Forecasting Demand For Automotive Aftermarket Inventories

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Informatica Economică vol. 17, no. 2/2013119Forecasting Demand for Automotive Aftermarket InventoriesOvidiu DOBRICANWest University of Timisoaraovidiu.dobrican@feaa.uvt.roManagement decisions regarding the resource allocation in the automotive aftermarket involves a good understanding of it. This includes a better understanding of the participants inthis market, the supply chains, specificities products and demand for these products. A usefulinstrument to anticipate the latter is the use of simulation methods, one of them being theMonte Carlo method, which, in this paper, is used to create various scenarios of supply.Keywords: Inventory Management, Aftermarket, Demand, Monte Carlo Simulation1IntroductionWe can define the inventory as a physicalstock of economic resources that are storedor reserved for a good functioning of relatedbusiness. Inventories are required because, ingeneral, a customer will not like to wait for along time, until their commands were filledfrom a source or were produced.On the other hand, maintaining inventory canprotect against the seasonal price fluctuationof some raw material, because a buyer canprocure a large quantity at good price and useit also when the price is high [1].Inventory management also concerns aboutlead time, carrying costs, asset management,inventory forecasting, inventory valuation,inventory visibility, future inventory price,physical inventory, physical storage, qualitymanagement, replenishment, returned goods,obsolete goods and demand forecasting [2].To maintain inventories involves the use ofresources, so, it is necessary to introduce aset of policies and controls that establish andtrack levels of inventory and determine whenstock should be refilled [3].In addition, in automotive aftermarket domain, a large inventory could, also, attractmore customers resulting increase in sale andprofits.For this purpose, we can use an inventorymodel based on Monte Carlo to determinethe optimum inventory level in terms of safety stock and replenishment scenario. For this,the starting point is forecasting of demandwhich includes the prediction, projection orestimation of expected demand of the products over a specified future time period.2 Inventories in Automotive AftermarketDomain2.1 Automotive Aftermarket Structureand EvolutionAftermarket parts are an alternative of OEM(original equipment manufacturer) parts.These parts are produced by companies otherthan the original manufacturer but are madeto fit and perform as well as the original. Aftermarket companies buy the rights to reproduce these parts. There are a variety of highquality aftermarket parts that can be purchased and live up to all the expectations ofthe original at a much lower price. In bothcases, OEM and aftermarket parts, the partshould perform the function it was designedfor, so the car owner will decide which partto choose [4].Over the years, supply chain in automotiveaftermarket has gone through many transformations as we see in Figure 1, Figure 2and Figure 3 consecutively.Cash and carry retailers (discount shop selling a wide range of products) had an important role in aftermarket domain but, recently, they reduced their presence in thismarket, because of the need to allocate resources to other increasing competition sectors, like in food and clothing [5].DOI: 10.12948/issn14531305/17.2.2013.10

Informatica Economică vol. 17, no. 2/2013120Fig. 1. Automotive aftermarket supply chain structure – version 1 (adapted [5])National wholesalers, who used to rely on theoriginal equipment (OE) manufacturers for alarge proportion of their goods, are now serviced by Inter Factors (local retailers that deliver to point of use on very short lead-time)which are closer to the customer and can deliver goods to the point of use quicker. Alsothey have now only the role to serve independent wholesalers, as we see in Figure 2DOI: 10.12948/issn14531305/17.2.2013.10and Figure 3.National retailers have also moved awayfrom OE supply in order to eliminate theneed for large central stores (warehousesowned by national players). Instead these national retailers have opted for service fromindependent wholesalers at a local level resulting in better service time and reducedstock ownership [5].

Informatica Economică vol. 17, no. 2/2013121Fig. 2. Automotive aftermarket supply chain structure – version 2 (adapted [5])Also, as we see in Figure 2 and Figure 3, therole of independent wholesalers increasedwith the decline of “Cash and Carry” organizations. These independent wholesalers haveswitched attention to fast fits, retailers, independent garages and fleet garagesFast Fits (menu based service providers)have also evolved in a similar manner, moving towards more locally based suppliers.They increased their supply area of by including strategically distributors and even directly OE/Aftermarket operators in order tofulfil end users’ needs.Local parts shops have found it increasinglyhard to win market share since many peopleprefer to deal directly with independentwholesalers. Their decline was also causedby disappearance of “Cash and Cary” storesin this market.Specialists (providers of specialist parts andequipment) have increased in importance, inthat they became a very fast connecting linkbetween the OE/Aftermarket operators andthe end users. By that, they bypass all intermediate levels, thus ensuring more rapid delivery to the final customer.Consequently, to the growing number of specialists, independent garages began to losemarket share.DOI: 10.12948/issn14531305/17.2.2013.10

Informatica Economică vol. 17, no. 2/2013122Fig. 3. Automotive aftermarket supply chain structure – version 3 (adapted [5])The end users now have access to a widerrange of suppliers like strategically distributors or specialist, as we see in Figure 3, [5]. 2.2 Reasons for Holding InventoriesTaking into account [4], [6] and the particularities of automotive aftermarket domain,companies keep inventories for the followingreason: To keep down productions costs. Becauseit is costly to set up machines, productionshould not be stopped as long as it is notnecessary, in order to obtain low unitcosts, but, it is necessary to establish abalance between these costs and the costsof holding stock; To help the production and distributionDOI: 10.12948/issn14531305/17.2.2013.10 operations run more smoothly. Here,stock is held to “decouple” the two different activities;To avoid lack of stock. Growing demandsincrease the risk of shortages. This riskwill be reducing by having safety stocksthroughout the distribution channels (national or independent wholesalers, retailers) as we see in Figure 3. But, in thiscase, an important aspect is inventory visibility. This refers to the requirement forcompanies located throughout the supplychain, to deliver the latest and accuratedata from in-stock inventory to in-transitinventory, and helps optimize supplychain process [7];To meet expected demand. It is necessary

Informatica Economică vol. 17, no. 2/2013to have a buffer stock to cover the expected demand, especially for seasonalproducts like air springs, for example; To take account of variable supply (lead)times. A supplementary stock is necessary to cover any unexpected delivery delays from suppliers; To take advantage of the large orderssize, by reducing order and shipmentcost; To maintain independence of operations.The possible problems that can occuracross the distribution channels require abuffer stock in order to maintain independence of the two operations, supplyand delivery; To protect against price increases. Sometimes is useful to order large purchase toachieve quantity price savings and also toprotect when expected price increasesare; To provide customers with immediateservice. It is essential in automotive aftermarket domain, to provide goods assoon as they are required, in order to increase customer confidence, and, by consequence, to increase the number of customers.All these reasons are important to avoid under stocking and, by consequence, missed deliveries, backlogged orders, lost sales andunhappy customers.2.3 Costs and demand in aftermarket inventory domainAn important role in decisions concerningthe type and level of inventory cost plays.Taking into account the specificities of thismarket, according to those specified in [6],[8], [9] and [10], we can emphasize the following types of costs: Inventory carrying cost: capital cost (thelargest component), storage space cost(rent, handling, heating, lightning), service costs (insurance, taxes), risk costs(obsolescence, damage, theft); Order cost - reviewing inventory stocklevels, preparing purchase orders, checking and inspecting stock prior to placement in inventory, preparing and pro-123cessing payments Shortage Cost - depends on how thecompany handles the problem. If theproblem is solved only with a back-order,this cost is simple to quantify. The majorproblems appear when the customer'sgoodwill is affected. This penalty costsare more difficult to measure, as it is influenced by further losses of sales.The other variables which taking into account on inventory decision making are: Lead – Time - it is the time between placing an order and its realization in stock. Itcan be deterministic or probabilistic. Instudied domain, the lead–time is deterministic; Demand – it may be deterministic, whendemand over a period is known, or probabilistic, when the demand over as periodis uncertain but can be predicted by aprobability distribution. Taking into account the specific aftermarket domainand those specified in [11], we emphasizes the several other classifications of demand:o Level of demand – is always measured in relative terms to your totalinventory and it may be high (service parts – filters) or low (centralunits);o Frequency of demand – it may befast and, in general, with deterministic demand (tyres) or slow (car bodyparts);o Patterns of demand – it may be stable, trend (upward or downward) orseasonality (like air springs for example);o Product life cycle positioning – thefive distinct phases in a product lifecycle - launch, emerging, established, decline, withdrawal are lessapplicable in aftermarket area, dueto the specialized nature of thismarket;o Product classification – it refers toParreto classification. According tothis classification, 20% of the totalnumber of items represent as muchas 80% of the total turnover (ADOI: 10.12948/issn14531305/17.2.2013.10

124products), 30% of the total numberof items represent only 15% of thetotal turnover (B products), and 50%of the total number of items represent only 5% of the total turnover (Cproducts). “A” items should receivea special attention in inventory control and in forecasting, but companies might decide to include “C”products in the “A category” if theseitems are strategically important tothe business or critical to one of thecompany’s strategic customers evenif their absolute sales value is low[11].It may be also independent or dependent demand, but in automotive aftermarket domain,we can consider that the demand is independent.To determine the quantities of items thatmust be ordered, firms usually analyze theirsales and market, using a variety of techniques, like customer surveys or forecasting.3 Demand forecasting and Monte CarlosimulationThe key problem is to closely match supplyand demand. According to [10], there areseveral ways to realize this: a supplier to have stock available for delivery at any time. This method maximizes sales revenues, but it is also expensivebecause of the cost of having inventoryand the possibility of discounts at the endof the selling season use of flexible pricing. During heavy demand periods, prices can be raised to reduce peak demand. Price discounts canthen be used to increase sales during periods with excess inventory or slow demand. This strategy can determine lostsales, stock outs, and thus cannot be considered a fair strategy to satisfying demand.Managing demand is difficult because it ishard to forecast future consumer requirements accurately. Suppliers must be able toaccurately forecast demand so they can produce and deliver the right quantities demanded by their customers in a timely and cost efDOI: 10.12948/issn14531305/17.2.2013.10Informatica Economică vol. 17, no. 2/2013fective fashion. Thus, it is imperative thatsuppliers along the supply chain find ways tobetter match supply and demand to achieveoptimal levels of cost, quality and customerservice to enable them to compete with othersupply chains. Any problems that adverselyaffect the timely delivery of products demanded by consumers will have ramifications throughout the entire chain [10].So, in order to realize a good inventory management, it is necessary to use forecasting.According to [12], “forecasting is a processof estimating a future event by casting forward past data. The past data are systematically combined in a predetermined way toobtain the estimate of the future”. The goal ofa good forecasting technique is to minimizethe deviation between actual demand and theforecast [10].Forecast the demand is also the key elementin automotive aftermarket supply chain, frommanufactures who need to know how muchto produce to wholesalers and retailers whoneed to know how much to stock. In today’scompetitive business environment, collaboration (or cooperation and information sharing)between supply chains actors is the rule rather than the exception. The benefits of betterforecasts are lower inventories, reduced stockouts, smoother production plans, reducedcosts and improved customer service [10].To realize a good inventory management, itcan be use a model which may be deterministic - limited because it treats all parametersrelating to future operations as certain, orprobabilistic (stochastic) – considers uncertainty in demand and the delivery lead time.A variable is stochastic if its value cannot bespecified in advance of observing it. [13]To determine which version of the model tobe applied, it starts with the analysis of theprevious demand levels, evaluated at n pointsin time, and calculation of average, standarddeviation and coefficient of variation.N1, N2,.,Nn – n points in timeN - averageσ – standard deviationCv – coefficient of variation

Informatica Economică vol. 17, no. 2/20131251 n Nin i 11 n 2 ( Ni N )2 n 1 i 1The probability that the value of a stochasticvariable X is less than or equal to a certainvalue xi is called the cumulative distributionfunction or cumulated probability and is denoted P(xi). 2 Cv NP(xi) p(X xi)P( x i ) p(v) for - xi N If Cv 0.2, i.e. have a low scattering values,models to be used are deterministic, sincedemand is sufficiently close to the average.If Cv 0.2, scattering demand values is quitehigh, the average is not a good demand’s level, so the applied model is probabilistic.In automotive aftermarket domain, demand’suncertainties require probabilistic inventorymodels. In these models the keyword is simulation.When we use the word simulation, we referto any analytical method meant to imitate areal-life system, especially when other analyses are too complex or too difficult to reproduce.A Monte Carlo method is a technique thatinvolves using random numbers and probability to solve problems. [14] The MonteCarlo method is just one of many methodsfor analyzing uncertainty propagation, wherethe goal is to determine how random variation, lack of knowledge, or error affects thesensitivity, performance, or reliability of thesystem that is being modelled. Monte Carlosimulation is categorized as a samplingmethod because the inputs are randomly generated from probability distributions to simulate the process of sampling from an actualpopulation. [15]The Monte Carlo method generates randomvalues of a variable, by using: a generator of random numbers uniformlydistributed in the interval [0, 1] and cumulated probability distribution associated to this random variable. [13]We consider a stochastic variable X withmultiple possible values and xi a particularvalue of the variable X.The probability that a value of the stochasticvariable X is equal to a certain value xi is denoted p(X xi) p(xi).v xihaving the propertyP(xi) 1Cumulative probability P(xi) is then, the sumof probability of values less than or equal toxi.For the distributions based on historical dataor by direct measurement of stochastic valuesof variable, considering that these are in thenumber of N, for that variable X, we can obtain the following table (Table 1):Table 1. Historical data or direct measurementsValue of thevariable xiFrequency ofoccurrence fix1f1x2f2.xmfmThe application of Monte Carlo method involves several steps: to calculate probabilities and cumulativeprobabilitiesfp ( xi ) m i fii 1iP ( xi ) p ( xi )k 0Where:m fi Ni 1p( x0 ) P( x0 ) 0 associate random numbers intervals foreach cumulative probabilityThe results can be synthesized in the Table 2.DOI: 10.12948/issn14531305/17.2.2013.10

Informatica Economică vol. 17, no. 2/2013126Table 2. Determination of the random number intervalsValue of the variable xiProbabilityCumulated probabilityRandom numbers intervalsx1p(x1)P(x1) p(x0) p(x1)[P(x0), P(x1))x2p(x2)P(x2) P(x1) p(x2)[P(x1), P(x2)).xmp(xm)P(xm) P(xm-1) p(xm)[P(xm-1), P(xm)) generate a table of random numbers uk,uniformly distributed on the interval[0,1], using a specialized generator, suchas RAND() function from Excel searching in Table 2, the interval [P(xk-1),P(xk)) which belongs uk number and writenext to it, the simulated value xk repeat the last two steps until we reachthe desired volume of simulated data.These values can be further used in the development of supply scenarios as we willemphasize in the case study.By Monte Carlo method, the real process isreplaced by an artificial process. To obtainbest results, it is necessary that the randomvariables generated during simulation experiments to replicate as closely as possible, realrandom variable.Therefore it can be established, for example,for average, a confidence interval calculatedusually at 95%.Confidence interval for the average 1 -α,where α is usually 0.05, is: ( x t / 2, N 1 , x t / 2, N 1 )NNwhere:tα/2,N-1 is obtained from the t distribution tables.N is the number of simulated eventsWe can observe that the method is more pre-DOI: 10.12948/issn14531305/17.2.2013.10cise (narrower range) as the number of eventsis greater, i.e. N1/2.If, after N experiments, is obtained an average M determined by the formula:1 NM viN i 1then the average of the variable X could beanywhere in the range (M-Δ, M Δ), where:N 2 (v M )2ii 1N 1NTherefore the question is, in general, in theMonte Carlo simulations, the number of procedures to be of the order of 103.4 Case StudyThe studied company collaborates with majormanufacturers or strategic distributors in particular from Germany. These vendors usuallyimpose conditions related to lead-time, minimum order values, discounts, etc. Thereforeit is necessary a harmonization of the valuesresulting from simulation with these conditions.Based on demand recorded in the previousperiods of time, we try to establish a supplysimulated scenario for a product, using Monte Carlo method with initial stock 0, as wecan see in Figure 4

Informatica Economică vol. 17, no. 2/2013127Fig. 4. Historical dataThese intervals are established based on thelead-time imposed by the manufacturers orstrategic distributors.We can observe in Figure 4, that Cv 0.2 AVG 2.4Fig. 5. Simulation dataAlso, we will consider value added, storagecost and shortage loss: (Figure 5) calculating value added as followsvalue added purchase price * prc ad calculating a shortage loss as followsShortage loss value added * (stock bgn per- stock end per) calculate a storage cost for remainingstock at the end of each periodStorage cost stock * percent storage costWe’ll apply then, Monte Carlo method in 2scenarios: Each order will be below average – 2pieces – Figure 6DOI: 10.12948/issn14531305/17.2.2013.10

Informatica Economică vol. 17, no. 2/2013128Fig. 6. Scenario with 2 pieces Each order will be above average – 3pieces – Figure 7Fig.7. Scenario with 3 piecesAt the end of the experiment we’ll emphasizenet profit and stock’s level for this product.5 DiscussionsAs we said, previous data observation periods were established based on delivery timesprovided by large operators in the market.Our company must conform to these terms.We will observe also, that in both simulatedscenarios, profit is approximately the same,but the final stock is 2 respectively 51, so thepreferred scenario that with 2 pieces order.But, in the scenario with 2 pieces could appear shortages, so a discussion can be maderelated to shortage cost.Mathematically, it can be calculated by theDOI: 10.12948/issn14531305/17.2.2013.10formula:Shortage loss value added * (stock bgn per- stock end per)In fact, the loss could be much higher because it may lead to losing the customer.For this, it is necessary to order more, at leastat certain periods, to cover additional demand.6 ConclusionsIn this paper we analyzed structure of automotive aftermarket and how it has transformed over the years. Then we analyzedwhy it is important to hold inventories. Theinventories has involves costs. So, we emphasized further the costs of holding stocks.

Informatica Economică vol. 17, no. 2/2013To establish the level of these inventories, weused a mathematical models based on simulation. The presented model shows howMonte Carlo simulation model can be used tohelp managers to build an order scenario.In terms of future directions, this model canbe extended by testing various scenarios andinclude elements of what-if analysis.Also, it can be extended to other scenarios ofsupply for all products provided by a manufacturer, to be able to make a “unified” stockorder.So, in conclusion, modeling provides an important support for decision making processin this dynamic and growing domain of automotive aftermarket, so integration of theseelements in inventories management concepts is absolutely necessary.The quality of the simulation, however, willbe confirmed only by the way future customer requirements are covered, something thatwe will see in the future sales level and innumber of new customers.References[1] S. Gor & Soni,Operations Research, PHILearning Pvt. Ltd., ch 20, 2013)[3] F. R. Jacobs, R. B. Chase, Operationsand Supply Chain Management: TheCore. McGraw-Hill, 2008[4] http://www.thepartsbin.com/oem vs aftermarket.html, (March 22, 2013)[5] J. Hammant, S.M. Disney, P.Childerhouse and M.M. Naim, “Modelling the consequences of a strategic supply chain initiative of an automotive aftermarket operation”, International Journal of Physical Distribution & Logistics129Management, Volume 29 (9):16, pp. 553550, Emerald Publishing, Nov 1999[6] A. Rushton, Ph. Croucher, P. Baker, Thehandbook of logistics and distributionmanagement, 4th Edition, Kogan PageLimited, 2010[7] A. N. Zhang, M. Goh, F. Meng “Conceptual modelling for supply chain ion Economics 133, pp. 578–585,Elsevier B.V., 2011[8] D.M. Lambert and J.T. Mentzer, “Inventory Carrying Costs: Current Availabilityand Uses”, International Journal of Physical Distribution & Logistics Management, Vol. 12 (3), pp. 56 – 71, 1982[9] J.J. Coyle, C.J. Langley, B.J. Gibson, R.Novack, E.J. Bardi, Supply Chain Management: A Logistics Perspective.Cengage Learning, 2008[10] J. D. Wisner, K-C. Tan, G. K. Leong,Principles of Supply Chain Management,3rd edition, Cengage Learning, 2012[11] C. Scott, H. Lundgren, P. Thompson,Guide to Supply Chain Management,Springer-Verlag Berlin, 2011[12] K. C. Arora, Production and OperationsManagement. Firewall Media, 2004[13] W. L. Dunn, J. K. Shultis, ExploringMonte Carlo methods, Elsevier B.V.,2012[14] T. N. Srivastava, Business ResearchMethodology. Tata McGraw-Hill Education, 2011[15] J.W. Wittwer. (March 23, 2013) Generating Random Numbers in Excel forMonte Carlo Simulation, GeneratingRandomInputs.html, June1, 2004.Ovidiu DOBRICAN is Teaching Assistant at Faculty of Economics andBusiness Administration at the West University of Timişoara and a PhD student in Business Information Systems at Babeş-Bolyai University of ClujNapoca. His main research areas are: DSS domain and collaborative systems.DOI: 10.12948/issn14531305/17.2.2013.10

quality aftermarket parts that can be pur-chased and live up to all the expectations of the original at a much lower price. In both cases, OEM and aftermarket parts, the part should perform the function it was designed for, so the car owner will decide which part to choose [4]. Over the years, supply chain in automotive

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