An important contributory factor to the bullwhip effect (i.e. the variance amplification of order quantities observed in supply chains) is the replenishment rule used by supply chain members. First the bullwhip effect induced by the use of different forecasting methods in order-up-to replenishment policies is analysed. Variance amplification is quantified and we prove that the bullwhip effect is guaranteed in the order-up-to model irrespective of the forecasting method used. Thus, when production is inflexible and significant costs are incurred by frequently switching production quantities up and down, order-up-to policies may no longer be desirable or even achievable. In the second part of the paper a general decision rule is introduced that avoids variance amplification and succeeds in generating smooth ordering patterns, even when demand has to be forecasted. The methodology is based on control systems engineering and allows important insights to be gained about the dynamic behaviour of replenishment rules. � 2002 Elsevier Science B.V. All rights reserved.

Production, Manufacturing and Logistics Measuring and avoiding the bullwhip effect: A control theoretic approach

A summary of the important issues arising in supply chain research is presented. A review of the various mathematical methods used to model and analyse supply chains is then undertaken. They are categorized as: continuous time differential equation models, discrete time difference equation models, discrete event simulation models and operational research techniques. Each method is then appraised from a system dynamics perspective. To conclude, a judgement is made on whether the merits of operational research techniques justify their predominance in industry over the other methods presented here.

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Modelling the dynamics of supply chains

I Background.- 1. Introduction.- 1.1 Organization and control.- 1.2 The approach followed in this study.- 2. Theoretical Background.- 2.1 Introduction.- 2.2 System theory, cybernetics and control theory.- 2.3 Organization theory and theory of organizations.- 3. The Field of this Study: Philips Industries.- 3.1 Introduction.- 3.2 History.- 3.3 Philips Industries in the seventies.- 3.4 Some notes on internal international relations.- II The Structure of Industrial Organizations.- Summary of Part II.- 4. Systems.- 4.1 Some concepts from system theory.- 4.2 Hierarchic systems.- 4.3 Stratified hierarchic systems.- 5. Organizations.- 5.1 The structure of organizations.- 5.2 Control in organizations.- 5.3 Selfcontrol and coordination.- 5.4 The 'levers' of coordination: influence and power.- 5.5 Stratified hierarchies in organizations.- 6. Industrial Organizations.- 6.1 Technology.- 6.2 Conversion systems and non-conversion systems.- 6.3 Physical relations between conversion systems.- 6.4 A hierarchic system of conversion systems.- 6.5 An example: Philips Industries.- III The dynamics of complex industrial organizations.- Summary of Part III.- 7. Control in Industrial Networks.- 7.1 Control as interference reduction.- 7.2 Inertia and interference reduction capacity.- 7.3 Controllability.- 7.4 Interference reduction in industrial networks.- 8. The Dynamics of a Network of Demand Servos.- 8.1 Introduction.- 8.2 The demand servo.- 8.3 Philips Industries as a network of demand servos.- 8.4 Business cycles.- 9. The Dynamics of a Network of Production Systems.- 9.1 The generation of cyclic behaviour.- 9.2 A simulation model.- 9.3 Simulation results.- 9.4 The damping of cyclic behaviour.- 10. The Dynamics of a Network of Social Systems.- 10.1 Transfer of interference.- 10.2 Some intra-organizational interfaces of Philips Industries.- 10.3 Examples of transfer of interference.- IV Control systems for complex industrial organizations.- Summary of part IV.- 11. Control-System Structure.- 11.1 A control paradigm.- 11.2 The integral control system.- 11.3 Decomposition and integration.- 11.4 The Aufbau-Ablauf framework.- 12. Coordination.- 12.1 Integration through coordination.- 12.2 Four coordination modes.- 12.3 Mode-1 coordination.- 12.4 Mode-2, -3 and -4 coordination.- 12.5 The coordination m.- 13. The Aufbau.- 13.1 The nature of the Aufbau.- 13.2 Some design considerations.- 14. The Control-Ablauf.- 14.1 Ablauf decomposition.- 14.2 Futurity.- 14.3 Vertical Ablauf decomposition.- 14.4 Horizontal Ablauf decomposition.- 15. Control-System Design.- 15.1 Control in the Large.- 15.2 Design aspects.- 15.3 Integration of control.- 16. An Example: The Prospect Blue-Print.- 16.1 Introduction.- 16.2 The project.- 16.3 The blue-print.- 16.4 Integration concepts in PROSPECT.- V Conclusion.- 17. Summing Up.- 17.1 Organization and control.- 17.2 A system of control concepts.- 18. Proposals for Further Research.- 18. 1 Industrial organizations.- 18.2 Research beyond industrial organizations.- References.- Name index.

On the control of complex industrial organizations

It is vital that auditing tools used by researchers and managers yield valid assessments and meaningful comparisons irrespective of the organisational setting. The Quick Scan Audit Methodology (QSAM) offers such a systematic approach by utilising multiple data collection methods and a codified value stream uncertainty measure to assess supply chain performance. Although previously only employed within the private sector, this article explores the use of QSAM to benchmark the pharmaceutical value streams in eight Australasian public sector hospitals, which are best characterised as having multi-layered and complex subsystems. Transferability of the QSAM methodology to public healthcare supply chains is critiqued and needed modifications are described. The audits also reveal that high levels of system uncertainty are negatively impacting the provision of critically important healthcare supplies.

Methodology challenges associated with benchmarking healthcare supply chains

When seeking to adopt innovative supply chain (SC) best practices from apparently better-performing organisations during re-engineering, it is important to acknowledge that context also impacts performance. Hence, the purpose of this study is to evaluate the SC uncertainty circle model for its potential to yield direct performance comparisons even when the benchmarked organisations are located in different business sectors and economic settings. Using a rigorous well-established audit methodology, longitudinal field research with an engineer-to-order SC company demonstrates how highly effective, potentially transferable best practices can be reliably identified when systems thinking is allied to a context-free uncertainty metric, and how reducing product delivery process uncertainty can itself increase innovation capability.

Systems engineering effective supply chain innovations

https://link.springer.com/content/pdf/10.1057%2Fjors.1968.36.pdf

Rex Acton

Papers

The Design of Production Systems

BISRA Annual Report