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  • Pipeline Information Survey:

    a UK Perspective


    University of Wales, Cardi, UK


    University of Wales, Cardi, UK


    University of Wales, Cardi, UK

    (Received June 1996; accepted after revision August 1997)

    Experiential and analytical research output from a number of sources suggests that the inclusion ofpipeline information in an inventory and production (or supply) ordering rule leads to a reductionin production and supply on-costs and improved customer service levels. The evidence is provided byconsideration of the dynamics induced in an ordering system due to existence of delays between anorder being placed and receipt of that order, namely, the pipeline. While the utilisation of pipelineinformation in such Decision Support Systems as the To-Make model is admirable the resourceimplications to a manufacturing enterprise have to be considered. This paper outlines a surveyundertaken of a sample of UK companies in order to determine their understanding of the pipeline,their practices in acquiring pipeline information and their application of such pipeline information.The survey concentrated on interviews with master production schedulers, but also included discus-sions with logistics managers, and was supported by plant visits. Clustering analysis of the surveydata gathered provides evidence that industry realises the importance of monitoring pipeline states,such as the level of orders in the pipeline or the pipeline lead-time, particularly where long and vari-able lead-times are encountered. The particular pipeline of interest to a given enterprise may be in-ternal (production) or external (supply) or both. It is noted that where the pipeline is monitoredthere is often an unfulfilled opportunity for utilising the pipeline information within a robust order-ing strategy. # 1998 Elsevier Science Ltd. All rights reserved

    Key wordspipeline management, material flow survey, master production schedule


    A FOUR MAN-YEAR UK publicly sponsored

    research project, Dynamic Analysis of an

    Adaptive To-Make Model for exploitation in

    Supply Chains and Individual Business

    Policies, has confirmed and reinforced the

    experiential results of Sterman [1] that the in-

    clusion of pipeline information in a master

    production schedule (MPS) ordering rule can

    have a dramatic impact on improving dynamic

    behaviour. The pipeline may be defined as the

    delay between generating an order and the

    receipt of that order into stock.

    The main output of the research has been

    the development of a structured control engin-

    eering and simulation methodology to analyse

    and optimise the To-Make model. The To-

    Make model is an in-house industry devel-

    oped ordering algorithm that utilises pipeline

    informationnamely lead-times and the order

    Omega, Int. J. Mgmt Sci. Vol. 26, No. 1, pp. 115131, 1998# 1998 Elsevier Science Ltd. All rights reserved

    Printed in Great Britain0305-0483/98 $19.00+0.00PII: S0305-0483(97)00041-8


  • in the pipeline (OPL). The theoretical researchwork has shown that incorporation of pipelineinformation within an ordering algorithmleads to improved dynamic behaviour [2] and

    can lead to increased customer service levelswhile minimising stock holding requirements[3, 4]. The research has also shown the import-ance of such pipeline information in minimis-ing the demand amplification eect within a

    supply chain [58].

    Although the theoretical research results aresignificant the ability for manufacturing enter-

    prises in general to generate and properly uti-lise pipeline information has to be considered[9]. Although the utilisation of pipeline infor-mation is admirable the resources to gathersuch information need to be considered.

    The research therefore undertook a surveyof a small sample of UK industry in order todetermine their:

    (1) Understanding of the pipeline: Agreedor at least a substantially common ter-minology would be likely to emergewhich would facilitate the feedback of

    theoretical research results to industrial-ists.

    (2) Practices in acquiring pipeline infor-mation: From the outset of the researchwe wished to test the hypothesis that

    pipeline information was readily avail-able and was being utilised, althoughnot necessarily for MPS purposes.

    (3) Application of such pipeline infor-

    mation: If a company did acquire pipe-line information to what use was itbeing put?

    (4) Industrial environment: We wished todetermine a niche for an ordering al-

    gorithm utilising pipeline information.

    The paper focuses on reporting the pipelinesurvey methodology and its findings. Prior tothis the theoretical implications on dynamic

    behaviour of introducing pipeline informationwithin ordering algorithms is summarised.This is important as it forms the backgroundto the survey itself and also depends on thesurvey output to determine appropriate what

    if? questions.

    A general outline of MPS requirements isalso introduced as it is the MPS function thatis being addressed by the pipeline survey.Also, the interpretation of the survey results isundertaken by drawing on the outputs ofother researchers, in particular Burcher [10, 11]who undertook a survey examining the capa-bilities of 400 companies MRPII systems.


    Sterman [1], through his behavioural studiesof the well known supply chain Beer Game,advocates two pieces of pipeline informationto be utilised in an ordering rule;

    (1) The lead-time between placement andreceipt of orders: To ensure that su-cient orders are received into stock theamount of OPL should be proportionalto the lead-time.

    (2) The amount of OPL within the pipelinewhen reordering: Ignoring the pipeline

    Fig. 1. Block diagram representation of a multi-productcompany incorporating the To-Make ordering control

    model [2].

    Berry et al.Pipeline Information Survey116

  • OPL leads to instability and it is there-fore far better to compensate for thedierence between desired and actualOPL.

    A lack of consideration of the pipeline inthe generation of orders can lead to costlyswings in supply, production and inventory.The consequences can be periods of poor cus-tomer service levels and/or excessive stockkeeping units.Figure 1 gives a block diagram showing the

    major features of a formalised computer baseddecision support system known as the To-Make model. The model was conceived withthe specific objective of achieving high custo-mer service levels without requiring excessivestocks. The model was developed in-house bya South Wales manufacturing company usingsteady-state control theory principals. A num-ber of authors have advocated the use of con-trol theory in the design of production andinventory management [1215]. Control theoryalso formed the basis of the simulation analy-sis undertaken to study and optimise themodel in a dynamic sense.The rules within the model incorporate a

    number of features which we consider to be

    advanced. Desired stock levels are determined

    via a service level requirement based on a stat-

    istical analysis of sales. Production orders are

    calculated according to a rule that utilises a

    stock replenishment policy and a forecast of

    future demand. Furthermore, the ordering rule

    also makes use of information about the level of

    orders still being processed in the pipeline and

    a frequently updated estimate of the pipeline


    A typical rule generated by a MPS may be:

    The order placed on the production shop floor

    is equal to a fraction of the finished goods

    inventory deficit (taken as the dierence

    between a target and the free stock in Fig. 1)

    plus a fraction of the OPL deficit (taken as the

    dierence between a target and the actual

    OPL, or WIP in Fig. 1) plus the average fore-

    cast customer demand (which may be deter-

    mined via exponential smoothing).

    Typical questions, among others, that may

    be asked of such a rule are;

    (i) Should the customer demand be used in

    the rule?

    Fig. 2. Response of a pipeline based ordering algorithm to a step change in demand (a) Factory com-pletion response (b) Free stock response.

    Omega, Vol. 26, No. 1 117

  • (ii) If so, how much smoothing should beapplied to average demand?

    (iii) What fraction of the inventory deficitshould be utilised?

    (iv) What fraction of the OPL deficit shouldbe utilised?

    Example outputs from the theoreticalresearch are shown in Fig. 2 [2] and Fig. 3

    [16]. Figure 2 shows the impact on the

    dynamics of production completions and stock

    by varying the proportion of OPL error within

    an ordering rule. As seen in Fig. 2a, increasing

    the fraction of the OPL error smooths the

    production response. The extreme case where

    there is no OPL feedback yields the fastest re-

    sponse but at the expense of a higher degree

    of demand amplification. A smoother pro-

    duction completions response unfortunately

    Fig. 3. Customer service levels for a pipeline based ordering algorithm for various operating scenarios.

    Berry et al.Pipeline Information Survey118

  • yields a slower stock recovery as seen inFig. 2b, with the greater risk of stock out.This dichotomy is overcome by careful designin choosing a compromise value of the frac-tion of OPL error.Figure 3 shows a greater degree of sophisti-

    cation in simulation analysis. The three graphsshow the relationship between customer ser-vice level (PIC rate) and weeks worth of stock

    for dierent scenarios. Two dierent pipeline

    representation are show


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