how much volume and how long these forecasted inventory levels will remain.

This type of demand, needs to be located to later dimension its effects within adequate storage, which in

turn implies resources in infrastructure, geographic location and transport.

Regular demand is one that reflects expected behavior patterns over time and can be broken down into

trend or stationary factors, which allows more assertive forecasts to be established. On the other hand,

irregular demand is one that is intermittent over time, reflecting variable volumes and wide range of

uncertainty, with disproportionate time series. There are other ramifications of the demand. The independent

demand is that which is coming directly from a group of clients towards a company of a certain turn; and the

derived demand is that which is generated on the parts required for a production process; this second is

much more complicated to predict, with high levels of bias; For this reason, the demand for parts required in

a production process for final parts is known as certainty, since it is planned how many final products are

required per order and therefore, the necessary production parts within the process.

There are different methods of demand forecasts, which are divided into: qualitative methods

(Maxwell, 1996), based on qualities or trends observed in the events studied and quantitative methods,

based on probabilistic sampling and statistical studies.

Within the dynamics of inventory level management, ROP (reorder point) or reorder point is

defined, to a fixed order quantity system in which the remaining inventory for an item is tracked each

time a product is made withdrawal of this one; In this way, it is established whether it is necessary or not,

to place a new order. The frequency of this review should be carried out according to the needs of the

company's business. As revisions are carried out, the definition of an inventory position or IP (Inventory

Position) will be necessary, which measures the ability of the item to meet a future demand, including

scheduled receipts or SR (scheduled receipts), which are orders that have already been made but have not

been received by the customer, plus the available inventory OH (On Hand), less backorders or BO

(Backorders).

Therefore, it is calculated as follows (Krajewski and Ritzman, 2000): IP = OH + SR – BO

When inventory levels reach the minimum established as a reorder point, a fixed amount must be

requested again to replenish inventory levels; This amount may be based on the EOQ (Economic Order

Quantity), which is defined as a price change amount or the minimum acceptable lot size to obtain a

quantity discount, container size or any other quantity selected by the management of a company

(Krajewski and Ritzman, 2000).


Selection of reorder points with uncertain demands: For the precise selection of reorder points in

the scenario of uncertain demands, it must be considered that both the demand and the delivery times are

not always predictable, so a large degree is handled of error in any forecast that can be made; therefore,

there is a need to have a safety inventory that can support periods in which 100% deliveries are not

available, as described below (Krajewski and Ritzman, 2000):

Reorder point: Average demand during the delivery time + Security inventory

Due to the uncertainty of the demand, the sales during the delivery time are unpredictable, for

which reason the safety inventory for the protection during temporary events prone to loss of sales should

be considered; In this way, the inventory is prevented from falling to a value of zero. The safety inventory

and reorder point are directly proportional, in this way the possible missing ones are foreseen. However,

maintaining high levels of safety inventory presents advantages and disadvantages, so it is necessary to

first define what is the level of service that customers want to provide, so that it is affordable at the same

time to keep stored materials up to the next purchase order arrives (Krajewski and Ritzman, 2000).

Calculation of the safety inventory: The calculation of the safety inventory for a service level

(Dickhersbach, 2006) above 50% implies that the reorder point must be greater than the average demand

during the delivery time. The formula is defined as follows (Krajewski and Ritzman, 2000):

Safety Stock = zσ

Where:

z = number of standard deviations from the average required to apply the service level cycle

σ = standard deviation of the demand in the probability distribution during the delivery time.

As the value of z is higher, the safety inventory will also be higher, such as the service level cycle.

If z = 0, there will be no safety inventory and they will be missing (Krajewski and Ritzman, 2000).

RESEARCH QUESTION

What are the inventory levels in warehouses for maintenance, repair and operation materials within

a company that runs a B to B business model?

DATA, METHODOLOGY AND RESULTS

Data collection

Bibliographic review: Texts related to the methodology of historical data collection will be

reviewed for a subsequent demand forecast elaboration, including both qualitative (Maxwell, 1996) and

quantitative methods. Good warehouse management practices will be included in the investigation, to

identify areas of greater opportunity for the reduction of excess inventory.

Review of records: In the investigation process, the various methods of collecting, analyzing and

interpreting data on the sale of materials from the MRO line will be studied, focused on determining a

demand forecast, whose purpose is to achieve a considerable percentage of assertiveness and decrease in

volumes and inventory storage costs.

Data analysis Content analysis: Terms related to statistical calculation methods for forecasts, algorithms for

determining reorder points, storage and inventory management and sales histories of a business model


that is governed under the business dynamics B to B will be identified for maintenance, repair and

operation turn materials.

Results

Company sales trends in the distribution of manufacturing, repair and operation materials (MRO).

Within the sales analysis carried out, a specific approach was carried out in two of the most popular

product families within the host company, due to its high participation in sales on a monthly basis.

The study includes an analysis of the sales behavior of the articles distributed by the host company

and that are within the families of Handy Tools and Safety Tools. Each item is within a family according

to the need to which it belongs. Each need is focused on satisfying a specific demand of a group of

customers, it can be in the area of cleaning, tools, electrical equipment, energy, precautionary materials

within an industrial plant, safety equipment for employees, among others.

The sales records analyzed include a period from August to December 2015, and from January to

December 2016, for this reason a separation of both periods is made in order not to bias observations.

Table 1: Percentage of participation of Hand Tools and Safety Tools categories in sales records Family of products 2015 2016 2015% 2016%

Hand Tools 2418 219100 43% 20% Safety Tools 3229 864921 57% 80%

Total 5647 1084021 100% 100%

Table 1 shows how specifically in 2016 there is a rebound in the distribution of articles

corresponding to the Safety Tools family; same higher trend of participation in the last 5 months of 2015.

Table 2: Annual sales history Hand Tools and Safety Tools on 2016 Month Sold Units Hand Tools & Safety Tools January 69638

February 84999 March 84418 April 82030 May 92962 June 94041 July 95341

August 99230 September 95708

October 97775 November 108274 December 79605

Table 3: Monthly sales division Hand and Safety Tools on 2016 Month Hand Tools Safety Tools January 12526 57112

February 17890 67109 March 15781 68637 April 16277 65753 May 20424 72538 June 18638 75403 July 20893 74448


August 18844 80386 September 21261 74447

October 19667 78108 November 20318 87956 December 16581 63024

Table 2 indicates a trend towards the increase in sales of items belonging to each group or family as

a whole, having a decline in the month of December, which may be subject to issues of fiscal year closing,

low demand and vendor closure.

Figure 1: Comparative history of annual sales on 2016 for Hand and Safety Tools

Table 3 and Figure 1 show a comparison of the sales behavior of each product category. To identify

the existing demand peaks, the average monthly sales of each need was obtained. In the case of Hand

Tools, its monthly sales average is 18,258 units; of Safety Tools, 72,076 units monthly average.

Table 4: Annual sales division Safety Tools 2016 Month 2016 Sold Units

January 57,112 February 67,109 March 68,637 April 65,753 May 72,538 June 75,403 July 74,448

August 80,386 September 74,447

October 78,108 November 87,956 December 63,024


Figure 2: Linear trend of monthly sales during 2016 of Safety Tools articles

Figure 2 shows the linear trend of sales for items belonging to the Safety Tools family, which

represents a higher percentage of monetary income for the company and its turnover. However, there is

no trend or correlation in historical sales data, so the demand turns out to be very erratic.

The methodology used to carry out a comparison of forecasting methods, includes the study of two

forecasting tools, moving averages method and exponential smoothing.

The purpose is to find a significant difference in the amount of inventory to be stored and total

volumetric by using both tools to forecast the necessary inventory to have available for a given demand

over a period of time.

The method of moving averages: A sampling of all items within the family of both Hand Tools and

Safety Tools was carried out, taking the history of demand for each of them throughout 2016; in this way,

the periods to average the forecast for the following consecutive month are obtained; in this case January

2017.

Results: Both the amount and the current volume of inventory level correspond to the quantities

obtained by the iterations carried out, so an important area of opportunity is reflected since these

conditions exceed the capacity levels of the centers of distribution conditioned for this type of materials.

The exponential smoothing method: Regarding the exponential smoothing method, the sales and

forecast history for the month of December 2016 was taken as a reference, to prepare a forecast

corresponding to the month of January 2017. The totality of articles comprising the families of Hand

Tools and Safety Tools.

Results: The results obtained were adjusted in different runs to variable alpha values, which through

each iteration generated a differentiated forecast, in order to find an optimal point in inventory value and

stored volume.

The value of α generally used is 0.5, which represents the average between distant or near historical

sales values evaluated.


Table 5: Cost / Volume Ratio method of moving averages (MA) and exponential smoothing (ES) for

Hand Tools and Safety Tools, α = 0.5. Cost MXN $ Volume m3 MA ES MA ES

Hand Tools $ 3,138,000.27 $ 3,358,901.86 53.92 63.92 Safety Tools $ 11,112,972.12 $ 16,757,683.99 422.80 675.06

Table 6: Cost / Volume ratio exponential smoothing for Safety Tools at α = 0.1 to 1. Alpha Cost MXN $ Volume m3

0.1 $ 21,545,232 926.0536 0.2 $ 20,348,345 863.3043 0.3 $ 19,151,458 800.5549 0.4 $ 17,954,571 737.8056 0.5 $ 16,757,684 675.0562 0.6 $ 15,560,797 612.3069 0.7 $ 14,363,910 549.5575 0.8 $ 13,167,023 486.8082 0.9 $ 11,970,136 424.0589 1 $ 10,773,249 361.3095

Table 6 shows an inversely proportional trend according to the value of α; As the value of this

variable increases, both cost and storage volume decrease, so it is advisable to use a value of α as close as

possible to 1 when running the iterations for calculating forecasts. This same relationship is observed in

the forecast trend for both Safety Tools and Hand Tools in Table 7.

Compared to the amounts in monetary value and inventory volume, alpha values are defined as

optimal, for Safety Tools at α = 1 and Hand Tools at α = 0.7, against the values obtained after iterations

under the moving averages method, which at these same inventory levels, it would be more expensive.

Table 7: Cost / Volume ratio exponential smoothing for Hand Tools at α = 0.1 to 1. Alpha Cost MXN $ Volume m3

0.1 $ 4,185,176.80 78.83402 0.2 $ 3,978,608.07 75.1061 0.3 $ 3,772,039.33 71.37817 0.4 $ 3,565,470.60 67.65025 0.5 $ 3,358,901.86 63.92232 0.6 $ 3,152,333.13 60.1944 0.7 $ 2,945,764.39 56.46648 0.8 $ 2,739,195.66 52.73855 0.9 $ 2,532,626.92 49.01063 1 $ 2,326,058.19 45.2827

SEASONAL DEMAND

Within the ordinary sales behavior, demand peaks can be presented that are defined as a temporary

or extraordinary demand.

Within the language of the host company, these articles are defined as Seasonal, of which a

different demand behavior is observed. Within the mixture of items belonging to the product families of

Safety Tools and Hand Tools, the following three were identified with higher percentages of participation


for sale under the label of "Seasonal", which are those items whose sale within a temporary It is above

50% of your ordinary demand:

a) SKU: 4LJM6 with 42% participation in Seasonal sale, b) SKU: 4LJM1 with 23% participation in

Seasonal sale, c) SKU: 4LJM7 with 20% participation in Seasonal sale.

These articles are in the “Summer” season, which lasts from April to August. Below is an analysis

of the behavior of these type of sale:

Table 8: Example of seasonal SKU 4LJM6 annual sales behavior Month Sales in units January 29

February 23 March 79 April 35 May 159 June 70 July 153

August 103 September 54

October 34 November 112 December 12

Total WO Season 343 Total withSeason 863

Figure 3: SKU 4LJM6 annual sales behavior trend

Assertiveness of forecast

The assertiveness of the forecast is a very important factor that determines how much is the level of

accuracy of a demand forecast made against the actual recorded sale of an item.


The assertiveness levels of the forecast carried out under the exponential smoothing method for the

Hand Tools and Safety Tools article families were defined with an alpha value of α = 0.7 and α = 1,

respectively.

Below is a breakdown of the amounts in value in pesos and inventory volume, and percentage of

forecast assertiveness obtained against actual sales data for January 2017.

Table 9: Cost-Volume Ratio m3-Assertiveness of Forecast for Hand Tools and Safety Cost Voulume Forecast Accuracy MA ES MA ES MA ES

Hand Tools $ 3,138,000.27 $ 2,945,764.39 53.92 56.47 51% 43% Safety Tools $ 11,112,972.12 $ 10,773,248.95 422.80 361.31 42% 45%

Tools with forecast methods of moving averages (MA) and exponential smoothing (ES).

Table9 shows the results obtained after the comparison and selection of the most appropriate

forecast method to use. An evaluation was made based on how much inventory would be stored in

amount in pesos and volume in cubic meters. The exponential smoothing method (ES) was selected

according to an alpha variable value suitable for each family of items. Being the assertiveness of the

forecast a comparative that is in the same ranges of 42% to 51% between both methods, the amount in

inventory weights that will be taken to store is taken into account as the main factor, the lowest amounts

being those corresponding to the exponential smoothing forecasting method.

Evaluation of stored inventory levels: The evaluation of inventory levels is necessary to correctly estimate whether a new forecasting

method is viable to be applied or not. The iterations made taking the sales history of the year 2016,

allowed us to make an estimate or forecast of how the demand would behave within the period of January

2017. From the information collected after the period of January 2017, a photograph of the levels of final

inventory obtained for the articles of the families of Hand Tools and Safety Tools at the end of the month,

so that it is possible to carry out an adequate comparison of what would have been the levels of inventory

covered by the exponential smoothing forecasting method (Armstrong, 1982) against the sale resulting

from said period.

A comparative analysis between the status of the exponential smoothing forecast method and

inventory levels at the end of January 2017, are presented below:

Table 10: Comparison of inventory levels of Safety Tools (Cost / Volume)

at the end of January 2017 and forecast by exponential smoothing. Real Cost OH mxn $ Cost OH ES Real volume m3 OH Volume OH m3 ES

$ 90,284,528.16 $ 10,773,284.95 3357.58 361.3

Table 11: Comparison of levels of inventory of Hand Tools (Cost / Volume)

at the end of January 2017 and forecast by exponential smoothing Real Cost OH mxn $ Cost OH ES Real volume m3 OH Volume OH m3 ES

$ 17,654,684.38 $ 2,945,764.39 229.29 56.46

Tables 10 and 11 show an important difference for both families of items in terms of reducing

inventory levels in cost and in stored volume. In the case of Safety Tools, a decrease in inventory cost of


88% and 89% in volume was obtained, under the application of exponential smoothing as a forecast

model. For the Hand Tools family of articles, a cost reduction of 83% and a volume of 75% was obtained.

LIMITATIONS

The following aspects are beyond the scope of this project: (a) Demand histories prior to 2015 not available.

(b) No company’s permission for comparing information with 2018 or 2019 inventory closure, only for

2017 closure data.

(c) Application of new proposed reorder point model for the current year (2019).

(d) Application of new forecast models in the system in 2019.

(e) All amounts in this report are reported in foreign currency (Mexican Pesos).

CONCLUSIONS

Based on the series of analyzes performed, it is concluded that the exponential smoothing method,

using a value of an alpha α = 0.7, for the demand behavior of items belonging to the Hand Tools family

and α = 1 for the item family Safety Tools, are the best to use; so, it is more advisable to have greater

control of inventory volume (Perdomo, 2000, 2001) at lower levels in cost, with moderate storage

volumes, covering ordinary demands effectively. Regarding extraordinary demands or seasonal, a

separate monitoring or alternate processes must be carried out that allow cleaning the sales history to

reduce biases and increase assertiveness percentages in the forecasts issued in the future.

In other words, from the study conducted on the analysis of sales history of the families of Safety

Tools and Hand Tools articles of the host company, it is concluded that the optimal forecasting method

(Armstrong, 1982)for the saving in monetary value of inventory and the reduction of levels of stored

volume, is the exponential smoothing method, applying and alpha value α = 0.7 for Hand Tools and α = 1

for Safety Tools, finding similar levels in terms of assertiveness in forecast compared to the method of

moving averages that of the same way was evaluated under the same scenarios in both cases.

On average, a percentage reduction of inventory levels in the amount of 88.5% and a volume of

79% in total was obtained for Safety Tools and Hand Tools.

One of the main areas of opportunity and greater monitoring within the host company, is the

reduction of physical levels of inventories in storage, this having a total capacity of 30,000 cubic meters,

so currently and after the evaluation of real levels of inventory (Perdomo, 2001) at the end of January

2017 and the levels that could have been obtained after the use of the exponential smoothing method to

forecast the sales of the family of items evaluated, the percentage of participation of Safety and Hand

Tools would have been reduced considerably, to Safety Tools having a difference in volume of 2,996

cubic meters and Hand Tools of 172 cubic meters.

REFERENCES

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States of America

Ballou, R. (2004). Logística: Administración de la cadena de suministro. Ed. Pearson. Edo. De México, México.

Dickhersbach, J.T. (2006). Service Parts Planning with SAP SCM. Ed. Springer. New York, United States of America.


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https://www.gartner.com/document/3527018?ref=solrAll&refval=186280846&qid=810cf86847ed6cd5bfccdb9473771188

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demand forecasting approach for optimizing inventory

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