style="font-size:15px;"> 1.2.3 Economic Benefits
Moving beyond the after‐sales industry, and the defence sector, we now examine the potential benefits that may result from intelligent intermittent demand forecasting for the wider economy. Purchased goods inventories and their management are significant concerns for firms wishing to remain competitive and survive in the marketplace. According to the 26th Annual State of Logistics report (CSCMP 2015, statistics referring to 2014), the United States alone has been sitting on approximately $2 trillion worth of goods held for sale. According to the same report, the inventory carrying costs (taxes, obsolescence, depreciation, and insurance) are estimated to be around $0.5 trillion (i.e. about 25% of the value of the goods). The total value of inventory was equivalent to approximately 14% of the US gross domestic product (GDP) in 2014. Although similar statistics have not been given in subsequent publications, the 30th Annual State of Logistics Report (CSCMP 2019) revealed that inventory carrying costs in the United States increased by 14.8% between 2014 and 2018.
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 in managing this inventory. In a make to stock (MTS) environment (discussed in Section 1.4.2), if there is no decoupling in terms of the ownership and location of the inventories, then these indirect costs become more significant the longer the stock remains unsold. The high volumes of stocks of intermittent demand items, and their high risk of obsolescence, should put them very high up the list of priorities for modern businesses.
1.2.4 Environmental Benefits
Obsolescence is a very important topic for supply chain management. The complexity of supply chains, in conjunction with increasingly reduced product life cycles, is resulting in high levels of obsolescence. Molenaers et al. (2012) discussed a case study where 54% of the parts stocked at a large petrochemical company had seen no demand for the last five years. Syntetos et al. (2009b) evaluated the inventory practices employed in the European spare parts logistics network of a Japanese manufacturer. They found one case, reported in Sweden, where some parts in stock had not ‘moved’ at all over the preceding 10 years. The value of the on‐hand excess (spare parts) inventory of the US Air Force, Navy, and Army has been estimated to be of $1.7 billion, $1.4 billion, and $2.5 billion, respectively (GAO 2015). Much of this excess stock is at risk of obsolescence.
When obsolescent (or ‘dead’) stock is created, there is considerable environmental waste. Firstly, there is an environmental cost associated with producing goods that are never used. Secondly, there are environmental costs of transporting these goods to national, regional, or local stocking points. Finally, there are environmental costs of disposing of these stocks. The prevention of the accumulation of dead stock relies on accurate demand forecasts. Consequently, more accurate and robust forecasting methods may be translated to significant reductions in wastage or scrap, with considerable environmental benefits.
1.2.5 Summary
More accurate forecasting of intermittent demand presents organisations with a distinct opportunity to reduce costs and address major issues on their environmental agenda. In the after‐sales context, intelligent intermittent demand forecasting is of paramount importance, as many items have demand patterns that are intermittent in nature. Other inventory settings that are dominated by spare parts (e.g. the military, public utilities, and aerospace) would also benefit directly from more accurate intermittent demand forecasting methods.
1.3 Intermittent Demand Forecasting Software
Given the relevance of intelligent forecasting methods in modern organisations, it is vital that they are included in software solutions. The continuous update of software to reflect research developments in the area of intermittent demand forecasting is of great financial and environmental importance. Forecasting software solutions are briefly reviewed in this section and revisited in greater detail in Chapter 15.
1.3.1 Early Forecasting Software
Early forecasting software solutions in the 1950s and 1960s were based on single exponential smoothing (SES) (a method that is discussed in detail in Chapter 6), meaning that intermittent demand items were not treated any differently from fast‐moving items. SES is a method devised for fast‐moving items that exhibit no trend or seasonality. It is a very practical forecasting method for these items, and is included in the vast majority of (inventory) forecasting software applications. It is still used for intermittent demand, although we shall see in Chapter 6 that it is not a natural method for these items and it does suffer from some major weaknesses.
1.3.2 Developments in Forecasting Software
Software packages have since moved on, with most, but not all, packages offering methods that are designed for intermittent demand. Croston's (1972) method, for example, was developed specifically for intermittent demand items, and is incorporated in statistical forecasting software packages (e.g. Forecast Pro), and demand planning modules of component based enterprise and manufacturing solutions (e.g. Industrial and Financial Systems, IFS AB). It is also included in integrated real‐time sales and operations planning processes (e.g. SAP Advanced Planning and Optimisation [SAP APO] and SAP Digital Manufacturing).
Similarly, more recent developments in demand categorisation (rules that distinguish between various types of demand patterns and signify when a pattern should be treated as intermittent) have also been adopted in some commercial software (e.g. Blue Yonder, Syncron International), allowing their clients the capability to achieve some dramatic inventory cost reductions (Research Excellence Framework 2014). However, the adoption of recent developments has not been widespread, and there are many software packages that have limited functionality. Overall, there have been rather minor improvements in commercial software since around 2000 despite some major improvements in empirically tested theory since that time.
1.3.3 Open Source Software
Another important development, to be discussed in detail in Chapter 15, is the availability of open source software of recently proposed intermittent demand forecasting methods. This enables companies to incorporate them in their own in‐house developed solutions, or for commercial software companies to extend their repertoire of methods more readily. Furthermore, sophisticated database systems are enabling companies to ‘slice and dice’ their data more easily. This means that data may be examined more readily by segments, such as geographical regions or product groupings, in forecasting and planning software (e.g. Forecast Pro, Smoothie). This provides the groundwork for implementing developments in forecasting at different levels of aggregation (to be discussed in detail in Chapter 6). However, whilst software solutions are moving ahead by embracing slicing and dicing, they do not do so in terms of new forecasting methods (including those that take advantage of slicing and dicing). There are significant opportunities offered by open source software and modern data analytics to improve the forecasting functionality of commercial software.
1.3.4
