The Bullwhip

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The bullwhip effect in

intra-organisational echelonsGoran SvenssonSchool of Management and Economics, Vaxjo University, Vaxjo, Sweden

Keywords Logistics, Inventory control, Supply-chain management

AbstractThis research applies the construct of bullwhip effect in a non-traditional context. It isexplored in intra-organisational echelons. It is argued that the bullwhip effect in a companysinventory management of inbound and outbound logistics flows depends in part upon the gapbetween the degree of speculation and postponement of business activities. It is also argued that thebullwhip effect is caused by the value adding of business activities in supply chains. The study showsthat there is a potential bullwhip effect between companies inbound and outbound logistics flows,

i.e. two internal stocking levels. A see-saw model of the bullwhip effect, and a typology of thebullwhip effect in intra-organisational echelons, are introduced. The term reversed bullwhipeffect is also introduced. Finally, a model of the bullwhip effect-scenarios in a dynamic businessenvironment positions these contributions in a wider theoretical and managerial context.

IntroductionSupply chain management (SCM) has been of interest for many years inliterature (Oliver and Webber, 1992; Jones and Riley, 1985, 1987; Houlihan,1985, 1987; Snowdon, 1988). Stock (2000) states that SCM is an influentialingredient in todays literature and thinking in the field of logistics. Themanagement of multiple relationships across the supply chain is often referred

to as SCM (Lambert et al., 1998). Alderson (1957, 1965) recognises theinterdependence between companies business activities in marketingchannels. Forrester (1958) also acknowledges the linkages between businessactivities in marketing channels, e.g. in terms of the interactions between theflows of information, materials, money, and manpower, and capital equipment.Furthermore, Weld (1916) stresses the importance of addressing thedistribution channel as a whole. SCM addresses the supply chain from thepoint of origin to the point of consumption (Mentzer et al., 2001; Lambert, 1992;Cavinato, 1992). Furthermore, SCM requires co-operation and co-ordinationbetween companies activities and resources in a supply chain (Xu et al., 2001;Holmstrom, 1997). Otherwise, the variability of business activities in a supplychain tend to be amplified as it is moved upstream in the supply chain (Towill,1996; Lee and Billington, 1992).

Lee et al. (1997a) write that the variance of orders may be larger than that ofsales and the distortion tends to increase as one moves upstream in the supplychain. Lee et al. (1997b) claim that the information transferred tends to bedistorted and can misguide upstream members in their inventory andproduction decisions. This phenomenon is referred to in literature as the

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The bullwhipeffect

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Received September2001

Revised May 2002and October 2002

International Journal of Physical

Distribution & Logistics Management

Vol. 33 No. 2, 2003

pp. 103-131

q MCB UP Limited

0960-0035

DOI 10.1108/09600030310469135


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bullwhip effect (Chen et al., 2000). In fact, practitioners and consultants havestriven to deal with the bullwhip effect, e.g. in the automotive, textile, and retailindustries. In the automotive industry the term just in time (e.g. Sugimoreet al., 1977; Toyoda, 1987) has been used, while in the textile and retailindustries the terms quick response (e.g. Stern et al., 1996) and efficientconsumer response (e.g. Kurt Salmon Associates, 1993; Fernie, 1994) havebeen applied. These terms, or business philosophies, aim at reducing thevariability in supply chains, and in the end improve profitability, reduce costsand increase the overall performance of the supply chain beyond judicialboundaries as a whole.

Research objective and research questionThe bullwhip effect indicates that the inventories in the supply chain tend to behigher upstream than downstream, e.g. they are caused by factors such as

deficient information sharing, insufficient market data, deficient forecasts orother uncertainties. Fransoo and Wouters (2000) writes that the bullwhip effectrefers to increasing variability of demand further upstream in the supply chain,and conclude that the theory of measurement of the bullwhip effect in apractical setting has received limited attention. The research of the bullwhipeffect has considered inter-organisational echelons, such as two echelonsbetween companies (e.g. Yu et al., 2001; Chen et al., 2000; Fransoo and Wouters,2000; Kelle and Milne, 1999), or three/multi echelons between a sequence ofcompanies (e.g. McCullen and Towill, 2001; Jacobs, 2000; Metters, 1997; Leeet al., 1997a, b), in supply chains. There is therefore a need for research of thebullwhip effect on a companys internal inventories, e.g. between a companys

inbound and outbound logistics flows (i.e. two internal stocking levels). Theobjective of this research is to explore the bullwhip effect on inventories ininternal echelons.

The process of rational decision making in companies inventorymanagement is in part based upon the postponement or speculation ofbusiness activities. In some circumstances a company maintains higher levelsof inventories (i.e. speculation), while in others lower levels of inventories arekept (i.e. postponement), in the inbound and outbound logistics flows. Theprocess of rational decision making is also influenced by the companiesbusiness activities adding value in a value chain. Lee et al. (1997a) concludethat the bullwhip effect results from the rational decision making between the

actors in a supply chain (i.e. inter-organisational echelons). This rationaldecision making might also be based upon the relationship between actorswithin a company (i.e. intra-organisational echelons), such as the actors incharge of business activities dealing with procurement and physicaldistribution. There is a need for research to explore the potential bullwhipeffect in intra-organisational echelons. This research is limited to companiesinventories in inbound and outbound logistics flows (i.e. two internal stockinglevels) and has been formulated as follows: Is there a bullwhip effect in the

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inventories between a companys inbound and outbound logistics flows?. Theinventories of an inbound logistics flow are derived from the procurement ofmaterials and components from sub-contractors to be used in production. Theinventories of an outbound logistics flow refer to the point of physicaldistribution of finished goods to satisfy other subcontractors, customer ormarket demand.

Frame of referenceSCM is the overall frame of reference of this research on the bullwhip effect incompanies inbound and outbound logistics flows. SCM used to be simplecompared to what it is today (Levy and Grewal, 2000). Various definitions ofSCM appear in literature (see Table I).

SCM might be seen as a management philosophy that strives to integrate thedependent activities, actors, and resources between the point of origin and the

point of final consumption. This means that SCM comprises different kinds ofdependencies in, between and across companies in marketing channels.Mentzer et al. (2001) argue that the definitions of SCM can be classified intothree categories, namely: a management philosophy; the implementation of amanagement philosophy; and a set of management processes.

Companies atomistic considerations (i.e. sub-optimisation of businessactivities) in a supply chain cause the bullwhip effect to occur. The bullwhipeffect has gained interest in the field of SCM, since SCM requires holisticconsiderations of the business activities in supply chains. The holisticconsideration of SCM from the point of origin to the point of consumption isevident (see Table I). The co-operation and co-ordination between companies

activities and resources is necessary to avoid or minimise the variabilitybetween business activities in the supply chain, such as ordering and sales, andthe inventories in inbound and outbound logistics flows. Otherwise, thebullwhip effect might affect negatively the overall outcome or performance ofthe supply chain. The frame of reference of this research is limited to andunderpinned by the principles of postponement (Alderson, 1950) andspeculation (Bucklin, 1965), the value chain concept (Porter, 1985), and thebullwhip effect (Lee et al., 1997a, b).

The principles of postponement and speculationIt was previously stated that a bullwhip effect between a companys inbound

and outbound logistics flows should indicate a higher level of inventories in theinbound logistics flows than in the outbound logistics flows, e.g. caused byinsufficient market data, deficient forecasts or other uncertainties. It could alsobe explained by the effects or consequences of the principle of postponement(Alderson, 1950) and the principle of speculation (Bucklin, 1965).

Alderson (1950, p. 1) writes: Postpone changes in form and identity to thelatest possible point in the marketing flow; postpone changes in inventorylocation to the latest possible point in time. The postponement of companies

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Source Definition of SCM

Mentzer et al. (2001) The systematic, strategic co-ordination of the traditionalbusiness functions and the tactics across these businessfunctions within a particular company and across businesseswithin the supply chain, for the purposes of improving thelong-term performance of the individual companies and thesupply chain as a whole

Lummus et al. (2001) Includes the logistics flows, customer order management, theproduction processes, and the information flows necessary tomonitor all the activities at the supply chain nodes

Mentzer et al. (2000) The management of close interfirm relationships, and thatunderstanding partnering is important in developingsuccessful retail supply chain relationships

Min and Mentzer (2000) To manage the flow of a distribution channel from the

supplier to the ultimate userLambert et al. (1998) To maximise competitiveness and profitability for the

company as well as the whole supply chain network,including the end-customer

Carter et al. (1995) A co-ordinated approach for managing the flow of goods fromsuppliers to ultimate consumers

Ellram and Cooper (1993) An approach whereby the entire network, from the supplierthrough to the ultimate customer, is analysed and managed inorder to achieve the best outcome for the whole system

Turner (1993) A technique that looks at all the links in the chain from rawmaterials suppliers, through the various levels of

manufacturing, to warehousing and distribution to the finalcustomer

Christopher (1992) Supply chain is the network of organisations that areinvolved, through upstream and downstream linkages, in thedifferent processes and activities that each produce value inthe form of products and services in the hands of the ultimateconsumer

Lambert (1992) The supply chain is a single entity that aims at satisfying theneeds and wants of the customer, and eventually the ultimateconsumer

Cavinato (1992) The supply chain consists of actively managed channels ofprocurement and distribution and that it is made up of agroup of firms that adds value along the product flow fromoriginal raw materials to final customer

Lee and Billington (1992) Networks of manufacturing and distribution sites thatprocure raw materials, transform them into intermediate andfinished products, and finally distribute the finished productsto customers

(continued)

Table I.The meaning ofSCM

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business activities reduces the risk by moving the differentiation nearer to thetime of exchange. It provides a point of departure for a critical examination toenhance the performance of companies business activities, and for a possiblereduction of the bullwhip effect, in a supply chain. Alderson (1950) states thatthe principle of postponement is not an answer to every analytical problem, butonly a major instrument that can be derived from the view that sorting is anessential function by both the seller and the buyer.

Bucklin (1965) argues that postponement of business activities is only half aprinciple and that there must be a converse principle equally significant to a

channel structure, and states: The principle of speculation holds that changesin form, and the movement of goods to forward inventories, should be made atthe earliest possible time in the marketing flow in order to reduce the costs ofthe marketing system. The principle of speculation facilitates a counter-viewin relation to the principal of postponement and enhances a critical examinationto improve the performance of business activities and dealing with thebullwhip effect. Bucklin (1965, p. 28) comments on the combination ofpostponement and speculation of business activities as follows: A speculative

Source Definition of SCM

Scott and Westbrook (1991) The supply chain is used to refer to the chain linking eachelement of the production and supply processes from raw

materials through to the end customer

Novack and Simco (1991) Covers the flow of goods from the supplier through themanufacturer and distributor to the end user

Langley and Holcomb (1992) Focusing attention on the interactions of channel members toproduce an end product or service that will provide bestcomparative value for the end user

Stevens (1990) Controls the flow of material from suppliers, through thevalue-adding processes and distribution channels, tocustomers

Ritchie (1990) Considers the supply chain to be a single entity and arguesthat the end performance of delivering satisfaction to

customers will only be as good as the weakest link in thesupply chain

Ellram and Cooper (1990) An integrating philosophy to manage the total flow of adistribution channel from supplier to ultimate customer

Houlihan (1988) Covers the flow of goods from supplier through manufacturerand distributor to the end user

Jones and Riley (1985) Deals with the total flow of materials from suppliers rightthrough to the end users

Oliver and Webber (1982) The marketing channel should be seen as an integrated singleentity Table I.

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inventory will appear at each point in a distribution channel whenever its costsare less than the net savings to both buyer and seller from postponement. In amanagerial context the ultimate goal is to achieve a balance and harmonybetween the postponement and speculation of ones business activities. Bothprinciples contribute to explain the reasoning behind a companys inventorymanagement, and provide a platform for cost efficient inventory managementin order to deal with the bullwhip effect in supply chains.

The value chain conceptPreviously, it has been stated that a bullwhip effect in a companys inboundand outbound logistics flows should indicate a positive association between thelevels of inbound and outbound inventories, e.g. if the level of inventoryincreases in the outbound logistics flows then the level of inventory alsoincreases in the inbound logistics flows. The disequilibrium between the points

of inventory in a supply chain might be caused by the value adding process incompanies different business activities. Therefore, the occurrence of thebullwhip effect does not necessarily have to do with demand variability. Itcould be explained by the effects or consequences of the value chain concept(Porter, 1985). The value chain concept is a guide or tool for identifyingdifferent ways of creating customer value (Porter, 1985, pp. 33-4): ... the valuechain disaggregates a firm into its strategically relevant activities... A firmgains competitive advantage by performing these strategically importantactivities more cheaply or better than its competitors. Generally, the valuechain concept shows that the value chain may be useful in terms of identifyingand understanding fundamental aspects to reach competitive strengths on the

market, and how these activities are tied together in order to create value forthe ultimate consumer. Specifically, the value chain concept identifiesstrategically relevant activities that create value and costs in a specificbusiness. These value chain activities are divided into two broad types:

(1) primary activities, which involve in the physical creation of the product,its sale, its transfer to the buyer and its aftersale activities; and

(2) support activities, which underpin the primary activities, and each other,by providing purchased inputs, technology, human resources, andvarious company activities.

This research is limited to the primary activities of inbound and outbound

logistics.Often it is argued that each step in the value chain exists because it providesor improves the value or adds value to the product and attributes value to theultimate consumer. Already at the beginning of this century, the idea of thevalue-added process was recognised (Weld, 1916, p. 6): At each step anincrement of value is added by those who handle or transform the product.The value-added approach contributes in part to the understanding of thebullwhip effect between a companys inbound and outbound logistics flows.

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The creation of value in value chains is often expressed as a successive orstepwise process in which value increases along a value chain or consecutivevalue chains, i.e. so-called value systems (Porter, 1985). From a financial pointof view, the inventory cost per unit is lower upstream than downstream, whichmitigates the effects or consequences of increased variability in the inventorymanagement upstream in the supply chain.

The bullwhip effectThe dependencies between actors, activities and resources cause negativeconsequences when variability occurs upstream or downstream in the supplychain. Sterman (1989) illustrates that misperceptions about information maycause human behaviour to over-react. Variability in the business environmentis therefore troublesome to handle in a managerial context. Lee et al. (1997a)state that the variability could be symptoms of excessive inventory, poorproduct forecasts, insufficient or excessive capacities, poor customer servicedue to unavailable products or long backlogs, uncertain production planning(i.e. excessive revisions), and high costs for corrections, such as for expeditedshipments and overtime. Lee et al. (1997b) identify four major causes of thebullwhip effect, namely demand forecast updating, order batching, pricefluctuation, and rationing and shortage gaming. Xu et al. (2001) present otherobservations:

. when the manufacturers forecasting errors are greater than those of theretailers before collaboration, co-ordination will be effective in decreasingthe manufacturers safety stocks;

. when the smoothing constant adopted by the retailer and manufacturer

determines the extent of the effect of co-ordination in terms of reducingboth safety stock and the variances of order releases; and

. when co-ordination is effective in the case of either non-stationary orstationary demand, though in some limiting situations the advantage isgreatly reduced.

The bullwhip effect can be mitigated by reduced lead times, revision ofreordering procedures, limitations of price fluctuations, and the integration ofplanning and performance measurement (Lee and Billington, 1992; Towill,1996; Fransoo and Wouters, 2000). Baljko (1999) writes that the bullwhip effectin the supply chain may be eliminated through measures such as: shared

knowledge with suppliers and customers to better gauge demand, co-operationwith supply chain partners to determine what information is causing anoverreaction, and usage of internet-enabled technology and the application ofthe web to speed communications and improve response time. Lee et al. (1997a)develop a typology, based upon the causes of the bullwhip effect and theremedies to discuss ways of controlling the bullwhip effect. It is based upon theunderlying co-ordination mechanism in terms of information sharing, channelalignment, and operational efficiency. Demand information at a downstream

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site is transmitted upstream in a timely fashion with information sharing. Theco-ordination of pricing, transportation, inventory planning, and ownershipbetween the upstream and downstream actors in the supply chain refers tochannel alignment. Improved performance, e.g. reduced costs and shortenedlead times, may be achieved through increased operational efficiency.

Chen et al. (2000) quantify the bullwhip effect in a two-stage supply chainconsisting of a single retailer and a single manufacturer based upon a modelthat includes two factors, namely demand forecasting and order lead times.This research illustrates that the bullwhip effect can in part be decreased bycentralising demand information. McCullen and Towill (2001) study a three-echelon supply chain consisting of overseas warehouses (US), a centralfinished-goods warehouse, and a UK factory. The results from the supply chainmodelling and the dynamic simulation show four material flow principles,which can be used to reduce the bullwhip effect, namely control system, time

compression, information transparency, and echelon elimination. Kelle andMilne (1999) study the bullwhip effect and consider three basic elements of asupply chain, namely, the purchase order of individual retailers, the aggregateorders of the retailers, and the suppliers ordering/producing policy. Thisresearch illustrates how demand correlation can decrease the variability ofaggregate orders, and how autocorrelation in buyers orders can smooth thesuppliers ordering policy. It is concluded that the negative effect of highvariability and uncertainty can be decreased by small frequent orders.

Metters (1997) considers multiple companies operating as a serial supplychain. In this context, end user demand forms the demand for the last companyin the supply chain, but the demand for upstream companies is formed by the

companies in the immediate downstream supply chain. The demandseasonality and forecast error can increase as one proceeds up the supplychain. The results of the study indicate that the importance of the bullwhipeffect to a company differs greatly depending upon the specific businesscontext. Eliminating the bullwhip effect can increase the product profitabilityby 10-30 percent. Fransoo and Wouters (2000) argue that the increased demandvariability in supply chains, i.e. the bullwhip effect, is troublesome to measurein a managerial context and discusse conceptual measurement problems, anddescribe experiences in handling with some of these problems in industrialprojects. Empirical results of measurements of the bullwhip effect in two

supply chains are presented. The outcome of this research is a method todocument and define various ways of measuring the bullwhip effect. Yu et al.(2001) show the benefits of supply chain partnerships through a case studybased upon information sharing. The supply chain actors may gain benefits interms of reductions in inventory levels and cost savings from formingpartnerships with another. It is argued that supply chain partnerships canmitigate deficiencies associated with decentralised control and reduce thebullwhip effect.

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Xu et al. (2001) examine the improvement of supply chain co-ordinationthrough more effective information exchange and consistent forecasting. Theresult illustrates the negative impact that independent activities performed byactors of a traditional supply chain have on order release volatility and forecast

error volatility. Xu et al. (2001) show when and to what extent order andforecast fluctuations can be controlled through collaboration within the supplychain. The study shows that the bullwhip effect of order releases andamplifications of safety stock increase within the supply chain even when leveldemand patterns with no trend and seasonality are stressed.

MethodologyThis research was performed as a non-sponsored and unsolicited mail survey.Initially, two independent representatives at each company were contacted, inorder to collect separately data for the companies inbound and outboundlogistics flows. The selection of the companies studied was based upon anidentified population (SCB, 1999) in the Swedish vehicle industry (i.e. mostlysub-contractors in the industry).

Companies in the industry having more than 20 employees were included inthe population. The population consisted of 251 companies and thus 502executives were selected for the survey. Two matched questionnaires (i.e. oneeach concerning the inbound and outbound logistics flows) were developed. Eachcompany and each respondent was initially contacted by phone in order to selectthe two most suitable executives at each company for each questionnaire. Aquestionnaire was sent to each of the executives. The selected executives for theinbound questionnaire, which contained items dealing with the inventories in theinbound logistics flows, were mainly the purchasing manager or logistics

manager. In the outbound logistics flows the manager in charge of theproduction or sales in each company was primarily selected.

In a few companies (approximately 10 per cent) a single executive respondedto both questionnaires, due to the lack of other suitable executives available. Inthese cases, the executives received the second questionnaire (i.e. the oneregarding outbound logistics flows) after a delay of two to three weeks in anattempt to have independent observations from these executives regarding theinventories in the companies inbound and outbound logistics flows.

A substantial amount of work was performed in the preparation,implementation and conclusion of the mail survey. For example, eachrespondent was briefly introduced to the research project to stimulate his or

her interest and willingness to participate in the survey. In addition, a brieftelephone interview was performed with each of them (approximately fiveminutes) in order to have a notion about each companys empirical context(i.e. in terms of the inbound or outbound logistics flows). Those executiveswho did not answer the questionnaire were contacted again by telephone inorder to stimulate their interest to fill in the required answers. Thecarefulness in this part of the research led to the achievement of asatisfactory response rate. A total of 93.2 per cent of the companies responded

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to at least one of the two questionnaires. A total of 418 responses (total responserate: 83.2 per cent) was collected from the identified population. The responsescollected for the questionnaires of the inbound logistics flows from sub-contractors were 214 units (response rate: 85.3 per cent). The responses collected

for the questionnaires of the outbound logistics flows to customers were 204 units(response rate: 81.3 per cent).

An analysis of non-response bias was performed in order to clarify if the non-response bias in the survey might affect the results of this study, and if therewere any differences between the companies who answered or participated in thesurvey, and the few who did not. The analysis of non-response bias included allnon-response companies that did not answer either of the two questionnairesused. The principal reasons why they did not participate in the survey was eitherthat they were too occupied at the time of the research, that they had a policy tonever participate in surveys, or simply that they were not interested inparticipating. A non-parametric test (chi square-test: Pearson) was used for the

analysis of non-response bias, using such variables as the number of employeesand the total company sales. There existed no significant difference (significance, 5 per cent) between obtained responses and non-responses.

HypothesesA bullwhip effect between a companys inbound and outbound logistics flowsshould indicate a higher level of inventories in the inbound logistics flows thanin the outbound logistics flows. In addition, a bullwhip effect in a companysinbound and outbound logistics flows should indicate a positive associationbetween the levels of inventories. For example, if the level of inventoryincreases in the outbound logistics flows then the level of inventory alsoincreases in the inbound logistics flows. Therefore, two hypotheses have beenformulated as follows:

H0a. There is no difference between companies inventories in the inboundand outbound logistics flows

H0b. There is no association between companies inventories in the inboundand outbound logistics flows

Empirical findingsA selection of univariate, bivariate, and multivariate statistical techniques was

used to analyse the collected data on inventories from the companies inboundand outbound logistics flows (e.g. Norusis, 1993, 1994). A total of 13 items wereused to measure and estimate the inventories in the companies inbound andoutbound logistics flows (see the Appendix). Initially, these items werestructured according to three pre-specified dimensions, namely inventoryturnover (i.e. B1-B6), lead time (i.e. B7-B11) and inventory trend (i.e. B12 andB13). A variety of items based upon various dimensions have been applied inorder to test the stability and randomness of the collected answers.

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Characteristics of multivariate analysesThe data collected was also analysed statistically using factor analysis. Aconfirmatory approach and an R factor analysis was applied on the collecteddata (e.g. Norusis, 1994 and Hair et al., 1992). A component model was used tosummarise the original variance of the variables in a minimum number offactors. An orthogonal solution was applied to extract the factors in such a waythat the factor axes were maintained at right angles to one another. Theorthogonal approach of Varimax was used to rotate the initial factor solution,which focused on simplifying the columns of the factor matrix. In addition, theorthogonal rotation procedure was applied, since it eliminates the collinearitybetween factors. Factors that have eigenvalues greater than one wereconsidered as significant. These factors have been selected and included in thefinal factor solutions. Factor loadings above 0.3 were interpreted as significantin the tables (Hair et al., 1992, p. 239).

Characteristics of the factor analyses for the inventories in companies inboundand outbound logistics flowsThe factor solutions for the companies inbound and outbound inventoriesaccount approximately for 71.2 per cent to 73.4 per cent of the total variance.The communalities for the variables are within the range from 0.59 to 0.80.Factor loadings above 0.3 are significant (Hair et al., 1992, p. 239). From eachquestionnaire, eight items remain in the final factor solutions. Factor scores arecomputed for each factor in order to be used in the section where theassociation between the companies inventories in inbound and outboundlogistics flows are tested.

Factor analysis inventory items in the inbound logistics flowsThe outcome of the factor analysis (see Table II) of the items in the inboundquestionnaire on inventories turned out to be significant (KMO 0:475;Bartletts test: approx. Chi-square 156:194; df 28; sig: 0:000). Fourfactors were identified:

(1) Factor 1 consists of the variables B1 and B3, which represent thehighest/lowest inventory turnovers in the inbound logistics flows, and islabelled inbound inventory turnover.

(2) Factor 2 consists of the variables B7 and B9, which represent theshortest/longest lead times from sub-contractors, and is labelled inbound

lead times.

(3) Factor 3 consists of the variables B2 and B8, which represent the highestinventory turnovers and the shortest lead times. This factor is labelledinbound turnover/lead time.

(4) Factor 4 consists of the variables B12 and B13, which represent thetrends for the lead times and the inventory levels. This factor is labelledinbound inventory level trends.

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Factor analysis inventory items in outbound logistics flowsThe outcome of the factor analysis (see Table III) of the items in the outboundquestionnaire on inventories turned out to be significant (KMO 0:556;Bartletts test: approx. Chi-square 152:192; df 28, sig: 0:000). Fourfactors were identified:

(1) Factor 1 consists of the variables B1 and B3, which represent thehighest/lowest inventory turnovers in the inbound logistics flows, and islabelled outbound inventory turnover.

(2) Factor 2 consists of the variables B7 and B9, which represent the

shortest/longest lead times from sub-contractors, and is labelledoutbound lead times.

(3) Factor 3 consists of the variables B2 and B10, which represent thehighest inventory turnovers and the longest lead times. This factor islabelled outbound turnover/lead time.

Factor CommunalityItem 1 2 3 4 per variable

B3. Lowest inventory turnovers 0.881 0.021 0.0882

0.031 0.785B1. Highest inventory turnovers 0.852 20.097 20.005 0.055 0.738B7. Shortest lead times 20.025 0.884 0.140 0.062 0.806B9. Longest lead times 20.059 0.788 20.320 0.005 0.727B8. Share of shortest lead times 20.076 20.003 0.822 20.131 0.698B2. Share of highest inventory

turnovers 0.161 20.096 0.738 0.113 0.593B12. Lead time trend 20.038 0.078 0.097 0.821 0.691B13. Inventory level trend 0.060 20.015 20.111 0.801 0.658

Total explained variance perfactor (per cent) 19.2 17.9 17.1 16.9

Cumulative explained totalvariance (per cent) 19.2 37.1 54.3 71.2

Table II.Factor analysis ofinventory items inthe inboundlogistics flows

Factor CommunityItem 1 2 3 4 per variable

B1. Highest inventory turnovers 0.876 20.043 0.063 20.154 0.798B3. Lowest inventory turnovers 0.876 0.037 0.142 0.100 0.799

B9. Longest lead times 20.033 0.879 20.064 0.025 0.778B7. Shortest lead times 0.029 0.808 0.254 0.034 0.719B10. Share of longest lead times 0.003 0.183 0.787 0.169 0.682B2. Share of highest inventory turnovers 0.233 20.014 0.779 20.167 0.689B13. Inventory level trend 20.007 0.083 20.210 0.820 0.723B12. Lead time trend 20.045 20.022 0.221 0.796 0.685

Total explained variance per factor (per cent) 19.9 18.4 17.7 17.5

Cumulative explained total variance (per cent) 19.9 38.3 56.0 73.4

Table III.Factor analysis ofinventory items inthe outboundlogistics flows

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(4) Factor 4 consists of the variables B12 and B13, which represent thetrends for the lead times and the inventory levels. This factor is labelledoutbound inventory level trends.

Differences and associations between inbound and outbound inventoriesThe comparisons between the inventories in companies inbound and outboundlogistics flows are analysed in this section (i.e.H0a andH0b). The differences aretested by the aid of three different statistical bivariate tests (e.g. Norusis, 1993).One parametric test is applied, namely the paired samples t-test. In addition,two non-parametric tests are applied as a complement, namely the sign test andthe Wilcoxon matched pairs signed-ranks test. The associations or the so-calledcorrelations are tested by the aid of three different statistical bivariatecorrelations tests (e.g. Norusis, 1993). One parametric test is applied, namelythe Pearson correlation coefficient. In addition, two non-parametric tests are

applied as a complement, namely the Spearman rank correlation coefficient andthe Kendall rank correlation coefficient.

It has been shown above that different dimensions and various items havebeen used to estimate the inventories in companies inbound and outboundlogistics flows. In addition, different scales have been used in order to evaluatethe stability and the randomness of the statistical outcomes shown inTables IV-VI. The following abbreviations are used:

P1 = paired samples t-test;

W = Wilcoxon matched pairs signed-ranks test;

S1

= sign test;P = Pearson correlation coefficient;

S = Spearman rank correlation coefficient;

K = Kendall rank correlation coefficient;

C = correlation (the direction of a significant association: (+) positive/(2)negative);

* = a significant difference or correlation of 5 per cent or less; and

** = a significant difference or correlation of 1 per cent or less.

The three inventory dimensions in the bivariate analyses are inventoryturnover, lead time and inventory level trend in the companies inbound andoutbound logistics flows. These dimensions are used to categorise the bivariateanalyses performed in order to find the potential differences and the potentialassociations between companies inbound and outbound logistics flows.Significant differences and significant correlations between the inbound andthe outbound inventories of the three dimensions are illustrated in the

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Factor

Association

Inbound

Outbound

P

K

S

C

Inventorytu

rnover

Inventory

turnover

0.8

80**

0.1

01

0.1

42

+

Variable

Difference

Association

Item

Inbound

Outbound

P1

W

S1

P

K

S

C

B1.

Highestinventoryturnovers

65.1

(2)

184.9

(+)

**

**

0.7

15**

0.3

29**

0.4

35**

+

B2.

Shareofhighestinv

entoryturnovers

41.2

(2)

52.7

(+)

**

**

**

0.1

65

0.1

36*

0.1

86*

+

B3.

Lowestinventorytu

rnovers

15.0

(2)

64.5

(+)

*

**

**

0.8

88**

0.2

93**

0.3

76**

+

B4.

Shareoflowestinventoryturnovers

14.7

(2)

19.1

(+)

0.1

63

0.1

41

0.1

87

B5.

Averageinventory

turnover

4.3

(2)

4.5

(+)

0.3

16**

0.2

60**

0.3

16**

+

Note:SeetextforexplanationofTable

Table IV.Inventory turnoversin inbound andoutbound logisticsflows

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Factor

Association

Inbound

Outbound

P

K

S

C

Leadtime

Leadtime

0.2

77**

0.1

66*

0.245*

+

Variable

Difference

Association

Item

Inbound

Ou

tbound

P1

W

S1

P

K

S

C

B7.

Shortestleadtimes

4.5

(+/2)

10.7(+/2)

0.0

46

0.1

87**

0.238**

+

B8.

Shareofshortestleadtimes

23.3

(2)

32.8(+)

*

**

*

0.0

52

0.1

01

0.132

B9.

Longestleadtimes

73.8

(+)

53.4(2)

*

**

**

0.3

38**

0.1

81**

0.254**

+

B10.

Shareoflongestl

eadtimes

17.9

(2)

29.1(+)

**

**

**

0.0

73

0.1

34*

0.178*

+

B11.

Averageleadtime

3.8

(+)

3.5(2)

0.2

14**

0.1

67**

0.206**

+


Table V.Lead times in

inbound andoutbound logistics

flows

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Factor

Association

Inbound

Outbound

P

K

S

C

Trend

Trend

0.1

91*

0.1

36*

0.20

6*

+

Variable

Difference

Association

Item

Inbound

Outbound

P1

W

S1

P

K

S

C

B6.

Trendinventorytu

rnover

4.6

(+)

4.5

(2

)

0.2

30**

0.2

32**

0.25

9**

+

B12.

Trendleadtime

3.5

(+)

3.2

(2

)

*

*

0.2

33**

0.2

26**

0.25

4**

+

B13.

Trendinventorysize

3.6

(2)

3.8

(+

)

*

*

0.2

05**

0.1

76**

0.20

7**

+


Table VI.Inventory trends ininbound andoutbound logisticsflows

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Tables IV-VI. Note that an asterisks (i.e. *) is used to indicate a significancelevel of 5 per cent or less in terms of differences or associations betweenvariables. Two asterisks (i.e. **) are used to indicate a significance level of 1 percent or less. The symbols plus and minus within brackets in the tables (i.e.+ or 2) illustrate if the mean value is lower or higher in the inbound oroutbound logistics flows (see also the Appendix). The factor scores saved in thefactor analyses are used in the bivariate analyses.

There is a higher inventory turnover revealed in the companies outboundlogistics flows than in the inbound logistics flows (see Table IV). In addition,the companies that have a high level of outbound inventory turnover tend tohave a high level of inbound inventory turnover and vice versa.

The lead times are shorter in the outbound logistics flows than in theinbound logistics flows (see Table V). In addition, the companies that haveshort lead times in the outbound logistics flows tend to have short lead times inthe inbound logistics flows, and vice versa.

The inventory level trends are slightly downwards in both the inbound andoutbound logistics flows (see Table VI). In addition, there is an associationbetween the inventory trends in the inbound and outbound logistics flows. Thecompanies overall inventories are higher in the inbound logistics flows than inthe outbound logistics flows.

The empirical findings indicate that the inventories upstream in the supplychain tend to be higher and associated with the downstream inventories in thesupply chain. Apparently, there is a potential bullwhip effect between inboundand outbound logistics flows. This implies that the companies in a managerialcontext have to consider upstream activities in the supply chain when they are

striving to improve their performance in the interface towards their present andpotential customers, and vice versa. Finally, there are significant correlationsbetween the inventory factors identified in the inbound and outbound logisticsflows and the size of the company. For example, larger companies have ahigher inventory turnover and more upward lead-time trends and inventorylevel trends than do smaller companies in the inbound logistics flows. Inaddition, larger companies have a higher inventory turnover, shorter lead timesand more upward lead-time trends and inventory level trends than do smallercompanies in the outbound logistics flows.

Theoretical and managerial implications

The fact that the bullwhip effect potentially exists between companies inboundand outbound logistics flows means that companies apply to a larger extent theprinciple of postponement in outbound logistics flows, and apply to a largerextent the principle of speculation in inbound logistics flows.

The gap between speculation and postponementThe bullwhip effect in a companys inventory management of inbound andoutbound logistics flows depends upon the gap between speculation and

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postponement of business activities (see Figure 1). In a managerial context, thebullwhip effect is eliminated if there is no gap between the degree ofspeculation and postponement of business activities in a companys inventorymanagement of inbound and outbound logistics flows. No gap between thedegree of speculation and postponement of business activities does notnecessarily represent an ideal situation, since the bullwhip effect only refers tothe increased variability upstream in a supply chain. Therefore, the bullwhipeffect has to be judged in the context of the overall performance of inventorymanagement in the supply chain. Other aspects of importance in the inventorymanagement of inbound and outbound logistics flows, beside the bullwhipeffect, are the leanness, responsiveness, and agility of business activities. Theseare crucial issues to be taken into consideration, since there is added a value(and costs too) for each business activity performed in the supply chain.

The rationale for a companys application of postponement (Alderson, 1950)

and speculation (Bucklin, 1965) in the inbound and outbound logistics flowsmight also be explained by the value chain concept (Porter, 1985), whichindicates that there is an adding of value in a companies successive businessactivities. Consequently, the finished goods have generated costs that usuallyrepresent a higher value. The financial cost of the inventories is higher in theoutbound logistics flows, which force companies to be more restrictive inmaintaining these inventories. The value-adding process is more importantthan the bullwhip effect itself.

The see-saw model of the bullwhip effectIn this research, the gap between the degree of speculation and postponement

of business activities in inventory management is assumed to influence thebullwhip effect between a companys inbound and outbound logistics flows.The empirical findings of this research indicate that there are associations anddifferences between a companys inventories in the inbound and outboundlogistics flows, which indicate a potential bullwhip effect. In a managerialcontext it is preferable to maintain a balance between the speculation andpostponement of business activities in order to reduce the impact of thebullwhip effect in the supply chain. The importance of the relationship betweenthe degree of speculation and postponement of business activities in a

Figure 1.The bullwhip effect thegap between speculationand postponement ofbusiness activities

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companys inventory management of inbound and outbound logistics flowsmay be described through the see-saw model of the bullwhip effect (seeFigure 2).

On the one hand, if there is a high degree of speculation (i.e. a low degree ofpostponement) in inbound logistics flows and a high degree of postponement(i.e. a low degree of speculation) in outbound logistics flows, then the bullwhipeffect is high (i.e. an upstream unbalanced see-saw scenario). On the other hand,if there is a low degree of speculation (i.e. a high degree of postponement) ininbound logistics flows and a low degree of postponement (i.e. a high degree ofspeculation) in outbound logistics flows, then the bullwhip effect might also beinterpreted as being high (i.e. an downstream unbalanced see-saw scenario).The latter is a kind of reversed bullwhip effect in a supply chain (see Figure 3).It may occur when there are uncertainties upstream in the supply chain, e.g.limited production capacity, product quality deficiencies, unreliable

deliveries/transports or inaccurate information sharing. Finally, if there is abalance in the degree of speculation (or postponement) between inbound andoutbound logistics flows, then by definition there is no bullwhip effect.

A typology of the bullwhip effectThe bullwhip effect is usually explored in terms of increased upstreamvariability, but under some circumstances the supply chain variability mayalso increase downstream in the supply chain. It is therefore important toextend the meaning of the bullwhip effect to consider both downstream andupstream variability caused by the gap (or unbalance) between companiesspeculation and postponement of business activities in the inventory

Figure 2.The see-saw model of the

bullwhip effect

Figure 3.A typology of the

bullwhip effect basedupon the postponement

and speculation ofbusiness activities in

intra-organisationalechelons

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management of the supply chain. The balance between the degree ofspeculation and postponement of business activities in a companys inboundand outbound logistics flows may reduce the impact on both the bullwhip effectand the reversed bullwhip effect in a supply chain. The latter is an extension ofthe construct of bullwhip effect. In some circumstances, it may be appropriatefor a company to let the bullwhip effect to occur, when, for example, there is atemporary uncertainty upstream in the supply chain due to unforeseen changesin the supply chain network structure. Likewise, it may be appropriate to let thereversed bullwhip effect occur, when for example, there is a temporaryuncertainty downstream in the supply chain due to extraordinary happeningsin the competitive environment in the marketplace. This motivates theintroduction of the construct of reversed bullwhip effect.

A typology of the bullwhip effect is introduced (see Figure 3) that classifies aset of potential bullwhip effects in intra-organisational echelons. The typology

consists of two intra-organisational echelons, namely a companys inbound andoutbound logistics flows. Each echelon is divided into the principle ofspeculation and the principle of postponement of business activities.

The typology of the bullwhip effect focuses on the degree of equilibriumbetween the principles of postponement and speculation in a companysinventory management of business activities in inbound and outboundlogistics flows. The typology consists of four cells (see Figure 3). In each cellthere is illustrated a bullwhip effect. Each cell in the typology has uniquecharacteristics that separate them from each other. On the one hand a bullwhipeffect signifies that the principle of speculation dominates a companysinventory management of business activities to a larger extent in the inbound

logistics flows (i.e. the inventories are higher) than in the outbound logisticsflows (i.e. the inventories are lower). This is the traditional approach of thebullwhip effect in supply chains. On the other hand, a reversed bullwhip effectrepresents a non-traditional approach of the bullwhip effect in supply chains.This signifies that the principle of speculation dominates a companysinventory management of business activities to a larger extent in the outboundlogistics flows (i.e. the inventories are higher) than in the inbound logisticsflows (i.e. the inventories are lower). A no bullwhip effect signifies that there isa balance between a companys inventory management of business activities ininbound and outbound logistics flows. This means that the principle of

speculation (or postponement) dominates equally in a companys inventorymanagement of business activities in inbound and outbound logistics flows. Asindicated previously, the typology may be applicable in an inter-organisationalcontext too. This means that the dimensions may be changed to an inter-organisational context. The dimension of upstream replaces the dimension ofinbound logistics flows. The dimension of downstream replaces the dimensionof outbound logistics flows. The different bullwhip effects are interpreted in thesame way as for the intra-organisational context.

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A model of bullwhip effect scenarios in a dynamic business environmentThe typology of the bullwhip effect (see Figure 3) based upon thepostponement and speculation of business activities in intra-organisationalechelons may be used to classify a focal companys bullwhip effect scenariobetween inbound and outbound logistics flows. It may be used for teaching andtraining purposes, as well as to position and compare the outcome of otherreplicating studies of the bullwhip effect in the automotive industry. Thetypology may be positioned into wider theoretical and managerial contexts,such as the generic dependencies in the business environment and a networkapproach (see Figure 4).

There are three generic categories of dependencies (Svensson, 2002) betweenbuyers and sellers in the marketplace of interest for the typology of thebullwhip effect, namely:

(1) time dependence;

(2) functional dependence; and

(3) relationship dependence.

The relevance of time dependence is motivated by the fact that time issues havebecome increasingly important in the management of recent marketingchannels in different industries that emphasise leanness (Lambert et al., 1998).For example, the automotive industry is influenced by just-in-time principles(Sugimore et al., 1977; Toyoda, 1987). Time dependence may be divided intotime compression and order response on one hand, and agility, the ability tochange direction, on the other. There is also a functional dependence betweencompanies (Bucklin, 1966; Alderson, 1954; Stigler, 1951). Functional

dependence refers to where companies business activities are specialisedand complement each other in channels or networks. There is a relationshipdependence between companies business activities (Hakansson and Snehota,1995; Morgan and Hunt, 1994; Gronroos, 1990; Bucklin, 1966; Alderson, 1954;Stigler, 1951). Relationship dependence refers to business activities beingdependent upon the interaction process between companies in marketingchannels. These generic dependencies create a dynamic business environmenton a micro level. These dependencies influence and may be incorporated intothe context of the typology of the bullwhip effect in Figure 3.

The network model (Hakansson, 1987; Hakansson and Snehota, 1995)

consists of three components, such as actors, activities, and resources. Thismodel contributes to the overall context of the typology of the bullwhip effect.Actors may be a company, a group of companies, an individual, or a group ofindividuals. Activities are different business functions performed in thebusiness environment. Resources are the tangible and intangible assets foractors to perform business activities. This means that actors consumeresources when activities are performed. These components areinterdependent, which means that as one change the others change to some

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extent. These three components influence and may be incorporated into the

context of the typology of the bullwhip effect in Figure 3.

Based upon the previous theoretical frameworks and proposed

incorporations into the context of the typology of the bullwhip effect in

Figure 3, a model of bullwhip effect scenarios in a dynamic business

Figure 4.A model of bullwhip-effect scenarios in adynamic businessenvironment

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environment is introduced in Figure 4. The model considers the genericdependencies between business activities and the components of the networkmodel, in a dynamic business environment. The model becomes dynamic sinceits various components are interdependent with the dimensions of speculationand postponement of business activities. This means that a change in one of thecomponents may have an impact on the others, and vice versa. The modelconsiders the generic dependencies between the business activities, the type oflogistics flows, and the components of the network model, in the businessenvironment.

This model suggests that a bullwhip-effect scenario occurs when thedegree of speculation in the inbound logistics flows in relation to the degreeof postponement in the outbound flows is stronger. The impact of thegeneric dependencies on the actors, the activities, and the resources isstronger in the inbound logistics flows (than in the outbound logistics flows)

based upon high levels of dependencies between them in the dynamicbusiness environment. A reversed-bullwhip-effect scenario occurs when thedegree of speculation in the inbound logistics flows in relation to the degreeof postponement in the outbound flows is weaker. The impact of the genericdependencies on the actors, the activities, and the resources is weaker in theinbound logistics flows (than in the outbound logistics flows) based uponlow levels of dependencies between them in the dynamic businessenvironment. A no-bullwhip-effect scenario occurs when the degree ofspeculation in the inbound logistics flows in relation to the degree ofpostponement in the outbound flows is equal. The impact of the genericdependencies on the actors, the activities, and the resources is equal in the

inbound and outbound logistics flows based upon the levels of dependenciesbetween them in the dynamic business environment.

Conclusions and suggestions for further researchThis research applied the construct of bullwhip effect in a non-traditionalcontext. It was explored in intra-organisational echelons, i.e. two internalstocking levels. It is argued that the bullwhip effect in a companys inventorymanagement of inbound and outbound logistics flows depends in part upon thegap between the degree of speculation and postponement of business activities.It is also argued that the bullwhip effect is caused by the value adding of

business activities in supply chains. The study showed that there is a potentialbullwhip effect between companies inbound and outbound logistics flows. Asee-saw model of the bullwhip effect, and a typology of the bullwhip effect inintra-organisational echelons, were introduced. The term reversed bullwhipeffect was also introduced. Finally, a model of the bullwhip-effect scenarios ina dynamic business environment positioned these contributions in a widertheoretical and managerial context. This model is applicable in an intra- andinter-organisational context.

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Based upon the multivariate and bivariate analyses of this research, the nullhypotheses H0a and H0b. It is concluded that companies inventories aresignificantly higher in the inbound logistics flows than in the outboundlogistics flows. In addition, the variability between the inventories incompanies inbound and outbound logistics flows tends to pull in the samedirection. It is therefore interpreted that the empirical findings of this researchindicate a potential bullwhip effect in intra-organisational echelons based uponcompanies inbound and outbound logistics flows.

This research has been limited to the difference and association between twointra-organisational echelons. The relative change of the size of variabilitybetween inbound and outbound logistics flows has been beyond the scope ofthis research. Therefore, further research might explore this specific limitationin intra-organisational echelons. Further research may also be dedicated toexploring the impact of the principles of postponement and speculation on the

occurrence of the bullwhip effect in intra- and inter-organisational multi-echelons in supply chains across industries. Causes of the bullwhip effect haveto be explored beyond current theoretical contexts, such as the application ofother non-traditional theoretical concepts and frameworks. The dynamicbusiness environment may be considered in terms of the generic dependenciesin a network context.

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Xu, K., Dong, Y. and Evers, P.T. (2001), Towards better coordination of the supply chain,Transportation Research, Part E 37, pp. 35-54.

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AppendixThe univariate outcome for each item for the inbound logistics flows is shown in Table AI, andfor each item for the outbound logistics flows in Table AII. The items have been translated fromSwedish into English. Therefore, minor bias of the significance of each item may have appeared

in the translation from one language to the other.

Item N Mn Me Md Sk Ku

B1. Our companys highest inventory turnoversin the in the inbound logistics flows are . . .? 185 65.1 20 12 7.1 53.8

B2. The highest inventory turnovers in theinbound logistics flows from sub-contractorscorrespond to approximately _____ per centof the total inventories in the inboundlogistics flows 176 41.2 35 10 0.4 21.0

B3. Our companys lowest inventory turnovers in

the inbound logistics flows are. . .

? 178 15.0 2 1 13.0 171.6B4. The lowest inventory turnovers in the

inbound logistics flows from sub-contractorscorrespond to approximately _____ per centof the total inventories in the inboundlogistics flows 169 14.7 10 10 2.8 8.5

B5. How high or low is your companys averageinventory turnover in the inbound logisticsflows? 208 4.3 4 4 20.1 0.1

B6. The trend for our companys inventoryturnover in the inbound logistics flows is . . .? 209 4.6 5 5 20.5 0.3

B7. Our companys shortest lead times in theinbound logistics flows from sub-contractors

are approximately _____ day(s) 191 4.5 3 1 3.7 16.2B8. The shortest lead times in the inbound

logistics flows from sub-contractorscorrespond to approximately _____ per centof total purchases 187 23.3 15 10 1.4 1.2

B9. Our companys longest lead times in theinbound logistics flows from sub-contractorsare approximately _____ day(s) 191 73.8 50 90 3.7 19.5

B10. The longest lead times in the inboundlogistics flows from sub-contractorscorrespond to approximately _____ per centof total purchases 188 17.9 10 10 1.9 4.2

B11. How short or long are your companys

average lead times in the inbound logisticsflows from sub-contractors? 211 3.8 4 4 0.2 0.3

B12. The trend for our companys lead-times inthe inbound logistics flows is . . .? 212 3.5 3 4 0.1 0.7

B13. The trend for our companys inventory levelin the inbound logistics flows is . . .? 211 3.6 4 4 20.9 20.1

Notes: N = number of observations; Mn = mean; Me = median; Md = mode; Sk = skewness;Ku = kurtosis

Table AI.The univariateoutcome ofinventory items inthe inboundlogistics flows

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Item N Mn Me Md Sk Ku

B1. Our companys highest inventory turnovers

in the in the outbound logistics flows are. . .

? 156 184.9 30 52 6.6 51.8B2. The highest inventory turnovers in theoutbound logistics flows to customerscorrespond to approximately _____ per centof the total inventories in the outboundlogistics flows 143 52.7 50 50 20.1 21.3

B3. Our companys lowest inventory turnovers inthe outbound logistics flows are . . .? 148 64.5 4 1 9.1 92.9

B4. The lowest inventory turnovers in theoutbound logistics flows to customerscorrespond to approximately _____ per centof the total inventories in the outboundlogistics flows 142 19.1 10 5 2.2 3.9

B5. How high or low is your companys averageinventory turnover in the outbound logisticsflows? 198 4.5 4 4 20.0 20.6

B6. The trend for our companys inventoryturnover in the outbound logistics flowsis . . .? 198 4.5 4 4 0.1 0.8

B7. Our companys shortest lead times in theoutbound logistics flows to customers areapproximately _____ day(s) 166 10.7 2 1 8.4 78.0

B8. The shortest lead times in the outboundlogistics flows to customers correspond toapproximately _____ per cent of total sales 157 32.8 20 5 0.9 20.5

B9. Our companys longest lead times in the

outbound logistics flows to customers areapproximately _____ day(s) 163 53.4 30 30 3.8 15.7B10. The longest lead times in the outbound

logistics flows to customers correspond toapproximately _____ per cent of total sales 155 29.1 20 5 1.1 20.1

B11. How short or long are your companysaverage lead times in the outbound logisticsflows to customers? 202 3.5 4 4 0.2 20.6

B12. The trend for our companys lead times inthe outbound logistics flows is . . .? 202 3.2 3 3 20.3 0.8

B13. The trend for our companys inventory levelin the outbound logistics flows is . . .? 202 3.8 4 4 0.0 0.3

Notes: N = number of observations; Mn = mean; Me = median; Md = mode; Sk = skewness;

Ku = kurtosis

Table AII.The univariate

outcome ofinventory items in

the outbound

logistics flows

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