This project deals with the problem of the Bullwhip effect, i.e. that of magnifying inventories as we move backwards in a supply chain. This effect has been highlighted in the academic literature for many decades and, admittedly, it has attracted a tremendous amount of scientific research. However, all the relevant projects have focused on only a limited number of the contributory factors, with no integrative model having yet been established. Despite the importance of such work for developing our understanding of the behaviour of supply chains, the problem still prevails in industrial applications. The fragmented approach to the relevant problem solving may well be explained in terms of the considerable associated complexity. That is to say, the contributory factors (e.g. demand signal processing, rationing/shortage gaming, order batching and price fluctuations) have to be viewed as interrelated components (as they are in practice) rather than stand-alone issues of a wider formulation, and this obviously increases the theoretical complexity of the problem. Analytical solutions cannot be developed unless some of the factors that contribute to the effect are isolated and tackled separately. In addition, and in order to facilitate the mathematical treatment of the problem, many assumptions need to be made, the validity (or universal applicability) of which has also been questioned. It is viewed as imperative to introduce novel holistic approaches in order to solve complex supply chain/ inventory problems, such as the Bullwhip effect. These complex problems demonstrate the industrial importance of inventory management and the considerable benefit that their solution may offer to modern organizations. Such a solution necessitates an inter-disciplinary approach to problem formulation and modelling and this is what out proposal introduces. There is evidence to suggest that the integration of System Dynamics models and cognitive maps constitutes a very promising approach to group model building and is particularly appropriate for supply chain models with significant behavioural components. The aim of this dedicated 3-month project is to re-conceptualise the complex problem of the Bullwhip effect through the integration of Cognitive Mapping and System Dynamics (stock and flow) modelling. Such an approach allows for the consideration of behavioural factors in addition to exploring interactions. Two such factors are highlighted in our research: i) judgemental changes to forecasts and ii) judgemental changes to replenishment orders. Both have been shown to prevail in industrial practices and they are appropriately incorporated in our models. In addition to researching the extent to which such models represent reality, we also intend to explore their potential to act as a training tool for those new to judgemental forecasting and, in the longer term, to change company policies.

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.

The value of remanufacturing is estimated at £2.4B to the UK economy, potentially increasing to £5.6B in the near future. The entire process relies upon the timing, quantity and quality of the returned items (cores), and yet there have been no studies to-date that look at returns forecasting and how such forecasts can be integrated in a systemic way with inventory and production optimisation (IPO) procedures. Such procedures are stepping-stones towards financial, environmental and societal sustainability. If supported, this is the first study to look at these issues and therefore would make a considerable contribution to the theory and practice of remanufacturing in the UK. Our vision is to create a sustainable and resilient world where remanufacturers and their closed-loop supply networks have 'visibility' of product returns and reflect such information into circular economy (CE) compatible IPO to improve sustainability and resilience. In a remanufacturing context, the bill of materials loses its original meaning, and greatly depends on the state of the returned-used items. This introduces a need to forecast not only the timing and volume of the returns, but also their quality, in order to decide: i) what parts need to be replaced for the item to be restored to the desired state? ii) which usable parts can be fed back into the manufacturing process when restoring the item is not economically or practically viable? Rate of returns is expected to strongly correlate to the number of items in use and the stage in the item's life cycle. In-use product data, service information and judgmental inputs should also have explanatory power while time series effects, e.g. seasonality, may also be present. The above make the utilisation of classic demand forecasting methods impossible, calling for novel estimation approaches. Despite the obvious importance of returns forecasting in a CE context, the relevant literature is extremely limited. Further, the uncertainty associated with returns does not imply that the classic demand uncertainty for (re)manufactured products is not present, leading to what may be termed a 'two-tailed uncertainty'! Critically, the foregoing forecasting problems translate into systemic IPO challenges. A growing body of literature looks at inventory and/or production problems in closed-loop supply chains. Interestingly though, all these works are conditioned to no uncertainty with regard to returns and thus no need to forecast them, obviously diminishing the practical utility of these solutions. Integrating returns and demand forecasting with IPO requires a holonic approach, not previously attempted. A holon is an element that is both a whole in its own right but also part of a wider system - for example, in any organisation each department may establish its own strategic priorities but potentially they could act in conflict with each other if there is no general higher level organisation strategic direction to optimise their interactions. Hence, each different forecasting protocol, inventory controller and production ordering rule has its own dynamic properties but which, when integrated in different combinations, creates a new whole that may not be the simple addition of the different parts. Therefore, we will develop appropriate forecasting protocols and integrate them into IPO through systems modelling. Inventory and production optimisation in the CE are stepping-stones towards: i) immense inventory reductions and space liberation, resulting in reduced supply chain costs and cheaper, more affordable products in the market (financial sustainability); ii) reduced obsolescence risk for materials, parts and finished items, with huge implications for environmental sustainability; iii) greater availability of remanufactured products, creating a more ethical marketing channel to consumers (societal sustainability).