Hierarchical Demand Planning (HDP) is an intricate part of most companies today. HDP is based on the assumption of independence among variables, and this allows for simple and easy aggregation and separation of plans and data. However, the most commonly used arguments for grouping and subsequent aggregating is shared traits contrary to the assumption of independency. One of the predominant issues is the conflicting objectives on different decision levels. An example of this is found in hierarchical forecasting of demand. When forecasting on e.g. a product family level to establish capacity requirements, the objective is usually to achieve a Mean Error (ME) of zero. This conflicts with forecasting for Demand Planning (DP) purposes on SKU level, where minimization of the Standard Deviation of Error (SDE) might be more important. In this paper these issues are addressed through a simple example of hierarchical forecasting and use of a Goal Programming (GP) approach to satisfy both objectives. It is found that some general guidelines for handling multiple objectives within HDP can be inferred from this, leading the way for a holistic demand planning framework.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References:
Bitran, G. R., E. A. Haas, and A. C. Hax, Hierarchical Production Planning: A Single Stage System, Operations Research, 29, pp. 717–743, 1981.
Bitran, G. R., E. A. Haas, and A. C. Hax, Hierarchical Production Planning: A Two Stage Approach, Operations Research, 30, pp. 232–251, 1982.
Bitran, G. R. and A. C. Hax, On the design of hierarchical production planning and inventory control systems, Bulletin of the Operations Research Society of America, 23, 1975.
Chankong, V., and Y. Y. Haimes, “Multiobjective decision making: theory and methodology”, North-holland series in System Science and Engineering; 8, Elsevier Science, 1983.
Fliedner, G., Hierarchical forecasting: issues and use guidelines, Industrial Management & Data Systems, 101, pp. 5–12, 2001.
Flores, B. E., and D. C. Whybark, Multiple Criteria ABC Analysis, International Journal of Operations & Production Management, 6, pp. 38–46, 1986.
Hax A. C. and J. J. Golovin, Hierarchical production planning systems, Studies in Operations Management, North-Holland, 1978.
Hax, A. C., and H. C. Meal, Hierarchical Integration of Production Planning and Scheduling, in edited by M. A. Geisler, Logistics, vol. 1 of Studies in the Management Sciences, North-Holland/American Elsevier, 1975.
Hendry, L. C. and B. G. Kingsman, Production Planning Systems and Their Applicability to Make-to-Order Companies, European Journal of Operational Research, 6, pp. 1–15, 1989.
Ignizio, J. P., Goal Programming and Extensions, Lexington Books, 1976.
Lee, S. M., Goal Programming for Decision Analysis, First edition, Auerbach Publishers Inc., 1972.
Otto, A., and H. Kotzab, Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain, European Journal of Operational Research, 144, pp. 306–320, 2003.
Silver, E. A., D. F. Pyke, and R. Peterson, Inventory Management and Production Planning and Scheduling, third ed., John Wiley & Sons, 1998.
Theil, H., Applied Economic Forecasting, vol. 4 of Studies in Mathematical and Managerial Economics, North-Holland Publishing Company, 1966.
Vollmann, T., W. Berry, and D. Whybark, Manufacturing Planning and Control Systems, fourth ed., Irwin/McGraw-Hill, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 International Federation for Information Processing
About this paper
Cite this paper
Nielsen, P., Steger-Jensen, K. (2008). Demand Planning & Control – Handling Multiple Perspectives Through a Holistic Approach to Hierarchical Planning. In: Koch, T. (eds) Lean Business Systems and Beyond. IFIP – The International Federation for Information Processing, vol 257. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77249-3_7
Download citation
DOI: https://doi.org/10.1007/978-0-387-77249-3_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-77248-6
Online ISBN: 978-0-387-77249-3
eBook Packages: Computer ScienceComputer Science (R0)