John E. Boylan

Intermittent Demand Forecasting


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Specialised software packages offer some forecasting support to companies but they often lag behind new developments. There are great benefits that have not yet been achieved in this area, and we hope that this book will make a contribution towards their realisation.

      There are three main audiences for this book:

      1 Supply chain management (SCM) practitioners, broadly defined, who wish to realise the full benefits of managing intermittent demand items.

      2 Software designers wanting to incorporate new developments in forecasting into their software.

      3 Students and academics wishing to learn and incorporate into their curricula, respectively, the state of the art in intermittent demand forecasting.

      In summary, business pressures to reduce costs and environmental pressures to reduce scrap (often introduced in the form of prescribed policies imposed by national governments or the EU for example) render intermittent demand items, and forecasting their requirements, one of the most important areas in modern organisations.

      There are great benefits associated with forecasting intermittent demand more accurately, and those benefits are far from being realised. This may be explained by the well reported innovation–adoption gap, which arises from the divergence between innovations and real‐world practices. Organisational practices typically lag behind software developments, and software developments typically lag behind the state of the art in the academic literature. It is the aim of this book to bridge these gaps and show how intelligent intermittent demand forecasting may result in significant economic and environmental benefits.

      In the remainder of this chapter, we first discuss in more detail the potential benefits that may be realised through improved intermittent demand forecasting. We then provide an overview of the current state of supply chain software packages and enterprise resource planning (ERP) solutions with regard to intermittent demand forecasting. This is followed by a section where we elaborate on both the structure of this book and the perspective that we take regarding the material presented here. We close with a summary of the chapter.

      Intermittent demand for products appears sporadically, with some time periods showing no demand at all. Moreover, when demand occurs, the demand size may be constant or variable, perhaps highly so, leading to what is often termed ‘lumpy demand’. Later in this chapter, we discuss why forecasting sporadic and lumpy demand patterns is a very difficult task. Specific characterisations of intermittent demand series are considered in detail in Chapters 4 and 5.

      1.2.1 After‐sales Industry

      Intermittent demand items are at the greatest risk of obsolescence. Many case studies (e.g. Molenaers et al. 2012) have documented large proportions of ‘dead’ (obsolete) stock in a variety of industries, with serious environmental implications. However, under‐stocking situations may be as harmful, given the potentially high criticality of the items involved. In civil aviation, for example, lack of spare parts is one of the major causes of ‘aircraft on ground’ events (problems serious enough to prevent aircraft from flying). Badkook (2016) found that a quarter of the aircraft in an (un‐named) airline's Boeing 777 fleet were affected by such aircraft on ground events over a year.

      1.2.2 Defence Sector

      Defence inventories, which are highly reliant on spare parts, have been repeatedly identified as a high risk area with a direct impact on a nation's economy. In the United States for example, the Department of Defense (DoD) manages around five million secondary items. These include repairable components, subsystems, assemblies, consumable repair parts, and bulk items. They reported that, as of September 2017, the value of the inventory was $93 billion (GAO 2019). Although a matter of concern, there had been no substantial reductions in inventory values over the previous decade (being, for example, $95 billion in 2013 and 2010; GAO 2012, 2015).

      A major determinant of the performance of an inventory system is the forecasting method(s) being used to predict demand. Inaccurate forecasts lead to either excess inventory or shortfalls, depending on the direction of the forecast error. Over‐forecasting can lead to holding stocks that are simply not needed. According to the US Government Accountability Office (GAO 2011, p. 11), ‘Our recent work identified demand forecasting as the leading reason why the services and DLA [Defense Logistics Agency] accumulate excess inventory’.

      Unfortunately, progress in improving forecasting and inventory management has been slow in many industries, with the defence industry being a case in point. The GAO of the United States reported, ‘Since 1990, we have identified DoD [Department of Defense] supply chain management as a high‐risk area due in part to ineffective and inefficient inventory management practices and procedures, weaknesses in accurately forecasting the demand for spare parts, and other supply chain challenges. Our work has shown that these factors have contributed to the accumulation of billions of dollars in spare parts that are excess to current needs’ (GAO 2015, p. 2). Progress in inventory management has been made since then, especially with regard to the visibility of physical inventories, receipt processing, and cargo tracking (GAO 2019). These improvements in information systems have led to inventory management being removed from the list of high‐risk areas. However, it is notable that no claims have yet been made for corresponding improvements in demand forecasting.

      1.2.3 Economic Benefits

      These figures show that a huge amount of capital is tied up in warehouses. They also indicate that small improvements in managing inventories may be translated into considerable cost benefits. We should, therefore, not be surprised to learn that firms, from manufacturing to wholesale to retail, are currently intensifying their search for more efficient and effective inventory management approaches. Their aim is to minimise not only their direct investments in purchased goods inventory but also the indirect cost incurred