On the Development of Theory-Informed Operationalised Definitions of Demand Patterns

Lead Research Organisation: University of Salford
Department Name: Unlisted


Different Stock Keeping Units (SKUs) are associated with different demand patterns, which in turn require different methods for forecasting and stock control. Consequently, we need to categorise the various SKUs and apply the most appropriate methods for each particular category. The way we are going to perform this task has obviously tremendous implications in terms of stock and customer satisfaction and, as such, the relevant rules constitute a vital element of every inventory management system. To deal with this problem people tend to classify, rather arbitrarily, the demand patterns (using rules that, based on experience, work well) and then select the forecasting and stock control methods that perform best in each category. Nevertheless, the choice of the most appropriate forecasting and inventory control methods is the very purpose of conducting any categorisation exercise. Therefore, it is more logical to first compare alternative estimation procedures and stock control models for the purpose of identifying their regions of superior performance and then, based on the results, categorise the demand patterns, rather than working the other way around. A procedure like this one is obviously expected to offer better results. In research conducted with John Boylan and John Croston we developed a theoretically coherent categorisation scheme, along the lines discussed above, for forecasting purposes only. However, stock control issues were not addressed and this is what I would like to do in this proposed research. This research area has attracted very limited academic attention over the years. A reason for that may well be the considerable associated complexity. That is, forecasting and stock control have to be viewed as interrelated functions (as they are in practice) rather than stand-alone modules of a wider solution, and this obviously increases the theoretical complexity of the problem. Although some early work has been done on the interaction between forecasting and stock control, a theoretically coherent approach is still required and this is the first proposal to provide it.The main objective of this research is to produce theoretically sound demand categorisation rules for both forecasting and stock control purposes. To conduct such a project, the input from industry practitioners is very important. In this regard, two companies have been selected as project partners. This collaboration will also ensure that the empirical data required for the purposes of this research becomes available. My philosophical stand-point is positivistic in the sense that universally applicable categorisation solutions are sought to be developed. However, due to the complexity of the problem, the research strategy employed cannot be purely deductive. An iterative procedure between theory and data is to be introduced and such an approach will ensure that all important factors are identified.In summary, the proposed research deals with an issue that is worth investigating from both a theoretical and practitioner perspective. Very little work has been conducted in the area of demand categorisation and, from the research to date, it is not clear how managers should classify demand patterns for forecasting and stock control purposes. The importance of this issue has been reported on numerous occasions, and what is agreed upon in the relevant literature is the immediate need to further advance knowledge in this area and empirically assess the relevant issues. The proposed research therefore constitutes a very timely project. The results of such a project will find application in all forecasting and stock control software package manufacturers. Indeed, there is also a natural application to any industrial setting where an in-house developed or bought-in demand classification computerised solution is in place to facilitate inventory management.


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Lengu D (2014) Spare parts management: Linking distributional assumptions to demand classification in European Journal of Operational Research

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Syntetos A (2009) Demand categorisation in a European spare parts logistics network in International Journal of Operations & Production Management

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Syntetos A (2010) Forecasting and stock control: A study in a wholesaling context in International Journal of Production Economics

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Syntetos A (2010) On the variance of intermittent demand estimates in International Journal of Production Economics

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Syntetos A (2017) Periodic control of intermittent demand items: theory and empirical analysis in Journal of the Operational Research Society

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Teunter R (2011) Intermittent demand: Linking forecasting to inventory obsolescence in European Journal of Operational Research

Description This research resulted in new theory informed fully operationalised rules for demand classification in an inventory context. It demonstrated the very tangible benefits that may be achieved (in terms of inventory costs reductions and service level improvements) through the implementation of new robust categorisation algorithms.
Exploitation Route Amendments of forecasting, inventory and supply chain software when it comes to slow / intermittent demand items.
Sectors Aerospace, Defence and Marine,Electronics,Manufacturing, including Industrial Biotechology,Retail

URL http://www.salford.ac.uk/business-school/research/operations-and-global-logistics-management/on-the-development-of-theory-informed-operationalised-definitions-of-demand-patterns
Description The findings of this project have influenced some major changes into the way forecasting and srock control are conducted in an intermittent demand context in real world practices and have motivated a tremendous amount of further scientific research into this area.
First Year Of Impact 2008
Sector Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology,Retail
Impact Types Economic,Policy & public services

Description Forecasting and Inventory Management: Bridging the Gap
Amount £50,000 (GBP)
Funding ID EP/F012632/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2007 
End 08/2008
Description Brother International Europe Limited 
Organisation Brother Industries
Department Brother International Europe
Country United Kingdom 
Sector Private 
PI Contribution Enabling the participating organisation to obtain insights into (and amend their solution on): i) demand classification for logistics purposes, ii) forecasting of service parts requirements; iii) the way judgement interacts with statistical decision making in inventory and supply chain management.
Collaborator Contribution Data provision, participation in focus group meetings, informal discussions on the development of solutions.
Impact A major joint publication in the International Journal of Operations and Production Management. Details are provided under the outputs of this project.
Start Year 2006
Description CSC Computer Sciences Ltd 
Organisation Computer Sciences Corporation (CSC)
Country United States 
Sector Private 
PI Contribution Offering insights to them into how there may be an improvement in service parts forecasting and stock control.
Collaborator Contribution Data provision and insights into how things operate in practice.
Impact Although the collaboration with the particular organisation did not continue, the collaboration with the company representative to the project is still active and has been extremely successful. The company representative subsequently moved to BAE and then Tata Consultancy Services ans is one of my close Industry Collaborators to date.
Start Year 2006