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Customer segmentation, allocation planning and order promising in make-to-stock production

In: Supply Chain Planning

Author

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  • Herbert Meyr

    (Technical University of Darmstadt)

Abstract

Modern advanced planning systems offer the technical prerequisites for an allocation of “available-to-promise” (ATP) quantities—i.e. not yet reserved stock and planned production quantities—to different customer segments and for a real time promising of incoming customer orders (ATP consumption) respecting allocated quota. The basic idea of ATP allocation is to increase revenues by means of customer segmentation, as it has successfully been practiced in the airline industry. However, as far as manufacturing industries and make-to-stock production are concerned, it is unclear, whether, when, why and howmuch benefits actually arise. Using practical data of the lighting industry as an example, this paper reveals such potential benefits. Furthermore, it shows how the current practice of rule-based allocation and consumption can be improved by means of up-to-date demand information and changed customer segmentation. Deterministic linear programming models for ATP allocation and ATP consumption are proposed. Their application is tested in simulation runs using the lighting data. The results are compared with conventional real time order promising with(out) customer segmentation and with batch assignment of customer orders. This research shows that—also in make-to-stock manufacturing industries—customer segmentation can indeed improve profits substantially if customer heterogeneity is high enough and reliable information about ATP supply and customer demand is available. Surprisingly, the choice of an appropriate number of priority classes appears more important than the selection of the ATP consumption policy or the clustering method to be applied.

Suggested Citation

  • Herbert Meyr, 2009. "Customer segmentation, allocation planning and order promising in make-to-stock production," Springer Books, in: Herbert Meyr & Hans-Otto Günther (ed.), Supply Chain Planning, pages 117-144, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-93775-3_5
    DOI: 10.1007/978-3-540-93775-3_5
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    Citations

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    Cited by:

    1. Waßmuth, Katrin & Köhler, Charlotte & Agatz, Niels & Fleischmann, Moritz, 2023. "Demand management for attended home delivery—A literature review," European Journal of Operational Research, Elsevier, vol. 311(3), pages 801-815.
    2. Konstantin Kloos & Richard Pibernik & Benedikt Schulte, 2019. "Allocation planning in sales hierarchies with stochastic demand and service-level targets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(4), pages 981-1024, December.
    3. Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).
    4. Ojha, Divesh & Sahin, Funda & Shockley, Jeff & Sridharan, Sri V., 2019. "Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type," International Journal of Production Economics, Elsevier, vol. 208(C), pages 529-543.
    5. Quante, R. & Fleischmann, M. & Meyr, H., 2009. "A Stochastic Dynamic Programming Approach to Revenue Management in a Make-to-Stock Production System," ERIM Report Series Research in Management ERS-2009-015-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Vogel, Sebastian & Meyr, Herbert, 2015. "Decentral allocation planning in multi-stage customer hierarchies," European Journal of Operational Research, Elsevier, vol. 246(2), pages 462-470.
    7. Ben Ali, M. & D’Amours, S. & Gaudreault, J. & Carle, M-A., 2018. "Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling," Operations Research Perspectives, Elsevier, vol. 5(C), pages 45-58.
    8. Fleischmann, Moritz & Kloos, Konstantin & Nouri, Maryam & Pibernik, Richard, 2020. "Single-period stochastic demand fulfillment in customer hierarchies," European Journal of Operational Research, Elsevier, vol. 286(1), pages 250-266.
    9. Raul Oltra-Badenes & Hermenegildo Gil-Gomez & Jose M Merigo & Daniel Palacios-Marques, 2019. "Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.

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