Pipeline information survey:a UK perspective

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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 shown (first order and third

    order) and the pipeline lead-time is fixed or

    variable throughout a simulation run. Best

    and worst case designs are shown for dierent

    average pipeline lead-times (Tp = 4 and 8

    weeks) with proportional (P) and pro-

    portional/integral (P and I) control. Under

    these scenarios the OPL and a lead-time esti-

    mate is fed back into the ordering rule. The

    latter is depicted under two extreme cases; the

    proactive system feeds back any lead-time

    change as it happens, while the reactive system

    waits for a product to come out of the pipeline

    before it feeds back the estimate.

    As an example the analysis has shown that,

    for a given target customer service level of

    97.5%, steady-state analysis (the baseline case

    in Fig. 3) misleadingly predicts low stock hold-

    ing requirements at 1.63 weeks worth of stock.

    Dynamic analysis on the other hand shows

    that by considering the pipeline delay a higher

    stock holding is required, although careful de-

    sign minimises requirements [16].

    The theoretical research has shown that

    pipeline information does improve dynamic

    behaviour. The theoretical research has gone

    further than Stermans experiential work by

    also showing the potential for using control

    theory and simulation in an integrated fashion

    Fig. 4. Relationship of MPS to other manufacturing plan-ning and control activities [26].

    Fig. 5. The essential requirements of an MPS [26].

    Omega, Vol. 26, No. 1 119

  • so as to design suitably robust ordering algor-ithms that incorporate pipeline information.


    The need to incorporate the present state ofthe pipeline into ordering rules/decisions hasalso been expressed in the UK by twoComputer Aided Production Management(CAPM) surveys [3, 17]. The surveys indicatethat there is a need in UK industry to developsuitably robust (in the dynamic sense) orderingalgorithms. Such ordering algorithms shouldmake the best use of all available informationwith regards to both exogenous and endogen-ous factors. For example, information aboutthe current order book, forecast demand andthe manufacturing systems state, when coupledwith suitable stock and replenishment policies,aid in the decision making process for pro-duction ordering.

    Within the context of CAPM a theoreticalmodel, shown as Fig. 4, developed byVollman et al. [18] was found to be adequatefor a general framework for the project. Themodel describes a range of planning and con-trol activities that are typically found in manycompanies to varying degrees. In the contextof this model a number of pipelines can beidentified: A production pipeline and a ma-terial supply pipeline. In addition, althoughonly implied in Fig. 4, is a capacity acquisitionpipeline which is the focus of much ofBurchers studies [10].In the context of the present research, the

    major pipeline under consideration is the (in-ternal) production pipeline, which is con-sidered in MPS activities; the (external)material supply pipeline became an issue oncethe survey was underway. The MPS is a man-agement commitment to produce certainvolumes of finished goods in particular timeperiods in the future. Key principles are out-

    Fig. 6. The focus of the company pipeline flow survey.

    Fig. 7. Example of a process flow analysis model developed during company survey.

    Berry et al.Pipeline Information Survey120

  • lined in Fig. 5. The MPS is created for eachfinished goods part number using known cus-tomer orders, sales forecasts and knowledge ofcapacity [20]. According to APICS the successof MRP systems depends greatly on the man-agement of the MPS process; The MPSdrives MRP and consequently the entire pro-duction and inventory management system[19]. Successful management of the MPSdepends on the adoption of a number of keyprincipals as described in Fig. 5.The theoretical studies summarised in

    Section 2 are of considerable importance informalising the MPS process. The MPS pro-cess in many companies is often undertaken inan ad-hoc manner, and often relies totally onthe accumulated experience of a master sche-duler. A number of authors have suggestedthat there is considerable scope for computerbased DSS [11, 20] of which the algorithmsdescribed in Section 2 are an example of.Improvements in company performance as aresult of a DSS have also been described else-where [21].



    The methodology adopted for the companysurvey initially involved drafting a list of com-panies with whom the research team had pre-vious contact and categorising the companiesaccording to their market sector, production

    strategies, product range and logistic pipeline

    structure. It should be noted that the previous

    contact with these companies were in colla-

    borative programmes unrelated to the research

    described in this paper. Thus, although the

    companies were known to the research team,

    they were a random sample from the UK

    industry base with a wide geographical spread.

    The basis of the sample selection was simply

    to determine whether there were any immedi-

    ate beneficiaries from the research.

    A trial interview with company executives

    was undertaken utilising an in-depth question-

    naire developed in-house addressing key issues

    in order to obtain industrial feedback on

    issues such as type and style of questions,

    interview length and whether the categories of

    information exist within one persons knowl-

    edge or with several cognate executives. Based

    on the interviewees recommendations it was

    decided to use a short list of points of discus-

    sion (as given by the headings in Appendix A)

    and to base the interview around the develop-

    ment of a series of simple diagrammatic ma-

    terial flow models.

    The essential ideas behind the interviews

    can be...


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