IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v263y2023ics0925527323001809.html
   My bibliography  Save this article

Optimal product aggregation for sales and operations planning in mass customisation context

Author

Listed:
  • Sali, Mustapha
  • Ghrab, Yahya
  • Chatras, Clément

Abstract

Sales and operations planning (S&OP) is a multi-actor tactical planning process that balances aggregate demand and capacities to deliver realistic production plans. The way demand is aggregated into product families (PFs) is crucial for the alignment sought by S&OP. The issue of gathering individual products into PFs is particularly challenging in the mass customisation (MC) context where products are depicted through combinations of attributes derived from a set of features. This paper introduces the aggregation scheme concept that refers to the subset of features used to constitute PFs. Several aggregation schemes may be considered with different performances from the perspective of the actors involved in the S&OP. A multi-objective model is designed to identify efficient aggregation schemes considering both sales and operations expectations. The model is implemented on numerical instances from automotive industry. The results highlight the existence of more efficient and balanced aggregation schemes than those currently used.

Suggested Citation

  • Sali, Mustapha & Ghrab, Yahya & Chatras, Clément, 2023. "Optimal product aggregation for sales and operations planning in mass customisation context," International Journal of Production Economics, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:proeco:v:263:y:2023:i:c:s0925527323001809
    DOI: 10.1016/j.ijpe.2023.108948
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527323001809
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2023.108948?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    2. Tavares Thomé, Antônio Márcio & Scavarda, Luiz Felipe & Fernandez, Nicole Suclla & Scavarda, Annibal José, 2012. "Sales and operations planning: A research synthesis," International Journal of Production Economics, Elsevier, vol. 138(1), pages 1-13.
    3. Hax, Arnoldo C. & Meal, Harlan C., 1973. "Hierarchical integration of production planning and scheduling," Working papers 656-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Laurent Lim, Lâm & Alpan, Gülgün & Penz, Bernard, 2014. "Reconciling sales and operations management with distant suppliers in the automotive industry: A simulation approach," International Journal of Production Economics, Elsevier, vol. 151(C), pages 20-36.
    5. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    6. J. Erschler & G. Fontan & C. Merce, 1986. "Consistency of the Disaggregation Process in Hierarchical Planning," Operations Research, INFORMS, vol. 34(3), pages 464-469, June.
    7. Tuomikangas, Nina & Kaipia, Riikka, 2014. "A coordination framework for sales and operations planning (S&OP): Synthesis from the literature," International Journal of Production Economics, Elsevier, vol. 154(C), pages 243-262.
    8. Ozdamar, Linet & Bozyel, M. Ali & Birbil, S. Ilker, 1998. "A hierarchical decision support system for production planning (with case study)," European Journal of Operational Research, Elsevier, vol. 104(3), pages 403-422, February.
    9. Frits K. Pil & Matthias Holweg, 2004. "Linking Product Variety to Order-Fulfillment Strategies," Interfaces, INFORMS, vol. 34(5), pages 394-403, October.
    10. Shanika L. Wickramasuriya & George Athanasopoulos & Rob J. Hyndman, 2019. "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 804-819, April.
    11. Zotteri, Giulio & Kalchschmidt, Matteo, 2007. "A model for selecting the appropriate level of aggregation in forecasting processes," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 74-83, July.
    12. Jornsten, Kurt & Leisten, Rainer, 1995. "Decomposition and iterative aggregation in hierarchical and decentralised planning structures," European Journal of Operational Research, Elsevier, vol. 86(1), pages 120-141, October.
    13. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    14. Dejan Mircetic & Bahman Rostami-Tabar & Svetlana Nikolicic & Marinko Maslaric, 2022. "Forecasting hierarchical time series in supply chains: an empirical investigation," International Journal of Production Research, Taylor & Francis Journals, vol. 60(8), pages 2514-2533, April.
    15. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2020. "Tactical sales and operations planning: A holistic framework and a literature review of decision-making models," International Journal of Production Economics, Elsevier, vol. 228(C).
    16. Paul Zipkin, 1982. "Exact and Approximate Cost Functions for Product Aggregates," Management Science, INFORMS, vol. 28(9), pages 1002-1012, September.
    17. Widiarta, Handik & Viswanathan, S. & Piplani, Rajesh, 2009. "Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework," International Journal of Production Economics, Elsevier, vol. 118(1), pages 87-94, March.
    18. Zotteri, Giulio & Kalchschmidt, Matteo & Caniato, Federico, 2005. "The impact of aggregation level on forecasting performance," International Journal of Production Economics, Elsevier, vol. 93(1), pages 479-491, January.
    19. John Paul MacDuffie & Kannan Sethuraman & Marshall L. Fisher, 1996. "Product Variety and Manufacturing Performance: Evidence from the International Automotive Assembly Plant Study," Management Science, INFORMS, vol. 42(3), pages 350-369, March.
    20. David F. Rogers & Robert D. Plante & Richard T. Wong & James R. Evans, 1991. "Aggregation and Disaggregation Techniques and Methodology in Optimization," Operations Research, INFORMS, vol. 39(4), pages 553-582, August.
    21. Rainer Leisten, 1998. "An LP-aggregation view on aggregationin multi-level production planning," Annals of Operations Research, Springer, vol. 82(0), pages 233-250, August.
    22. Sven Axsäter, 1981. "Aggregation of Product Data for Hierarchical Production Planning," Operations Research, INFORMS, vol. 29(4), pages 744-756, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    2. Vicens, E. & Alemany, M. E. & Andres, C. & Guarch, J. J., 2001. "A design and application methodology for hierarchical production planning decision support systems in an enterprise integration context," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 5-20, December.
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    4. Wochner, Sina & Grunow, Martin & Staeblein, Thomas & Stolletz, Raik, 2016. "Planning for ramp-ups and new product introductions in the automotive industry: Extending sales and operations planning," International Journal of Production Economics, Elsevier, vol. 182(C), pages 372-383.
    5. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    6. Hakeem‐Ur Rehman & Guohua Wan & Raza Rafique, 2023. "A hybrid approach with step‐size aggregation to forecasting hierarchical time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 176-192, January.
    7. Unai Apaolaza & Aitor Orue & Aitor Lizarralde & Aitor Oyarbide-Zubillaga, 2022. "Competitive Improvement through Integrated Management of Sales and Operations," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    8. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
    9. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    10. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    11. 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.
    12. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    13. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2023. "Design of a sales plan in a hybrid contractual and non-contractual context in a setting of limited capacity: A robust approach," International Journal of Production Economics, Elsevier, vol. 260(C).
    14. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).
    15. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2022. "Merging make-to-stock/make-to-order decisions into sales and operations planning: A multi-objective approach," Omega, Elsevier, vol. 107(C).
    16. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    17. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    18. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2020. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," Applied Energy, Elsevier, vol. 261(C).
    19. Noroozi, Sayeh & Wikner, Joakim, 2017. "Sales and operations planning in the process industry: A literature review," International Journal of Production Economics, Elsevier, vol. 188(C), pages 139-155.
    20. Tomokaze Shiratori & Ken Kobayashi & Yuichi Takano, 2020. "Prediction of hierarchical time series using structured regularization and its application to artificial neural networks," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:263:y:2023:i:c:s0925527323001809. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.