Forecasting, Production and Inventory Management of … · Forecasting, Production and Inventory Management of Short ... sales over the entire sales period) 2. Forecasting by period,

36Supply Chain Forum An International Journal Vol. 12 - N°4 - 2011 www.supplychain-forum.com

Forecasting, Productionand InventoryManagement of ShortLife-Cycle Products: A Review of the Literatureand Case Studies

Sabrina Berbain GriIsg, Institut Supérieur de Gestion, Paris, France

[email protected]

Régis BourbonnaisLEDa, Paris-Dauphine University, France

[email protected]

Philippe VallinLamsade, Paris-Dauphine University, France

[email protected]

The evolution of consumer behaviour (a dual demand of a large varietyand personalised products) has encouraged firms to enlarge their productrange. As a result, certain products have a very short life cycle (a fewweeks to a few months). The supply chain for these products cannot beexactly the same as for mass-market products. In this article, we proposea literature review specific to short-life-cycle products' supply chain. Wecover the three following fields: demand forecasting, inventorymanagement, and production management. We also present the resultsof two studies that we have conducted: the first deals with demandforecasting for CDs and the second deals with the printing policy for anannual guide. The absence of sales history in the case of short life-cycleproducts does not allow the use of classical models for calculatingforecasts. Specific models are then used: the Gompertz model and thelogistical model. For inventory management, two policies can beconsidered: a single supply or two. The results of the study conducted onthe policy of edition of an annual guide showed that the total cost of thesecond policy is lower. The production management of short life-cycleproducts can be based on the following triptych: modular design, delayeddifferentiation and flexibility

Keywords: product life cycle, short-life-cycle products, demandforecasting, inventory management, production organisation

Introduction

Studies dealing with the evolutionof consumer behaviour show thatconsumers, on the one hand, arelooking for diversity in theconsuming process (or at least awide choice) and, on the otherhand, want to use the product toset themselves apart. Certainproducts, therefore, have a veryshort life cycle (a few weeks to afew months).

Other products, by their verynature, have a life cycle that is

limited by their technology or theiruse. To work within these expectations,firms find it necessary to managemore and more items with shorterand shorter life cycles. Thistendency leads to severalconsequences.

Increased difficulty in coming upwith reliable sales forecasts As a result of this evolution, theforecaster is faced with two newproblems: • The reduction of product life

cycle limits the depth of the

© Copyright BEMISSN print 1625-8312ISSN online1624-6039

An International JournalSupply Chain Forum


history available and, therefore,holds back - or even makesimpossible - the use of classicalsales forecasting methods basedon analysis of this history. This isa problem well known toforecasters in the clothing-textilesector, where most of theirproducts have a life cycle of nolonger than six months (acollection).

• Another factor interfering with the forecasting exercise isassociated with the increase inthe number of items. The dangerof cannibalism among theproducts increases and, as aresult, the distribution of theproduct mix within a product linebecomes unstable.

Increase in the required inventorylevel The decline of the quality of salesforecasting leads to an immediateincrease in the level of safetystocks, whose role is tocompensate for unexplainedvariations in demand.

In addition, because a rolling stockand a safety stock are necessary foreach item, the inventory levelincreases mechanically with thenumber of items (Anand, 2008).

Thus, inventory managementbecomes more complicated: thereis an increased danger of shortageor of having unsold items. In fact,the inventory cost of theseproducts is high because of themajor risk of obsolescence. Thisnecessitates a more accurateprocurement management than forstandard products.

Increased production cost due toshorter production runsThere is an additional industrialproblem. Short-batch productionrequires more flexibility and morefrequent production stops, thusleading to increased productioncosts. Economies of scale inproduction costs by classical massproduction are no longer possiblein this case; it will be necessary tofind a more appropriateorganisation of production.

We propose, in this article, topresent a literature review

covering short-life-cycle products'supply chain in terms of demand forecasting, inventorymanagement, and productionmanagement. We will also presentthe results of studies that we haveconducted: the first deals with thedemand for CDs and the secondwith the printing policy for anannual guide.

Demand Forecasting

Problems

Demand forecasting for short-life-cycle items is not part of theclassical process of time seriesanalysis. In fact, the choice of aforecasting method (Giard, 2003) isa function of a certain number ofcriteria, such as the length ofavailable history, the existence ofseasonal variability, and so on.

We can list three categories ofshort-life-cycle products: • Products created for a single

season (textile-clothing) or for aparticular occasion (a Mother'sDay present package)

• Limited-life-cycle products (CDs, DVDs, books, etc.)

• Products with very fast technical obsolescence (Decker & Gnibba-Yukawa, 2010), such as mobiletelephones, computers, GPSsystems, and so on

All of these articles have a commoncharacteristic: the lack of historicalreferences.

It is also appropriate to distinguishbetween (1) products with the

possibility of replenishment (e.g.,mobile phone, laptop, etc.) and (2)products that can be replenishedquickly (e.g., CDs, DVDs, etc.) andproducts with a long replenishmentperiod (e.g., textiles).

Three forecasting problems can beidentified: 1. Forecasting total sales (total

sales over the entire salesperiod)

2. Forecasting by period, usually monthly

3. Replenishment forecasting (possibly)

The results of case study on theindustry of CD are presented. So wewill, first introduce this industry.

Context of the industry of the CD

We develop in this section the mostimportant characteristics of theindustry of the CD.

Distribution typesDistribution is realized throughnetworks (specialized distribution,hypermarket / supermarket,wholesalers) which behavior isdifferent in terms of:- Facing implementation,- Replenishment,- Inventory management.

These distributors represent anintermediate level between finaldemand and the producer.

Great number of references with verydifferent behaviorsThere is very different behavior ofpermanent articles (as classicalmusic) and fashion articles(example of “Single” disk).

Items with very short life cycleSome articles have very short lifecycle, only few weeks.

A volatile marketThe market of CD can change fastly.The result of this fast changes isvery high stock which is reducedsometimes reduced by thedestruction of the products.

Management of returnsThe excess inventory in thedistribution are re-shipped andrefunded. This device can lead toadverse effects of order with high

The absence of sales

history in the case

of short life-cycle

products does

not allow the use of

classical models for

calculating forecasts.


quantities (gross order) followedby returns. The difference betweengross orders and returns is callednet orders.

A retro-active loop amplifier: The "Hits"The fact that an item is among thetop 10 Hit increases the orders,mainly from the wholesalers, byamplifying the demand. If the itemdecreases in Hit, the oppositephenomenon is observed.

The strong influence of advertising orpromotional policy on the demand We can mention:- The Special Operations (SO):

commercial actions planned sixmonths ahead on a group ofarticles.

- Television's Advertising campaigns: very focused on onearticle (sometimes single andalbum). They are often scheduledat the last moment to takeadvantage of blank advertisingspace.

- The event: promotion done by the artist on radio or on television.

- The advertising poster

An implementation andreplenishmentWhen a new article is launched, afirst order is done. It is followed byreplenishment order according todemand observed in distribution.

Large variety of articles due topackagingThe same article (with the samegencode) may be packageddifferently depending on the type ofdistribution.

Statistical forecasting methodsfor short-life-cycle products

The absence of history for thesearticles does not allow the use ofclassical forecasting methodsbased on historical analysis:analysis of seasonal variations,smoothing methods, and the modelof Holt, Holt-Winters, and so on(Giard, 2003).

In this context, the forecastingmethod is chosen by taking intoaccount its use and productionconstraints (continuous or bybatch): either the company

requires forecasts for each salesperiod (monthly or weekly)because it has relatively a rapidand inexpensive production orrestocking capacity, or it is onlyinterested in a forecast of totalsales (batch production or single orquasi-single restocking).

Forecasting demand by period The objective is to calculate aforecast at the level of the family inquestion (mobile telephones,computers, GPS systems, etc.) andto apply this forecast to eacharticle. The useful data aretherefore made up from the familysales history.

Three stages are, therefore,necessary: Stage 1: Constitution of families ofhomogeneous articles found in themarket (same technicalcharacteristics, same price range,etc.) Stage 2: Forecasting at the familylevel by a classical method ofhistorical analysis (exponentialsmoothing) Stage 3: Breaking down the forecastby article as a function of thearticle of reference, weight of asimilar article, market estimation,and so on

Forecasting by period andestimation of total sales The objective for this task is tocalculate a forecast by modellingthe product life curve (Abbas &Hirofumi, 1996; Niu, 2006) and byhistorical analogy (CDs, DVDs,books, etc.). The useful data are theaccumulated observed sales in thestart-up phase.

We will consider two main modelsof the life curve: the Gompertzmodel and the logistical model.

The Gompertz model This model is formulated asfollows: yt = ebrt + a or elseLog yt = a + b r t with 0 < r < 1Log yt = Natural logarithm of salesat time ta, b, r are the model parameters

We are concerned with a curvewhich evolves in an “S” shape, thatis to say one that increases rapidly

then slows down after an inflectionpoint. That is the typical evolutionof the sales of a product: the salesare zero at the beginning (t = 0)then reach saturation (t ).We note the following properties: If t - then Lim yt 0 if b < 0,If t then Lim yt ea (e is thebase of natural logarithms).

The parameter r takes into accountthe speed of the process: the lowerthe value of r, the more rapidlysaturation is arrived at. Theparameter a characterises thesaturation threshold (threshold =ea). Finally, the parameter b isassociated with the origin.

The inflection point of the curve isfixed (the second derivativevanishes 2 yt / 2 t = 0); it is arrivedat when cumulative sales represent36.8% of the saturation thresholdea.

Modelling of the sales during thefirst 10 weeks of distribution of aCD by a successful artist leads, for example, to the following result: yt = e-4.35x0.85t + 12.81. The saturationthreshold is, therefore, equal to e12.81= 366,813 units. The total salesof this CD will, therefore, be about367,000 units. Figure 1 illustratesthese results.

The logistical model The logistical model can beexpressed in the following way:

, with

ymax the saturation threshold,a and r two parameters of themodel (-1 < r < 0).The properties are as follows: IIf t - then Lim yt 0,If t then Lim yt ymax.The inflection point of the curve isfixed; it is reached when thecumulative sales reach 50% of thesaturation threshold ymax.Using the same data as previously,the estimated logistical model is

.

The saturation threshold,according to this model, is equal toabout 350,000 units sold.

The parameters estimation of thesemodels makes use of nonlinear

yt =ymax

1+ br t

yt =349 902.54

1+18.44!0.78t


estimation methods (Greene, 2005).These methods have limitationsbecause they assume an evolutionof sales that follows a particularform (even though the parametersallow a very large variation ofprofile), which sometimes isperturbed by unusual events(special offers, shortages, etc.).

For further discussion on thecomparative performance of thetwo models see (Morisson J., 1996).

Forecasting demand over theentire period The process draws on the productlife curve. It is based on theaccumulation of orders recordedand the status of the promotionaltour as explanatory factors (Baglinand al., 2007; Bourbonnais &Usunier, 2007).

The objective is to calculate a salesforecast (the most often cited is inthe textile-clothing sector) byintegrating the progressiveaccumulation or orders during thepromotion (the orders start to beplaced six months before delivery). Let us take the following intuitiveexample: if by week six theaccumulation of orders for a

summer short-sleeved shirt is30,000 units and that, for theprevious year, for the family ofsummer short-sleeved shirts, theorders at week six were for 420,000shirts, with total sales for theseason of 920,000, we can concludethat the total sales for the currentseason for the summer shirt willamount to 30,000 x 920,000/420,000= 65,714 units.

The forecasting methodology,although a bit more complex, isbased on this analogical principle.

We specify a relation for aninstantaneous cut (that is to say, amodel where all of the data usedare expressed for the same date),integrating two explanatoryfactors: - The fraction of the potential

sales following a visit by arepresentative (as shown in theturnover ratio of all of the clientsvisited to the total turnover of thecompany or, perhaps, the familyof articles)

- The accumulation of orders (weekly series representing theaccumulation of orders as of thelaunching of the collection)

The general model can be written inthe following way: VT = a0 x PPVa1 x PCa2

or elseLog VT = Log a0 + a1 Log PPV + a2Log PCwithVT = total sales for the season, for afamily of articles, PPV = fraction of the potential salesfollowing visits, for this family, PC = accumulation of the orders, forthis family.

The sample is an instantaneous cut.For each week during which thecollection is sold, it corresponds tothe history of orders for articlesfrom the same family.

The parameters a1, a2 are theweighting coefficients of eachexplanatory factor, which arerepresented in terms of elasticity. - a1 elasticity of total sales to clients

visited, - a2 elasticity of total sales to orders

recorded.

The parameter a0 (constant term)tends towards 1 at the end of theseason.

Figure 1Estimation of total sales of a CD using a Gompertz model and logistical model

Use of weekly models There is not a single model, butmany different models as thenumber of weeks in the promotion(about 20 per season in the textile-clothing domain). In fact, it isappropriate to integrate more andmore accurate informationaccording to the state ofadvancement of the promotion.The model for week i can be writtenas Log VTi = Log a0,1 + al,i Log PPVi + a2,iLog PCiwithVTi = total sales for the season,estimated at week i,PPVi = the fraction of potential salesfollowing visits at week i,PCi = orders accumulated in week i.

The parameters a0,1, al,1, and a2,iare the regression coefficientsestimated from the sales observedweek by week for a family ofarticles of the preceding year.

The coefficient al,i decreases,tending towards 0 as the promotionadvances, whereas the coefficienta2,1 increases and tends towards 1at the end of the promotion (therepresentatives have contacted allof their potential customers).

Forecasting use Thus, each week, based on theinformation transmitted by therepresentatives (most often bymeans of portable computers), it ispossible to estimate more andmore accurately the total sales forthe season for each article. All thatneeds to be done is to reinsert thetwo data sets (orders and,therefore, the new total and thefraction of potential sales visited)for the year and the current week inthe model, estimated on the basisof the preceding season.

The forecast can be made at thelevel of the item or at the level ofthe item colour, but rarely at thesize level for reasons of volume(standard breakdown tables by sizeare then used).

Procedure for estimating thecoefficients Generally, the coefficients areestimated at the level of the familyof products, which implies the

application of the same forecastingmodel for all of the articlesbelonging to a single family.

The estimation of the coefficients isdone using a technique of multipleregression applied, successively,for the 20 weeks that make up theseason, which provides the set of20 x 3 coefficients for the model (20weeks x a0, a1, a2):Log VT = Log a0,1 + a1,i Log PPVi + a2,iLog PCi for i = 1.20.

It is useful to make up 20 samplescomposed of n articles selected in afamily (n > 30):- the series for explaining (VT) the

total sales of the article j for theseason, j = 1, n,

- the first explanatory series (PPVi), the fraction of potential salesvisited in week i,

- the second explanatory series (PCi), the accumulated orders byweek i for the article j.

Thus, 20 regression results providean estimate of the 20 coefficientsthat are applied to the followingseason.

In order to be effective thisforecasting method requires a gooddeal of care. The model coefficientsare specific to each week. It is,therefore, necessary to establishperfectly the correspondencebetween the various regressioncoefficients (for a given week,calculated on the basis of thepreceding year) and the real stateof advancement of the promotion(to use the data for the same weekin the current year, to fill out themodel). A shift can lead to biasedresults.

Planning restocking The closer we are to the finaldemand, the shorter the timeremaining for dealing with theorders. The company must,therefore, be ready to react quicklyto a change in the final demand(Bourbonnais & Usunier, 2007).

How does one obtain informationon the final demand? We areparticularly interested in twospecific tools of SCM (supply chainmanagement): CPFR (collaborativeplanning forecasting and

replenishment) and SSM (sharedsupply management).

CPFR (Collaborative PlanningForecasting and Replenishment)CPFR is a collaborative processamong producers, suppliers, andthe distribution network to ensureoverall management of the logisticchain. It is based on thecommunication standards of thecomputerised data exchange. Theprocess aims to integratecommercial strategies of partnercompanies. The transmission ofpayments and promotion planningmakes it possible to developdemand forecasts.

The advantages of CPFR arenumerous:• Synchronisation of actions and

therefore flows • Close estimation of the final

demand • Producing a unique forecast for

all participants • Anticipation of anomalies• Problems solved a priori rather

than a posteriori• Reduction of inventories all along

the logistical chain

The limitations of CPFR may be dueto possible failures of a partner: • Absence of the will to collaborate • Inadequate competence• Technical limitations

SCM (Supply Chain Management) The objective of the SCM is tooptimise the efficiency of thesupply chain. It is a method withinthe framework of ECR (efficientconsumer response) techniquesand includes a strategy ofcontinuous resupply.

By making use of a collaborativeapproach, the supplier becomes co-responsible for supplying clientsbased on received information. Themanagement of supply to thedistributor is transferred to themanufacturer via a shared process.The supplier is no longer restrictedto simply executing the orders sentby the clients but, based oninformation concerning stocks,sales, promotion planning, and soon transmitted by the distributor,the manufacturer can prepareforecasts of needs and thus


adapt production and logisticalresources.

Based on sharing of information, oncooperation, and the adoption of awin-win strategy, the SCM realisesits objective of reducinginventories and stock outages(improving service), passing from alogic of pushed flows to a logic ofpulled flows and the adaptation ofmeans and resources to the realneeds of the consumers.

The partners, the manufacturer,and the distributor decide to • Share the management of

procurement • Establish a logistical dialogue

based on negotiated performanceobjectives

Table 1 summarises the variousforecasting models as a function ofthe objective sought.

Supply of Short-Life-CycleProducts

The term supply is used here bothfor the supply as well as forinitiating production.

The context

The supply of this type of productis characterised by three structuralcomponents:

- A localised demand limited by a horizon of a few months at themost, without exploitable history.This often leads to forecastingbased on analogy. The impact ofthe present time is strong (chanceevents, unanticipated competingcampaigns, etc.) with no hope oflater compensation, accentuatedby feedback information oftenslow in arriving and badlyadapted to the life cycle. Thisleads to a considerable risk offorecasting error .

- Possibilities of correction are very limited: the number of possibleresupplies is very low, oftenlimited to one or two.

- A strong penalty in case of a poor adjustment of inventories: stockoutages lead to lost demand orexpensive urgent corrections andunsold items have almost noresidual value.

What characterises the difficulty inmanaging this type of article is notso much the absolute lifetime of thearticle but the number ofresupplies that can technically oreconomically be made during thelife of the article, this numbernecessarily being close to one. Inthis sense, paradoxically, thedefinition of a fiscal policy ofannual collection of taxes goes withthe management of a short-life-cycle product.

The problem

The three fundamental questionsare as follows: - How many resupplies are planned

for during the life of the article? - What is the quantity for each

resupply? - What criterion should be chosen

to evaluate the effectiveness ofthe supply policy?

In Table 2 we try to classify theshort-life-cycle articles as afunction of the type of supplypolicy and the nature of thedemand. The demand can beexpressed only once over a veryshort time period within themanagement horizon (the typicalcase for Christmas trees) or can berepeated for similar references, asis the case for the demand for adaily or weekly newspaper.

The technical and industrialconstraints on the location of thesupply source can imply that asingle supply would be realisable orelse one main supply followed byoccasional restocking, as would bethe case for certain seasonalfashion articles.

When the context is uncertain, the policy evaluation criteriondeserves some thought in order tomake an appropriate choice. In the


Objectives Forecasting demandby period

Forecasting by period and

forecasting by total sales

Forecasting sales overthe entire period

Forecastingrestocking

Statisticalmodels

Smoothing model (Holt, Holt-Winters, trend polynomial, etc.)

Product life curve (Gompertz, logistics, etc.)

Multiple regression with instantaneous cut None

Data used Sales history of the family over 36 months

Sales observed during the first periods

Orders observed and the accumulation of orders for the same period the previous year

Payments

Limits Homogeneity of the behaviour of articles in the family

Rigidrepresentation of sales evolution

Homogeneity of the behaviour from one year to another

Desire for partnershipand the sharing of information

Table 1Typology of forecasting models as a function of the forecasting object


absence of probability to influencethe various levels of demand, it isnot possible to evaluate theprobability of the consequences ofa management rule. The notion ofclassically used mathematicalexpectation is not applicable. It ispossible, in this case, to make useof criteria such as worst imaginablecase evaluation or to measure thespread between the result of thedecision taken and that whichwould have been obtained if, bychance, the best decision had beentaken. One would then adopt thedecision that reduces the worstcase effect. These criteria have thedisadvantage of directing thedecider towards refusal of risktaking and, therefore, to lose theopportunity for possibly associatedgains.

Faced with this observation, it is,explicitly or not, the concept ofmathematical expectation thatguides the choice of action eventhough the probabilities of the levelof demand have been estimated apriori.

The concept of mathematicalexpectation is based on theclassical notion of the mean. Thereis, therefore, an implicit notion ofcompensation, the possible lossesbeing compensated for by thegains. This notion of compensationis entirely pertinent in the case ofcyclic management of multiplesupplies or of the simultaneous orrepetitive management of a line ofreference because it thencorresponds approximately to thereal gain obtained. In the case of asingle supply of a single item, thismean does not correspond toanything real; it must simply be

considered as an abstractquantitative indicator making itpossible to establish a hierarchyamong the various policies.

If the use of mathematicalexpectation is the best choice asthe lesser evil, it still remains todefine what should be measured:the profit margin? the unsold stock?demand satisfaction?

The sales manager will choose theprofit margin, the logistics directorwill choose to minimise the unsoldstock that must be returned, andthe marketing director will opt fordemand satisfaction.

A classical situation

An article is procured at cost (costof production or deliveredpurchase price) pr. This article isresold at selling price pv. Let usdefine the gross profit margin by M = pv - pr. If unsold, the article isput on sale or recycled at a price ps < pr; the cost of the unsold itemis, therefore, I = pr-ps.

What is the optimum size of thesupply lot based on themathematical expectation of gain?The law of demand is assumedknown and defined by itsdistribution function F(L)corresponding to the probabilitythat the demand (not the sales) ofthe article is strictly less than thelevel L.

Let us consider the situationmarginally: the supply lot must beat least of size L if the economicbalance of the supply of the Lth

article is positive.

The mathematical expectation ofthis balance is - For financial receipts: pv(1-F(L)) +

ps F(L) where (1-F(L)) representsthe probability that the demandwill be greater than or equal to L

- For payments: pr

The optimal lot L* is characterisedby the balance between receiptsand payments, that is, pv(1-F(L)) +ps F(L)=pr, which, translated bySilver and Peterson (1985),becomes

(1),

where R= M/I is the ratio of theprofit margin to the cost of theunsold item. The size of the supplylot depends uniquely on this ratio.If this ratio is large, L* approachesthe maximum estimated demand; ifit is small, the lot will be equal tothe minimum demand. Forexample, if this ratio is 4, thesupply should cover the demandwith a probability of 80% with,therefore, a risk of outage of 20%.Let's make it clear that covering thedemand with a probability of 80%does not mean that only 80% of thedemand is satisfied; generally,much more is offered.

For example, for a ratio R = 4 for aproduct whose average expecteddemand is 200 articles for the lifecycle, with a standard deviation of100, covering the demand(assumed to follow a usual law)with a probability of 80% comesdown to supplying 200 + 0.84_100 =284 articles. In this way, 95% of theexpressed demand will be satisfied(Silver & Peterson, 1985; Vallin,2006). The quantity 284 is the sumof the average expected demand

Single demand Repetitive for several references

A single resupply Gastronomic guide, current affairs book, crop choice, Christmas tree

Newspapers

Fashion: seasonal articles, fresh products

Several resupplies Fashion: festive products, rapid technical evolution products

*

Table 2Product typology according to the demand and the frequency of resupply

*Note: We consider that management depending on several resupplies for a sequence of references comes down to classical management.

F(L*) =pv " prpv " ps

=pv " pr

pv " pr + pr " ps=

MM + I

=R

R +1

(200) and a safety stock (84)corresponding to a particularstandard deviation, here 0.84. Thisvalue is obtained by reading a tableof statistics at the chosenprobability of not running out ofstock. In fact, one should notconfuse risk of an outage withpercentage of demand satisfied; itis possible to be out of stock whilehaving ensured an excellent qualityof service in the sense of overallsatisfaction of demand.

Note that knowledge of the totalityof the distribution is notindispensable for solving equation(1); it is enough to estimate thedemand level for which the risk ofan overrun is 1/(R+1). For example,if R = 4, it is necessary to estimatethe thresholds for which thedemand has a 20% chance of notbeing satisfied; more specifically, itis the size of the lot for which 20%of the shops of the same kind willlack sales.

Optimising the profit marginunder the constraint of servicequality

It is possible to simultaneously takeinto account the optimisation of theeconomic calculation and thequality of service. The lot, L*,resulting from the economiccalculation, is obtained according

to the procedure presented in thepreceding paragraph. The lot, Ls,ensuring satisfaction of the demandwith a certain intended probability,is obtained by the classicalmethods of inventory management(Giard, 2003; Tersine, 1994). Themanager will choose the maximumof these two lots. If this is theeconomic lot L* that dominates thesearch for the maximum profitmargin and implies a quality ofservice better than intended, whydeprive oneself?

Otherwise, it is the requirement ofservice quality, which can reducethe value of the economic optimumby the danger of increasing the costof unsold items; the potential costof the intended service can then bemeasured.

In the absence of probability, ascenario approach

In decision theory with anuncertain future, one approachesthe decision process by an“opposed” to nature game(Guerrien, 1995). Here, the deciderhas a choice of the lot size; “nature”controls the level of demand. Weassume that the range over whichthe demand will be expressed ismeasurable, though it may benecessary to increase the range offluctuation. Knowing the cost of

stocking, of supply, of lost sales,and of waste, it is possible tocalculate the economic balance foreach hypothetical lot size and eachdemand scenario. In this demand-lot pivot table, called a performancetable, one can choose the “goodlot”: - The one that minimises the risk of

loss, whatever the level ofdemand: choosing the lot size thatoptimises the consequences ofthe worst-case situation, which isthe prudent attitude

- The one that reduces the loss of profit, calculated for each demandpossibility, by the profit differencebetween the best solution and theone chosen

This rather heavy approachrequires the preparation of aperformance table and is onlyjustified for the most importantreferences (reference “A” inPareto's analysis).

Favouring restocking

Even in the case of shortcommercial or technical life, it isoften possible to plan restocking. Apriori, this restocking can takeplace at any date within theforecasting horizon; it is chosen bytaking into account the distributionof the demand over this horizonwhile keeping in mind productionconstraints.


Time

time

T1 T2

T

stock

stock

Figure 2Comparison between single and double launch policies

This policy has the disadvantage of requiring a supplementaryresupply, which is, however,generally less costly and provides the following importantadvantages: - There will be a lower level of

inventory.- If there is at least a minimum of

flexibility in launching the secondsupply, the safety stock will bedimensioned to cover only thedemand of the second period,which corresponds to a lowerdemand.

See Figure 2 for a comparisonbetween single and double launchpolicies

- Management of the second period depends on knowledge of the firstperiod (adjustment of forecast).

We compare in the following twopolicies for printing an annualguide, re-edited each year. Thedemand is shown for the entireyear but is essentially concentratedduring the first months of theproduct life cycle. Two scenariosare studied: - A single printing intended to cover

the annual demand, which has theadvantage of reducing the fixedlaunching costs but the

disadvantage of having to providefor the risk of running out of stockfor the entire demand

- Printing twice, a first run of 150,000 guides covering the firstfour months of consumption anda second run of 50,000 guidescovering the last eight months ofthe product life cycle

The standard deviation of the totaldemand, measured over thehistory, is 11.5% of the estimateddemand, or 23,000 guides. We assume a certain flexibility withrespect to the start of the secondcampaign (which does not meanthat the lead time will be zero),which allows us to neglect thesafety stock made by the editor forthe first campaign, which will be,therefore, a run of 150,000 guides(average estimated value).

We show in Table 3 the results ofthe two scenarios in economicterms and in quality of service,seen from the editor's point of view.

Not only is the cost of the campaignconsiderably lower for the policy ofrestocking (!50,800 against!60,800) but the quality of serviceis also greater because one expectsa risk of being out of stock by 1,000units in the case of restocking

against 5,300 with the policy of notreprinting.

It may seem paradoxical that thedanger of an outage is lower withthe policy that plans a lower totalrun (102,800 units with twoprintings against 210,200 units).This can be explained in the case oftwo printing runs (a secondprinting is expected), whereascovering the risk is calculated fromthe beginning of the period with nopossibility of catching up in thecase of the single campaign. Thesame phenomenon appears in thecase of a demand that is less thanforeseen.

Management of Production

Mass production is known to beoptimal in terms of productioncosts but is not possible in the caseof short-life-cycle products.Because of the great variety andstrong risk of obsolescence of theseproducts, it is not possible toproduce large volumes and to makeup stocks by anticipation. The riskof unsold items is important in thiscase and leads to a highmanagement cost. As a result, otherways of organising the productionmust be considered. This neworganisation must respond to


Table 3Comparison of policies of edition of an annual guide

the imperatives and specialcharacteristics of short-life-cycleproducts. The most pertinent areflexibility and reactivity. However,the great industrial decisionsremain the same as for the case ofmass production. It is theresponses that are different.

These industrial decisions respondto the following questions:1. Product design: What products

should the company offer?2. Process design: How should they

be manufactured?3. Production capacity: How many

should be manufactured?

We propose, for each of the threestrategic decisions, a solution forshort-life-cycle products. We shallsee that it is essentially necessaryto favour three axes: modulardesign, delayed differentiation, andflexibility.

Modular design

Mass production products aredesigned once and for all and it isnot necessary to regularly reviewtheir design. Short-life-cycleproducts, however, changeconstantly. For example, everyChristmas new models of toys areoffered. But should one then alwaysinvest in highly costly designs withthe danger of a high selling price?Obviously, this strategy cannot beenvisioned. It is necessary to find away to reduce these major designcosts and lead times. Modular design is recommendedfor these short-life-cycle products.Modular products are manu-factured by assembling standardcomponents. The objective of thistype of design is to have standardreusable components for severalproducts. One can thus reduce thenumber of components andincrease the volume of productsper component. This makes itpossible to reduce the risk ofobsolescence and thus reduceinventory costs. Take, as anexample, the Lego line of toys. Thecomponents are standardised asmuch as possible in order to make them interchangeable andcompatible with various models offinished products. Thus, in thecourse of the production process,

there are fewer components for alarge number of finished products.This makes it possible to reduceinventories and the associatedcosts. The design phase of newproducts is thus focused on theoptimisation of the assembly ofexisting components. But it is notalways possible to use only existingcomponents to create newproducts. In this case, twopossibilities can be presented: 1. One can foresee new accessories

in the final assembly phase topersonalise the product.

2. One can create new components as supplements to the existingcomponents.

The uncertainty of sales is thusfocused on the accessories. Let usconsider once more the case oftoys. The body of a doll is reused invarious finished products, whichare then personalised by clothing(dress, trousers, shorts, etc.) andby accessories (hat, handbag,necklace, etc.). The finishedproducts have a short lifetime butcertain components or sub-assemblies have a long lifetime andmay even be standard components.The standardisation of componentsand sub-assemblies is advisable inthe framework of short-life-cycleproducts or for a large variety offinished products. The more thecomponents are standardised themore they will be compatible with alarge number of finished products.Thus, for an average or low numberof finished products, we have alarge volume of a limited number ofsub-assemblies. Standardisation ofcomponents makes it possible tocapitalise on the advantages ofmass production.

Mass production, for example,made it possible for the Ford ModelT (Forza & Salvador, 2007) to sellfor $360 in 1916 when the averagegoing price for an automobile was$2,000. Several companies thenadopted the concept bystandardising not only theirproducts but also their design,their manufacture, and theirdistribution.

One should favour as much aspossible the use of existingcomponents when designing new

products. There is a threefoldinterest in this approach: 1. To reduce design and

manufacturing lead times: whenusing existent components, thereis no need to look for newsuppliers. The existing circuitscan then be used.

2. To reduce component stocks 3. To reduce the uncertainty

associated with forecasting thedemand for a short-life-cycleproduct. One can then makeforecasts at the level ofcomponents (which arestandardised, if possible, andbecome part of several finishedproducts).

Process design

We have seen in the previoussection that modular design makesit possible to reduce inventoriesand sub-assemblies as well as thelead time in getting to the market.Nevertheless, a strong uncertaintyremains with respect to thequantity to produce. If the choice isbased uniquely on demandforecasts, one can find oneself inone of the following situations: lotsof unsold items if the demand isoverestimated or outages if thedemand is underestimated. Howcan we configure the manufacturingprocess in order to reduce thisuncertainty?

Delayed differentiation isconsidered in the general case ofstrong variation and in theparticular case of short-life-cycleproducts. Figure 3 shows thisconcept. It consists of placing thedifferentiation of products as fardownstream in the industrialprocess as possible. This practicetends to favour overall productivitywhile minimising forecasting risks.The moment at which the productsare differentiated is called the orderpoint position or the externalsynchronisation point (Wang and al.,2010). It is the point thatdistinguishes the phase ofmanufacturing by anticipation(constitution of the stock ofundifferentiated products) from the phase of manufacturing for an order received, when the components and accessoriesare added to the product


for differentiation. Productionplanning for the first phase ofmanufacturing is carried out basedon demand forecasts. Theseforecasts are made for theundifferentiated products, whichare fewer in number than thefinished products. In this way, theforecasting uncertainty is reduced.The planning of the second phase iscarried out based on firm orders.The market lead time representsthe waiting time that the clientmust expect for delivery.

Research on delayed differentiationhas evolved since Alderson (Garcia-Dastugue & Lambert, 2008) in twodirections: the first deals withdelayed differentiation at the levelof manufacture and the secondwith geographical and logisticaldelayed differentiation.

The objective of delayeddifferentiation is to move thedifferentiation point as close to themarket as possible. In certaincases, some activities have beenmoved from the factory to thewarehouse. The ready-to-wearcompany Zara manufactures itsproducts without labels and sendsthem to the distribution centre,which finalises the product byadding labels. Thus, eachwarehouse becomes a productionsite. This task can be performed bythe logistical service providers.

Production capacity

Several research projects havedealt with the planning ofproduction capacity in an uncertain

environment. We will not considerthe solution to this problem in thisarticle. For more detailedinformation on the subject, seeKazemi Zanjani and al. (2010) andLucas and al. (1999). We will ratherconsider the strategic decisions tobe taken upstream to solve theproblem of capacity in the case ofshort-life-cycle products.

Modular design and delayeddifferentiation, see (Gupta &Benjaafar, 2004), make it possible toreduce product manufacturingcosts by taking advantage of massproduction. Production planning isthus done at the level of sub-assemblies, which are fewer innumber than the finished productsand have a relatively importantvolume. This planning is refined bymaking use of market informationdealing with the volume of finishedproducts to produce in a time lapseacceptable to the client. Certainstudies show that the reliability offorecasts increases when use ismade of sales history over the twofirst weeks (Marshall & Ananth,1999). In fact, one can, in this case, reduce the forecasting errorfrom 50% to 10%. This recentinformation on the sales volume isnot enough, by itself, to adapt tothe demand. The production toolhas to be flexible in order to exploitthis fresh information and respondto the demand with a short leadtime.

Flexibility can be defined(Krajewski and al., 2004) by thecapacity of an organisation torapidly and efficiently adapt to the

needs of a client. There are twotypes of flexibility: product orservice personalisation and volumeflexibility.

• Personalisation is based on the ability to satisfy the unique needsof each client by participating inthe design of products andservices.

• Volume flexibility is the capacity to accelerate or slow down theproduction flow reactively inorder to adapt to strongfluctuations in demand.

Flexibility can also be defined(Everaere, 1997) as a “capacity toadapt under the double constraintor uncertainty and urgency.”

Reviewing the literature, we havefound several definitions offlexibility. We will, above all,consider various means of attainingthis flexibility. These means can beclassified in three categories: theproduction tool, the staff, and theorganisation.

The production tool must bereactive and adapt to an increase ora decrease in production. For that,it is necessary that themaintenance be well planned andregular in order to avoid as muchas possible random shutdowns.Reliability of the tool is alsonecessary, which makes possiblethe manufacture of high-qualityproducts with a minimum of waste. The staff should be highlycompetent, making it possible tomanage and adapt rapidly tocomplex situations. First-levelmaintenance, for example, can beperformed by the operatorsdirectly to avoid long machineshutdowns. For that, it is necessarythat the operators be qualified andregularly trained. Among thesetasks are simple adjustmentswithout disassembling or openingthe equipment; replacingaccessible consumable elements,such as display lamps, oil, filters;and so on. It is also necessary togive the staff greater autonomy (ifthey are qualified and well trained).They will then be in a position totake decisions rapidly.


Figure 3Delayed differentiation

The organisation should bedecentralised to shorten thedecision-making circuits and toadapt reactively to demand. It isalso necessary that it be a source of development for the staff byproviding more autonomy.Management must have, therefore,a strong will to delegate certainresponsibilities. Modulation ofworking time can be negotiated, ifnecessary. For example, use may bemade of overtime, partial layoffs,working in teams (evenings andweekends), and timing of vacations.The employees thus accept to taketheir holidays when there is littleactivity and, inversely, to worklonger hours during the highseason.

We have seen, in general, thevarious means to achievingflexibility. It is, however, an illusionto think that this general approachwould be suitable for allcompanies. Each company has tobe considered in its context to findthe flexibility that is appropriate toit. To this effect, it is necessary tostudy carefully its environment andconstraints in order to providepersonalised solutions. In theseconditions, one does not have to beflexible in everything; it is onlynecessary to be flexible to theextent required by the company'sown environment (Carr & Lovejoy,2000).

Conclusion

After having presented thespecifics and problems of short-life-cycle products, we have discussedthe literature review based onforecasting demand, inventorymanagement, and the managementof production. We have alsopresented the results of two studiesthat we have conducted onforecasting demand for a CD andthe policy of printing an annualguide. The absence of sales historyin the case of short-life-cycleproducts does not allow the use ofclassical models for calculatingforecasts. Specific models werepresented: the Gompertz modeland the logistical model.

The application of the Gompertzmodel for forecasting the demandfor a CD showed good results but it

has certain limitations due to thefact that it assumes that theevolution of sales will obey aparticular form (even if theparameters allow great variety),which can sometimes be affectedby exceptional events (promotions,outages, etc.).

For inventory management, twopolicies can be considered: a singlesupply or two. The results of thestudy conducted on the printingpolicy of an annual guide showedthat the total cost of the secondpolicy of printing twice is lower. Infact, this strategy makes it possibleto better control variations indemand and to make the necessaryadjustments.

As for the management of theproduction of short-life-cycleproducts, it can be based on thefollowing triptych: modular design,delayed differentiation, andflexibility.

If there is any particular insight tobe stressed from the presentarticle, it is that the supply chain ofshort-life-cycle products should beconceived as more than justapplying a forecasting, inventoryand production techniques andmeasuring the accuracy of thattechniques. Indeed, the supplychain of short-life-cycle productsshould be viewed as a processcomprising the use of multipletechniques and the involvement ofmultiple departments with the goalto achieve “optimal” supply chain.Although more research is neededto delineate the most preferablesupply chain for short-life-cycleproducts, it is hoped that thepresent article has provided afoundation on which to base suchresearch and stimulate ongoingdiscussion of the supply chain ofshort-life-cycle products.

References

Abbas, A. K., & Hirofumi, M. (1996).Forecasting and inventory managementof short life-cycle products. OperationsResearch, 44(1), 131-150.

Anand, K., Anupindi R. and Bassok Y.(2008). Strategic inventories in verticalcontracts. Management Science, 54(10),1792-1804.

Baglin, G. and al. (2007). Managementindustriel et logistique (5th ed.). Paris:Economica.

Bourbonnais, R., & Usunier, J. C. (2007).Prévision des ventes (4th ed.). Paris:Economica

Carr, S., & Lovejoy, W. (2000). Theinverse Newsvendor problem: Choosingan optimal demand portfolio forcapacitated resources. ManagementScience, 46(7), 912-927.

Decker, R., & Gnibba-Yukawa, K. (2010).Sales forecasting in high-technologymarkets: A utility based approach.Journal of Product InnovationManagement, 27(1), 115-129.

Everaere, C. (1997). Management de laflexibilité. Paris: Economica.

Forza, C., & Salvador, F. (2007). Productinformation management for masscustomization. Basingstoke: PalgraveMacmillan.

Garcia-Dastugue, S. J., & Lambert, D. M.(2008). La différentiation retardéeinterorganisationnelle dans la supplychain. Logistique & Management, 16(1),31-46.

Giard, V. (2003). Gestion de la productionet des flux (3rd ed.). Paris: Economica.Greene, W. H. (2005). Econometricanalysis (5th ed.). Upper SaddleRiver:Prentice Hall.

Guerrien, B. (1995). La théorie des jeux.Paris: Economica.

Gupta, D., & Benjaafar, S. (2004). Make-to-order, make-to-stock, or delayproduct differentiation? A commonframework for modeling and analysis.IEE Transactions 36, 529-546.


Kazemi Zanjani, M., Nourelfath M. andAit-Kadi D. (2010). A multi-stagestochastic programming approach forproduction planning with uncertainty inthe quality of raw materials anddemand. International Journal ofProduction Research, 48(16), 4701-4723.

Krajewski, L. J., Ritzman, L. P. andMalhorta, M. K. (2010,) ProductionManagement (9th ed.), Upper SaddleRiver NJ: Prentice Hall.

Lucas, C., Mitra G. and MirHassani S.A.(1999). Supply chain planning underuncertainty (pp. 77-96). In E.Hadjiconstantinou (Ed.), Quickresponse in the supply chain. Springer.

Marshall, M., & Ananth, R. (1999).Managing Short Life-Cycle Products.Ascet,1.

Morisson J. (1996). How to use DiffusionModels in New-Product Forecasting,Journal of Business Forecasting, Summer,6 - 9.

Niu, S. C. (2006). A piecewise diffusionmodel of new-product demands.Operations Research, 54(4), 678-695.

Silver, A., & Peterson, E. A. (1985).Decision systems for inventorymanagement and production planning.New York: John Wiley.

Tersine, R. J. (1994). Principles ofinventory and materials management.Upper Saddle River, NJ: Prentice Hall.

Vallin, P. (2006). La logistique. Paris:Economica.

Wang, F., Lin, J., and Liu, X. (2010).Three-dimensional model of customerorder decoupling point position in masscustomization. International Journal ofProduction Research, 48(13), 3741-3757.

About the authors

Sabrina BERBAIN is an associate professor ofsupply chain management at the ISG BusinessSchool of Paris, and a researcher at the GrIIsG(Groupement de Recherche Interdisciplinairede l'ISG). Her current interests includeproduction planning, short life cycleproducts, and transportation problemspecifically real-time traffic control ofjunction.

Regis BOURBONNAIS is Assistant Professorat Dauphine University and specialized ineconometrics. He is the author of severalbooks on Econometrics and sales forecasting(Prévisions des ventes with J. C. Usinier, 2007,Econometrie, 2009, Analyses des sériestemporelles en Economie, 2010). He also isthe co-director of the Master in Logistics atDauphine University.

Philippe VALLIN was Assistant Professor atDauphine University in Operational Research;he is now consultant in strategy logistic. Hewrote of several books on logistics andinventory management. He is also the co-director of the Master in Logistics atDauphine University.


Documents

Forecasting, Production and Inventory Management of … · Forecasting, Production and Inventory Management of Short ... sales over the entire sales period) 2. Forecasting by period,