navee ijlsm update

8/13/2019 Navee IJLSM Update

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Simulation of Retail Supply Chain Behaviour and Financial Impact in an Uncertain

Environment

K. Cha-ume and N. Chiadamrong*School of Manufacturing Systems and Mechanical Engineering

Sirindhorn International Institute of TechnologyThammasat University, Pathumthani,Thailand, 12121

e-mail:[email protected]*Corresponding author

Abstract: Traditionally, attention has been focused on uncertainty in customer demand.However, uncertainty is also inherent in the market on the supply side, because the quantityand quality of raw materials delivered from external suppliers may differ from thoserequested. This paper quantifies the financial impact of having uncertainty in a retail supply

chain. Having unstable inventory records leads to profit losses in a supply chain. Theseinventory records may not be correct due to various causes such as transaction errors,misplacement, etc. These inaccuracies are caused by the uncertainties in customer demand,

parts supply, and variations in the process itself. The aim of this paper is to examine therelationship between inventory inaccuracy and financial performance. The results indicatethat each type of uncertainty can have different impacts on the chain, and the elimination ofuncertainty can more or less reduce supply chain costs and hence increase profit.

Keywords: Uncertainty, Financial impact, Retail supply chain, Information inaccuracy,Simulation

Reference to this paper should be made as follows: Cha-ume, K. and Chiadamrong, N.(201x) Simulation of Retail Supply Chain Behaviour and Financial Impact in an UncertainEnvironment,Int. J. Logistics Systems and Management,Vol. x, No. x, pp. xx-xx.

Biographical notes: Navee Chiadamrong is an associate professor at the School ofManufacturing Systems and Mechanical Engineering, Sirindhorn International Institute ofTechnology, Thammasat University, Thailand where he teaches and researches in the area of

production planning and control methods and supply chain management. He received hisMsc. Degree in Engineering Business Management from Warwick University and PhD inManufacturing Engineering and Operations Management from University of Nottingham,

UK. Some of his recent articles have appeared in International Journal of ProductionEconomics, Computer and Industrial Engineering, International Journal of ManufacturingTechnology and Management, European Journal of Industrial Engineering and TQM &Business Excellence.

Kamolwon Cha-ume received her engineering degree in Industrial Engineering fromSirindhorn International Institute of Technology, Thammasat University, Thailand and herMsc. Degree in Supply Chain and Logistics Management from Warwick University, UK. Sheis currently PhD student at the Sirindhorn International Institute of Technology, ThammasatUniversity. Her main research interests include collaboration and information sharing insupply chains and lateral transhipment problems.
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

In the modern business world, considerable emphasis has been placed on Supply ChainManagement (SCM). A supply chain is generally viewed as a network of facilities linked orcoordinated by the flow of goods and services from suppliers through production to end

customers with an information flow through the network (Shahabuddin, 2011). Threesubsystems are recognizable in the supply chain: procurement, production, and distributionsubsystems (Petrovic et al., 1998). They are interrelated in a way that decisions made at oneof the subsystems affect performance of the supply chain as a whole. In recent time, manycompanies focus only on improving their own supply chians performance to maximize their

customer benefit without emphasizing on benefits to others players in the supply chain. Onlyif companies start focusing on the entire supply chain, can they realize maximum efficiencyof a whole (Azadeh et al., 2011). Shahabuddin (2011) has stated that the purpose of thesupply chain is to create value for customers by requiring all participants in the supply chainto work together in order to reduce the amount of inventory and facilitate a smooth flow ofgoods from the source to the end customer. As a result of this coordination, all the

participants will be benefited.

Currently many companies are faced with an increasing in risk exposure. This is mainlycaused by a greater dependence between supply chain partners. (Kersten et al., 2011). To becompetitive, the improvements in logistics and supply chain management are essential for all

businesses (Diaz et al., 2011). One of the reasons why supply chain management is such acritical area of business is because there are many risks and uncertainties that can pose aserious threat to the integrity of the business. The collapse or inability to function of thesupply chain can lead to disastrous results, and it is best that this is averted by any means

possible (Merschmann, 2010). Therefore, in order to succeed in supply chain management,the understanding and assessment of these risks and uncertainties are the fundamental steps

(Ganguly et al., 2011).

2. Literature Review

A great deal of research has been done in the area of supply chain dynamics. A widely usedapproach to study supply chain dynamics has been based on the system dynamicsmethodology (Forrester, 1961; Towill, 1991; Sterman, 2000; Sodhi, 2001). This dynamicmodel is a deterministic mathematical description of the supply chain, which is then used tosimulate supply chain dynamic behaviour. Another approach used to study supply chaindynamics has been based on modelling a supply chain as a discrete event system (Banks et

al., 2002). Results of interesting research have been reported in Southall et al. (1988) inwhich their model has included uncertainty in customer demand and factory lead time, whichfollowed specified probability distributions. Effects of a step wise change in customerdemand were simulated.

In a supply chain, there are many aspects and operations that are susceptible to uncertainty.Supply chain uncertainty can be defined as factors that cannot be controlled or eliminated inthe operation of a supply chain. This uncertainty cannot be measured or forecasted withaccuracy but can yield positive results. However, due to globalization of customers,companies need to be flexible and more adaptive enough to satisfy more demandingcustomers as well as provide a high service level for customers (Swafford et al., 2004). The

concept of uncertainty has been defined in various ways by several studies (Duncan, 1972).Decision theorists define uncertainty as the situation where the probability of the outcome of


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an event is unknown, as opposed to a risk situation where each outcome has a calculatedprobability (Luce and Raiffa, 1957). Lawrence and Lorsch (1967) and Duncan (1972) vieweduncertainty as consisting of three components: (1) a lack of information regarding theenvironmental factors associated with a given decision-making situation; (2) not knowing theoutcome of a specific decision in terms of how much the organization would lose if the

decision were incorrect; and (3) inability to assign any degree of confidence, as to howenvironmental factors are going to affect the success or failure of a decision unit in

performing its function.

Applying these definitions to the contexts of a supply chain, the uncertainty occurs wheninformation becomes unreliable for effective decision-making or prediction of the outcome ofa decision. This uncertainty also occurs when there are unpredictable changes in the system.Uncertainties in parameters in supply chain management and control problems have beentreated as stochastic processes and described by probability distributions. A probabilitydistribution is usually derived from evidence recorded in the past. This requires a validhypothesis that evidence collected is complete and unbiased, and that the stochastic

mechanism generating the recorded data continues in force on an unchanged basis. However,there are situations where not all these requirements are satisfied, and therefore, theconventional probabilistic reasoning methods are not appropriate. For example, there may bea lack of evidence available or lack of confidence in evidence, or simply evidence may notexist, as in the case of launching a new product. In these situations, uncertainties in

parameters can be specified based on managerial experience and subjective judgement.

It has long been theorized that there is a relationship between variability and performance(Bhatnagar and Chandra, 1994). Deming (1986) went so far as to propose that the key aim ofmanagement is to control variability in order to ultimately lower costs. Moreover, Dellino etal. (2010) suggested that an effective way to optimize simulated systems involves usingTaguchi's worldview to separate decision variables that are to be optimized, and uncertainenvironmental variables that affect the optimum. The general operations management theoryof swift, even low argues that reduced variance in materials flow and operational activities

contribute to improved performance (Schmenner and Swink, 1998). The more consistent theflow of materials, the more productive processes should be, and this implies improvedfinancial performance (Germain et al., 2001). The importance of financial decision in supplychain management has been highlighted by many researchers (Shapiro, 2004; Hammami etal., 2008; Papageorgiou, 2009) especially as a crucial part of global supply chain, beingsignificant to its configuration (Melo et al., 2009).

Treating uncertainty is an important issue in supply chain modelling and analysis of supplychain behaviour and performance (Petrovic, 2001). According to Van der Vorst and Beulens(2002), supply chain uncertainty refers to a decision making situation in the supply chain inwhich the decision maker does not know definitely what to decide, as he is unclear about theobjectives: lack of information about its environment or the supply chain, lack information

processing capacity, is unable to accurately predict the impact of possible control actions onsupply chain behavior, or lacks effective control actions.

Different sources of uncertainty in a supply chain can exist, encompassing suppliers,production/manufacturing processes and customers. Davis (1993) also identified threedistinct sources of uncertainty in supply chains: supply uncertainty, process uncertainty, and

demand uncertainty. Supply uncertainty is caused by the variability of supplier performancedue to late or defective deliveries. Process uncertainty results from the unreliability of the


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production process due to machine breakdowns. Finally, demand uncertainty, whichaccording to Davis (1993) is the most serious of the three, arises from volatile demand orinaccurate forecasts. In fact, these uncertainties may be different in nature, caused by randomevents, imprecision in judgment, lack of evidence available, or lack of certainty in evidence.

However, speaking of different types of demand uncertainty, the double probabilistic settingshould be taken into account. De Rocquigny et al. (2008) has proposed that there are twocomponents of the double probabilistic setting: epistemic and aleatory uncertainty. Theepistemic uncertainty refers to sampling the mean of uncertainty, but the aleatory uncertaintyis instead sampling the demand for an appropriate mean. Helton (2009) proposed that thealeatory uncertainty arises from an inherent randomness in the properties or behaviour of thesystem under study while the epistemic uncertainty derives from a lack of knowledge aboutthe appropriate value to use for a quantity that is assumed to have a fixed value in the contextof a particular analysis.

Supply chain inventory management decisions also depend on inventory data gathered from

automated or manual control systems. As a result of advances in information technologies,companies started to automate their inventory management processes and use inventorymanagement software (Lee and Ozer, 2005). Although the use of Information Technology(IT) has made collecting and storing data about the flow of items through a supply chaineasier and less expensive, the tracking of inventory remains prone to error. The data collectedmay not be accurate due to various reasons: incorrect product identification, transactionerrors, inaccessibility of items due to improper usage of the depot, misplacement of items,shrinkage, etc. These may result in two problems: unplanned inventory depletion, andaddition. If the inventory records do not agree with the actual physical stock, either an ordermay not be placed in time or excessive inventory is held. Kang and Gershwin (2005) reportedinventory accuracies of a global retailers stores. It is seen that the inventory accuracy is only

51% on average for 500 stores. In other words, the stores have accurate records for onlyabout a half of the SKUs (Stock Keeping Units). The best performing store in the studyknows its actual inventory with only 75-80% accuracy. Raman et al. (2001) reported similarfindings for a leading retailer. Almost 370,000 SKUs were investigated for the retailer. It wasconcluded that more than 65% of the inventory records do not match with the physicalinventory.

3. Simulation Optimization

In this study, the simulation and optimization procedure are completed by using ARENAcommercial software and the OptQuest tool. Like all practical simulation optimizationmethods, the OptQuest is also an iterative heuristic (Kleijnen, 2008). It can be used to utilizea combination of three meta-heuristics: Scatter Search (SS), Tabu Search (TS), and Neural

Networks (NN) (Glover et al., 1999; Keskin 2010). An example of successful application ofscatter search within OptQuest is reported by Bulut (2001) to solve a multiscenariooptimization problem based on a large-scale linear programming model.

The OptQuest package can also be used to combine with other simulation software systems(Kleijnen, 2008). It requires the specifications of lower and upper values for the inputvariables that are to be optimized. Moreover, it requires the selection of the (random)simulation output that is the goal or objective variable to be maximized. In our problem, weselect the maximization of the whole chains profit as an objective. The OptQuest also allowsthe user to explicitly define integer and linear constraints on the deterministic simulationinputs. In joining a chain, it should be guaranteed that all members are better off by creating a


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win-win situation for all members. As a result, the non-negativity constraints to guaranteethat all members must at least gain profit (profit of each member 0) were also set in thestudy.

The interest in simulation optimization is to find which of a large number of sets of model

specifications have led to the optimal output performance (Vaghefi et al., 2009). Foranalyzing complex systems, such as our case, the objective functions are not expressed asexplicit functions of the input parameters; rather they normally involve some performancemeasures of the system whose accurate values can be found only by running the simulationmodel. Simulation optimization is defined as the process of finding the values for the inputvariables, such that an expected system performance from stochastic simulation is optimized(Kleijnen, 1987, 2008; Swisher et al., 2000; Fu, 2002; Law 2007).

4. Basic Model Assumptions

An illustrative case of having uncertainty in a retail supply chain, consisting of three retailersand a manufacturer, is studied. This is to gain a better understanding of the retail supply chaindynamic behaviour and performance in the presence of various sources and types ofuncertainty. Assumptions concerning the system operations are as follows:

- Retailers are differentiated by different standard deviations of the customers inter-arrivaltimes under the same value of mean (2 hours). A normal distribution with a mean of 2 hoursand 25%, 50%, and 75% of its mean set to be the standard deviation is used for Retailer 1,Retailer 2, and Retailer 3, respectively. This is considered to be the aleatory uncertainty,which is inherent randomness in the retailersdemand.

- The inventory in each retail shop is controlled based on a periodic review policy. Themaximum target stock level of each retailer (TSL) is set up periodically to a certain level,

which is optimized by the OptQuest.

- End customer demand is fulfilled from the shops inventory. When demand exceeds theavailable stock, unmet demand is considered to be shortages, and penalty costs are incurred.

- The manufacturer is assumed to know the retailers demand in the base case for eight weeksin advance, in which the manufacturer uses this information to update its ordering and

production plan for the finished products using the Wagner-Within algorithm with an 8weeks rolling planning horizon. The plan will be updated each time before producing a new

batch of production. A built-in Visual Basic sub-model was written and interfaced with themain ARENA simulation model to generate the rolling production plans of the manufacturer.

At this point, the uncertainty in customer demand has no effect on changing the pattern of thecalculated plans.

- Replenishment quantities for each inventory are received within a given, planned lead time.The lead time includes the time necessary for order processing, production, andtransportation.

5. System Configuration

Figure 1 shows the scope of the simulated retail supply chain. The whole chain consists ofthree retailers and one manufacturer, whereas the supplier of the manufacturer is not includedin the study. Three retailers place orders to the manufacturer at the end of each week, to refill

their stock up to individual maximum stock keeping levels or target stock level (TSL). After aconstant delivery lead time of 2 days, the ordered units will arrive at the retailers. At this


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point, we are setting different levels of variation in demand, supply, and process to study theireffects that may occur in the supply chain system.

Figure 1: Overview of the studied retail supply chain

5.1. Epistemic Uncertainty in the system

There are three types of epistemic uncertainty in the system which are: demand, supply, andprocess uncertainties. The demand uncertainty is represented by the uncertainty of the rate ofcustomer arrival at the shop. This represents inaccuracy caused by the accuracy of demandforecasting. The supply uncertainty is represented by the uncertainty of the product delivery

lead time. This inaccuracy is caused by an uncertainty in logistics operations. Finally, theprocess uncertainty is represented by the uncertainty of the delivering product quantity to theshops which is called a missed quantity. This missed quantity may be caused by the lack ofcoordination among a supply chains members. It encompasses the inconsistency in productflows into, and out of, the shop, as well as internal variability, such as misplaced items oreven theft during the delivery.

In all three types of uncertainty, there are two levels of effects, low and high, that will beinvestigated. At the low level, the mean can be varied within a range of 25% (swinging on

both sides with an equal chance from the base cases mean), and in the high level, the meancan vary in the range of 50%. For example, in the case of a low level of the demand

uncertainty, the variation on the mean is introduced by the expression of the uniform (2*0.75,2*1.25) = uniform (1.5, 2.5) as can be seen from Figure 2. At the low level of demanduncertainty for Retailer 1, the mean inter-arrival time of customers will follow the uniformdistribution varying from 1.5 to 2.5. As the variation of the customers demand follows thenormal distribution, the inter-arrival times of the customers at Retailer 1, Retailer 2, andRetailer 3 are normally distributed with the mean stated above and 25%, 50%, and 75% of themean being set for its standard deviation.

CustomerDemand

In

ter-arrivaltimeofcustomers


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Figure 2: Epistemic uncertainties in demand, supply and process

5.2. Objective Function and Financial Performance Measures

In this section, the objective function, financial performance measures and their notations willbe introduced.

Notations

S m = Selling price of manufacturer ($/unit)S r = Selling price of retailers ($/unit)Om = Ordering cost of manufacturer ($/order)Or = Ordering cost of retailers ($/order)Cm = Production cost of manufacturer ($/unit)C

r = Operating cost of retailers ($/unit per unit time)

ns(m) = Sales volume of manufacturer (units)


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ns() = Sales volume of retailer i(units)rm = Number of orders of manufacturer (orders)

= Number of orders of retailer i (orders)

RMm = Raw material cost of manufacturer ($/unit)RMr = Raw material cost of retailers ($/unit)

hm = Holding cost of parts per unit time at manufacturer ($/unit per unit time)hr = Holding cost of parts per unit time at retailers ($/unit per unit time)

Pm = Penalty cost of manufacturer ($/unit)Pr = Penalty cost of retailers ($/unit)nRM(m) = Number of raw materials purchased by manufacturer (units)

= Number of raw materials purchased by retailer i(units)

tm = Average holding time of parts at manufacturer (unit time)

= Average holding time of parts at retailer i(unit time)

= Operating time duration of retailer i(unit time)

n(m)miss = Number of lost sales at manufacturer (units)

n ()miss = Number of lost sales at retailer i(units)ai = Average number of units in the shop of retailer i(units)

5.2.1. Objective Function and Constraints

Maximize Profit of the whole chain = Manufacturer profit + (Retailer 1sprofit + Retailer2s profit + Retailer 3s profit)

where Manufacturer Profit= SalesTotal Costs= Sales (RM Cost + Ordering Cost + Production

Cost + Holding Cost + Transportation Cost +Penalty Cost)

whereSales is calculated from a selling price per unit (Sm) multiplied with the sales volume(ns(m)).

Smx ns(m) (1).

RM Cost is calculated from a raw material cost per unit (RMm) multiplied with the number ofpurchased raw material(nRM(m)).

RMmx nRM(m) (2).Ordering Cost is the cost incurred when the manufacturer places an order for raw materials.It is calculated from the ordering cost (Om) multiplied with the total number of orders (rm)made by the manufacturer. In some instances, the manufacturer may take responsibility todeliver the ordered product itself and the transportation is also charged per order. So, thetransportation cost is included in the ordering cost in this study.

Omx rm (3).Production Cost is calculated from a conversion cost per unit (Cm) multiplied with thenumber of purchased raw material(nRM(m)).

Cmx nRM(m) (4).


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3

1i

3

1i

3

1i

3

1i

3

1i

3

1i

3

1i

Penalty Costis the cost incurred when the retailer fails to satisfy the demand. It is calculatedfrom the penalty cost per unit (Pr) multiplied with the number of lost sales at each retailer(n ()miss).

Pr n ()miss (13).

Hence, Retailer Profit is calculated by:

Sr ns()- (RMr

+ r + hr

x t+ Cr

ai +Pr n ()miss) (14).

Non-negativity constraints:

Manufacturers profit 0Retailer 1s profit 0Retailer 2s profit 0

Retailer 3s profit 0

5.3 Decision Variables

There are three decision variables in the model, which are the target stock levels at Retailer 1,Retailer 2 and Retailer 3. These variables will be searched for their optimal setting by theOptQuest. The lower and upper bounds of the searching boundary in each variable areguaranteed to be large enough to ensure that the optimal setting falls inside the boundary.

5.4 Parameter Values (costs, prices, etc.)

The following cost structure is used throughout our experiment. It is in line with the coststructure of day to day convenience goods sold in general convenience stores in practice.Even though, this cost structure can affect the findings, the sensitivity analysis of some cost

parameters, as they are usually not reported, is another area where further study is needed. Infact, a spread sheet file in MS Excel was built in this study as a means to input the coststructure. When a new cost structure is proposed, a new finding can easily be presented.

Manufacturer Selling price = $20/unit RM cost = $5/unit Holding cost = $0.16/unit per week (40% of selling price per year) Penalty cost = $20/unit Production cost = $5/unit Ordering cost = $ 250/order

Retailer Selling price = $50/unit Holding cost = $0.40/unit per week (40% of selling price per year) Penalty cost = $50/unit Operating cost = $2/unit per week Ordering cost = $250/order


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5.5. Steady-state Conditions

Ten replications were simulated with 360 days per replication after an initial warm-up periodof 180 days. Based on 10 replications, a 95% confidence interval for the throughput has awidth less than 1.5% of its mean. The warm-up run of 180 days proved sufficient to generate

stable estimates of the steady-state results.

6. Results

Table 1 shows the list of abbreviations of the models under the study. The Tukey comparisontest is used to compare the means of cost among the interested models, that is, it appliessimultaneously to the set of all pairwise comparisons and identifies where the difference

between two means is greater than the standard error would be expected to allow. The resultscan be presented as follows:

Table 1: Model abbreviations

Model Meaning

LD Model with LOWlevel of DEMANDuncertainty

HD Model with HIGHlevel of DEMANDuncertainty

LL Model with LOWlevel of LEAD TIMEuncertainty

HL Model with HIGHlevel of LEAD TIMEuncertainty

LM Model with LOWlevel of MISSED QUANTITYuncertainty

HM Model with HIGHlevel of MISSED QUANTITYuncertainty

6.1. Effect of Aleatory Uncertainty

The retailers are differentiated by the different variations of the customers inter-arrival timewhile keeping the same mean (2 hours) at each retailer. In summary, the aleatory uncertaintywas presented by setting the distribution of the customer inter-arrival time of Retailer 1,Retailer 2 and Retailer 3 following the normal distribution with the mean equal to 2 minuteswhile the standard deviation is 0.5, 1, and 1.5 minutes, respectively.

After, optimizing the base model without epistemic uncertainty by the OptQuest, the result ofthe optimized target stock levels are shown in Table 2 where Retailer 3, who has the largestdemand variation, shows to have the highest target stock level as expected. Please note thatthe base case is referred to the case without epistemic uncertainty, but there is aleatoryuncertainty from setting different inherent randomness in Retailer 1, Retailer 2, and Retailer3.

Table 2: Optimized Target Stock Level from OptQuest

Retailer 1 (units) Retailer 2 (units) Retailer 3 (units)

Base Case 114 117 120


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Table 3 shows financial performance measures of the retailers under the base case. Thepresented numbers are the averaged costs per replication (from 10 replications). It was foundthat Retailer 3, who has the largest demand variation, has the worst financial performance.The reason for the lower profitability of Retailer 3 in relation to Retailer 1 and Retailer 2 isdue to a reduction of revenue as well as an increase in operating, holding and penalty costs. It

can be seen that the variation in demand has an inverse effect on the profitability. Thisbasically stemmed from the normal distribution created demand (inter-arrival time) ofcustomers of Retailer 3, which has the largest variance as compared to those of Retailer 1 andRetailer 2.

Table 3: Financial performance measures of the retailers under the base case

6.2. Effect of Epistemic Uncertainties

The profits of all members and the whole chain were investigated to find which epistemicuncertainty may cause a greater impact on the profits. The obtained optimized target stocklevels of each scenario from the OptQuest can be seen in Table 4.

Table 4: Optimized Target Stock Levels of each scenario from OptQuest

It was found that when there is demand and supply uncertainty, the retailers have tried toprotect the effect of uncertainty by keeping higher inventories through setting a higher targetstock level, as compared to the base case. But a lower target stock level (keeping lessinventories) was suggested when there is process uncertainty. This is since there is a chancethat extra stock could be delivered in the future, and those exceeding inventories may not besold.

In addition, the trend of optimal target stock level of the base case and the models with a lowlevel of uncertainty in demand, supply, and process keeps increasing as the variation of thecustomer demand of Retailer 1, Retailer 2, and Retailer 3 increases. This is to prevent

possible lost sales when there is more uncertainty in the system.

Retailer (i) Revenue ($) Profit ($) Holding cost ($)Operating cost

($)

Reorder Cost

($)RM Cost ($)

Penalty

Cost ($)

Retailer 1 (R1) 216,035.00 111,008.05 986.09 4,795.86 12,850.00 86,370.00 25.00

Retailer 2 (R2) 215,115.00 109,987.56 1,055.01 5,131.43 12,850.00 85,996.00 95.00

Retailer 3 (R3) 210,230.00 106,558.26 1,144.88 5,568.86 12,850.00 84,018.00 90.00

Scenario Retailer 1 (units) Retailer 2 (units) Retailer 3 (units)

Base Case 114 117 120

LD 118 123 124

LL 117 120 121

LM 104 105 108

HD 142 122 118

HL 121 123 124

HM 105 104 100


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When the uncertainty is set to the high level except for the case of high supply uncertainty (inwhich the uncertainty in lead time shows the least significant effect), the trend of the targetstock levels of Retailer 1, Retailer 2, and Retailer 3 shows a decrease due to the fact that thevariation of the customers demand for Retailer 1, Retailer 2, and Retailer 3 increases. From adetail analysis where we managed to swing the uncertainties in the demand and process half

way (either swing up or swing down), it was found that, there are two possible outcomes. Thefirst outcome results in having less inventory, as the demand and process uncertainties onlyset to swing down (- 25% or -50%). With this case, the trend of the optimal target stock levelsof Retailer 1, Retailer 2, and Retailer 3 shows an increase similar to the base case, due to ahigher inherent aleatory uncertainty of each retailer. On the contrary, with the case of havingmore inventories as the demand and process uncertainties only set to swing up (+25% or+50%), the retailer is advised to keep less inventory (setting lower target stock level) as theuncertainty increases. This is because there is a high chance that the kept inventory may not

be sold. Therefore, it is more profitable to keep less inventories in the shop.

Table 5: Profits in the retail supply chain

The profits of the whole chain and each member under different scenarios of uncertainty arepresented in Table 5. Having applied various uncertainties in the models to investigate theireffects under the optimal setting conditions of the interested parameters (target stock level ofthe retailers), it was found that the uncertainties cause a negative impact on the whole chains

profit, and a higher level of uncertainty can cause more or less even more severe negativeimpact. For example, as shown in Table 5, for the model with a low level of demand

uncertainty (LD), the profit of the whole chain is reduced by 2.53% in relation to the basecases profit, while for the model with a high level of demand uncertainty (HD), the profit isreduced up to 12.41%.


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Figure 3 : Profits of each retailer and the whole chain under each type of uncertainty

From Figure 3, it can also be noticed that the demand and process uncertainties (missedquantity) show negative effects on the supply chain profitability. The demand uncertaintydemonstrates a significant effect on the whole chain and particularly on the retailers profit,

but no significant change in the manufacturers profit. This is due to the fact that themanufacturer always sets its production 8 weeks in advance due to a retailers demand

-

50,000.00

100,000.00

150,000.00

200,000.00

250,000.00

300,000.00

350,000.00

400,000.00

450,000.00

500,000.00

Base Case LD HD

Profit($)

Demand Uncertainty

Manufacturer's Profit ($)

Retailers' Profit ($)

Whole Chain's Profit ($)

-

50,000.00

100,000.00

150,000.00

200,000.00

250,000.00

300,000.00

350,000.00

400,000.00

450,000.00

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Base Case LL HL

Profit($)

Supply Uncertainty




-

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100,000.00150,000.00

200,000.00

250,000.00

300,000.00

350,000.00

400,000.00

450,000.00

500,000.00

Base Case LM HM

Profit($)

Process Uncertainty





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without epistemic uncertainty. As a result, when there is the demand uncertainty, it does notshow any effect on the manufacturers production plan. In fact, as the uncertainty in demandswings both up and down, it does not show much effect on the manufacturers profit.However, in the case of process uncertainty, there is a negative effect for all members and thewhole chain. This is due to the difference between the number of units ordered and actual

units delivered from the manufacturer. When the process uncertainty swings up, moreexcessive units are delivered from the manufacturer but when the process uncertainty swingsdown, some delivered units are presumably lost during the delivery.

The supply uncertainty (lead time) fails to show a significant change in retailers,manufacturer, or the whole supply chain profits under each optimal target stock level

condition providing that it is varied up to 50% from the normal level. A Tukey comparisontest of each members profit is also shown in Figure 4. This is to rank the profits underuncertain conditions from maximum to minimum as compared to the base case where there isno epistemic uncertainty. Please note again that underlined models denote models that cannotdistinguish their profits under a 95% C.I. The Tukey comparison test results also confirm that

there is no significant impact of lead time uncertainty on profits as compared to otheruncertainties.

1) Result from Tukey 95% C.I. comparison of the Manufacturers Profit

Demand uncertainty Supply uncertainty Process uncertaintyLD Base HD LL Base HL Base LM HM

2) Result from Tukey 95% C.I. comparison of the Retailers Profit

Demand uncertainty Supply uncertainty Process uncertaintyBase LD HD Base LL HL Base LM HM

3) Result from Tukey 95% C.I. comparison of the Whole Chains Profit

Demand uncertainty Supply uncertainty Process uncertainty

Base LD HD Base LL HL Base LM HM

Note: Profits are ranked from maximum to minimum

Figure 4: Tukey 95% C.I.comparison test of the profits under the uncertainty

6.2.1. Effect of Epistemic Uncertainties on the Manufacturer

According to the Tukey comparison tests results on the manufacturers profit presented inFigure 4, the demand and lead time uncertainties show insignificant effect on themanufactures profit. As for the process uncertainty, the profit of the manufacturer is provento be lower by the comparison test when the level of uncertainty increases. This is mainly dueto a decrease in the revenue of the manufacturer, which is reduced according to this

uncertainty, as shown in Table 6. When the process uncertainty occurs, the manufacturer stillproduces the same production level as in the base case. Hence, the raw material, ordering and


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This variation has been proven by the statistical analysis as shown in Table 8 (Tukeycomparison test) as well as in the plot between the profit and conditions under the demanduncertainty shown in Figure 5. The demand uncertainty has a negative impact on all retailers

profit, despite the fact that the revenue of Retailer 1 is increased, while revenues drop for thecase of Retailer 2 and Retailer 3 when the level of uncertainty is changing from a low level to

a high level (as also shown in Table 7). This is corresponding to the higher target stock levelof Retailer 1 under the high uncertainty level as mentioned earlier. The penalty cost is shownto increase as the level of uncertainty increases while the holding, operating, and raw materialcosts seem to follow the same pattern. Retailer 1 pays the highest amount among retailers asmore products are kept in the shop.

Table 8: Tukey comparison test of demand uncertainty on the performance measures of theretailers

Demand Uncertainty

Retailer 1 Retailer 2 Retailer 3

Revenues H B L L B H L B H

Profit H B L L B H L B H

Holding Cost B L H B L H B L H

Operating Cost H L B H L B L B H

Penalty Cost H L B H L B L B H

RM Cost H L B L B H H L B

Ranking: All measures are ranked from maximum to minimum.

Note: B = Base case

L = Low level of demand uncertaintyH = High level of demand uncertainty


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Figure 5: Retailers financial performance measures with the demand uncertainty

Table 9: Tukey comparison test of process uncertainty on the performance measures ofretailers

Process Uncertainty

Retailer 1 Retailer 2 Retailer 3

Revenues B L H B L H B L H

Profit B L H B L H B L H

Holding Cost B H L B L H B L H

Operating Cost B H L B L H B L HPenalty Cost H L B H L B H L B

RM Cost B L H B L H B L H

Ranking: All measures are ranked from maximum to minimum.

Note: B= Base caseL= Low level of process uncertaintyH= High level of process uncertainty

Table 9 and Figure 6 show the Tukey comparison test and the plot of all measures under theprocess uncertainty. It was found that the process uncertainty also has a negative impact on

the revenues and profits of all retailers. When this uncertainty increases, revenues and profitsdrop as their penalty costs rise up sharply. Even though the raw material cost shows a

80,000

85,00090,000

95,000

100,000

105,000

110,000

115,000

Base case LD HD

Profit($)

Profit

Retailer 1

Retailer 2

Retailer 3

900

1,000

1,100

1,200

1,300

1,400

1,500

1,600

Base case LD HD

Cost($)

Holding Cost

Retailer 1

Retailer 2

Retailer 3

4,000

4,500

5,000

5,500

6,000

6,500

7,000

7,500

Base case LD HD

Cost($)

Operating Cost

Retailer 1

Retailer 2

Retailer 3

0

2,500

5,000

7,500

10,000

12,500

15,000

17,500

20,000

22,500

Base case LD HD

Cost($)

Penalty Cost

Retailer 1

Retailer 2

Retailer 3

80,000

82,000

84,000

86,000

88,000

90,000

Base case LD HD

Cost($)

RM Cost

Retailer 1

Retailer 2

Retailer 3

202,500

205,000

207,500

210,000

212,500

215,000

217,500

220,000

222,500

225,000

Base case LD HD

Revenue

($)

Revenue

Retailer 1

Retailer 2

Retailer 3


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19

decrease due to the fact that the target stock levels become lower in relation to the base case,the holding and operating costs, which are charged to the number of units held in the shops,seem to be quite stable when the process uncertainty has changed from a low level to a highlevel. This is since the order quantity can be missed in both directions (either exceed orshortage). As a result, the inventories in the shops are not much different.

Figure 6: Retailers financial performance measures with the process uncertainty

6.3 Analysis of the effect of epistemic uncertainties with interaction effects

Full factorial design with 8 scenarios ( ) has been performed. All models under theepistemic uncertainty have also been optimized by The OptQuest in order to find the optimaltarget stock levels. Table 10 summaries the financial results when there are interaction effectsfrom the interested uncertainties and Figure 7 shows ANOVA of total profit from theexperiment.

60,000

70,000

80,000

90,000

100,000

110,000

120,000

Base case LM HM

Profit($)

Profit

Retailer 1

Retailer 2

Retailer 3

800

850

900

950

1,000

1,050

1,100

1,150

1,200

Base case LM HM

Cost($)

Holding Cost

Retailer 1

Retailer 2

Retailer 3

4,000

4,500

5,000

5,500

6,000

Base case LM HM

Cost($)

Operating Cost

Retailer 1

Retailer 2

Retailer 3

0

5,000

10,000

15,000

20,000

25,000

30,000

Base case LM HM

Cost($)

Penalty Cost

Retailer 1

Retailer 2

Retailer 3

70,000

74,000

78,000

82,000

86,000

90,000

Base case LM HM

Cost($)

RM Cost

Retailer 1

Retailer 2

Retailer 3

180,000

185,000

190,000

195,000

200,000

205,000

210,000

215,000

220,000

Base case LM HM

Revenue

($)

Revenue

Retailer 1

Retailer 2

Retailer 3


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Table 10: Financial performance measures under the interaction effects

Revenue ($) Profit ($)Holding

Cost ($)

Operating

Cost ($)

Penalty

Cost ($)RM Cost ($)

Manufacturer 246,508.00 96,521.18 3,979.82 63,877.50 5,402.00 63,877.50

Retailer 1 177,140.00 37,933.82 797.05 3,874.13 50,945.00 70,740.00

Retailer 2 185,605.00 53,729.18 979.34 4,760.48 39,010.00 74,276.00

Retailer 3 208,900.00 90,622.40 1,441.05 7,004.55 13,400.00 83,582.00

Manufacturer 252,128.00 101,373.13 3,943.87 63,877.50 6,206.00 63,877.50

Retailer 1 198,625.00 67,991.88 1,056.62 5,138.50 32,220.00 79,368.00

Retailer 2 202,545.00 74,087.92 1,055.12 5,067.73 28,715.00 80,974.00

Retailer 3 203,230.00 86,319.39 1,205.86 5,867.76 15,675.00 81,312.00

Manufacturer 248,540.00 98,073.20 3,589.80 63,877.50 6,272.00 63,877.50

Retailer 1 187,650.00 50,615.17 943.16 4,581.67 43,600.00 75,060.00

Retailer 2 202,235.00 70,872.45 1,116.05 5,428.51 30,590.00 80,522.00

Retailer 3 188,715.00 58,463.18 954.01 4,640.81 36,425.00 75,382.00

Manufacturer 251,368.00 100,696.84 3,764.16 63,877.50 6,302.00 63,877.50

Retailer 1 201,680.00 70,495.91 1,058.40 5,147.69 31,515.00 80,708.00Retailer 2 213,315.00 93,171.65 1,354.23 6,591.73 15,050.00 85,182.00

Retailer 3 191,070.00 68,497.78 922.79 4,489.43 27,850.00 76,460.00

Manufacturer 248,026.00 100,132.61 3,878.39 63,877.50 3,410.00 63,877.50

Retailer 1 183,435.00 57,352.61 827.60 4,022.79 35,070.00 73,312.00

Retailer 2 196,825.00 76,476.57 1,030.24 5,013.19 22,815.00 78,640.00

Retailer 3 195,470.00 78,775.73 1,064.52 5,174.75 19,235.00 77,970.00

Manufacturer 252,290.00 105,581.81 4,363.20 63,877.50 1,740.00 63,877.50

Retailer 1 199,550.00 82,008.33 910.87 4,429.80 20,240.00 79,756.00

Retailer 2 205,530.00 93,795.87 1,066.54 5,185.59 10,490.00 82,142.00

Retailer 3 200,170.00 89,938.04 1,032.39 5,031.31 11,375.00 80,028.00

Manufacturer 249,884.00 102,200.56 3,902.44 63,877.50 3,176.00 63,877.50

Retailer 1 191,130.00 68,833.69 911.80 4,434.52 27,500.00 76,600.00

Retailer 2 204,500.00 88,604.00 1,203.77 5,861.23 14,355.00 81,626.00

Retailer 3 179,525.00 56,240.01 819.76 3,984.23 33,855.00 71,776.00

Manufacturer 252,112.00 105,041.79 4,013.21 63,877.50 2,452.00 63,877.50

Retailer 1 204,700.00 89,281.21 989.65 4,812.14 14,520.00 81,902.00

Retailer 2 207,615.00 94,765.07 1,076.01 5,233.92 10,680.00 83,010.00

Retailer 3 194,695.00 80,480.96 895.92 4,356.12 18,260.00 77,852.00

5. LD + HL + HM

6. LD + HL + LM

7. LD + LL + HM

8. LD + LL + LM

1. HD + HL + HM

2. HD + HL + LM

3. HD + LL +HM

4. HD + LL + LM


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Figure 7: ANOVA of the whole chains profit versus demand, supply, and processuncertainty

ANOVA results presented in Figure 7 show that, with 95% confidence level, the demand andprocess uncertainties have an effect on the total profit of the supply chain. If we look closelyat the results from ANOVA, we can see that the process uncertainty (missed quantity) has thehighest effect to the chain profit, followed by the demand uncertainty. However, the supplyuncertainty and all interactions have shown no effect on the whole chains profits.

As can be seen from Figure 8 (the main effect plot of the demand and process uncertainties),the graph shows that the level of uncertainty of both demand and process has a negativeeffect to the whole chains profit. When the level of uncertainty changes from a low level to ahigh level, the total profits of the whole chain reduce accordingly. Since, there is nointeraction among the main effects; the profits of the chain are mainly reduced as the changeof conditions from the main effects, which are the demand and process uncertainties.

General Linear Model: Whole chains Profit versus DEMAND, SUPPLY, PROCESS

UNCERTAINTY

Factor Type Levels Values

DEMAND fixed 2 1, 2

SUPPLY fixed 2 1, 2

PROCESS fixed 2 1, 2

Analysis of Variance for Total Profit, using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F P

DEMAND 1 10299263437 10299263437 10299263437 60.67 0.000

SUPPLY 1 55088762 55088762 55088762 0.32 0.571

PROCESS 1 24746527058 24746527058 24746527058 145.77 0.000

DEMAND*SUPPLY 1 6441125 6441125 6441125 0.04 0.846

DEMAND*PROCESS 1 34932531 34932531 34932531 0.21 0.651

SUPPLY*PROCESS 1 2453501 2453501 2453501 0.01 0.905

DEMAND*SUPPLY*PROCESS 1 1789216 1789216 1789216 0.01 0.919

Error 72 12222686155 12222686155 169759530

Total 79 47369181786

S = 13029.2 R-Sq = 74.20% R-Sq(adj) = 71.69%


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Figure 8: Main effect plots of demand and process uncertainties

7. Conclusion

In most supply chain production/inventory models that involve uncertainties in theenvironment, the attention has been focused on the probabilistic modelling of the customerdemand side. Therefore, up until recent years, the uncertainties in the supply side have notreceived the amount of treatment that they deserve. We have studied how demand, supplyand process uncertainties affect inventory inaccuracy, the out-of stock level, and the costrelated to inventory inaccuracy. Our results indicate that eliminating inventory inaccuracycaused by uncertainty can reduce supply chain costs as well as increase profit. These resultsare achieved in a supply chain in which information on customer demand is alreadyexchanged.

There are three main causes of uncertainty in the retail supply chain under study demand,supply, and process uncertainties. The impact of the uncertainty on supply chain performance

varies by the factor that causes it. Inaccuracy caused by the process uncertainty, such asmissed quantity and theft, appears to have the biggest impact on supply chain performance,compared to inaccuracy caused by inaccurate demand forecast and shipment lateness. In ourstudy, inventory inaccuracy caused by supply uncertainty (lead time uncertainty) does nothave a significant impact on supply chain performance. This can mainly be attributed to thefact that the level of its impact on the supply chain profitability is not as significant, and can

be managed by setting a higher target stock level in the shop.

While this study makes a contribution to the academic literature and provides the potential topositively influence managerial practice, there are nonetheless limitations that provideopportunities for further research. First, the accuracy of a companys cost structure plays an

important role in obtaining good results. In fact, it is quite difficult for a company to committo some numbers in its cost structure since they probably have never been recorded or, in

MeanofTotalProfit

32

300000

290000

280000

270000

260000

32

DEMAND PROCESS

Main Effects Plot (fitted means) for Total Profit

LOW LOWHIGH HIGH


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many instances, managers are hesitant to estimate them. Moreover, the cost structure variesfrom one company (industry) to the other. As poor inputs lead to poor results, without areliable cost structure, the obtained results could be misleading and could lead tomisinterpretation. Sensitivity analysis could also be conducted with respect to some cost

parameters to check their influence on the results. Also various structures of supply chains

are needed to further investigate in order to understand the circumstances under which it isworthwhile to address the problem of inventory inaccuracy. Our study is limited to a one-

product supply chain configuration with specific parameter estimates (e.g., for demand, leadtime, and incorrect delivery variability) and default values for the factors that cause inventoryinaccuracy. An extension to cover a wider range of the above-mentioned factors could

possibly lead to other in-depth results.

Our current results suggest that it can be useful for companies that face a high level ofinventory inaccuracy to examine procedures or technologies to eliminate them. To give someguidelines, the results of our model indicate that an elimination of inventory inaccuracy canincrease the chain profit up to 30%. A larger saving comes from the retailers than the

manufacturer since most of the members in the retail supply chain are retailers. With thecurrent setting, the manufacture does not show to be affected much by the uncertainty. Infact, only the process uncertainty can affect the manufacturers profit. However, the retailers

are somewhat more sensitive and should be well aware of these unforeseeable uncertainties.In practice, there are different approaches that can help to improve inventory accuracy. Someresearchers advocate the use of benchmarking, awareness building, and processimprovements. Additionally, automatic identification technologies such as RFID offer the

potential to increase accuracy. Recent developments indicate that RFID is going to be, and insome cases, has been, adopted in a number of retail supply chains to replace the old bar codesystems. As a result, case studies based on real data should be conducted to study the impactof these uncertainties in relation to total supply chain costs. In these case studies, one mayalso compare the benefits of eliminating the uncertainty with associated cost of processchanges or the introduction of new technologies.

Acknowledgements

This work was supported by the National Research University Project of Thailand Office ofHigher Education Commission. We are also grateful to the reviewers for their constructiveand helpful comments on an earlier version of this paper.

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