John E. Boylan - Intermittent Demand Forecasting
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- Название:Intermittent Demand Forecasting
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Intermittent Demand Forecasting: краткое содержание, описание и аннотация
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The first text to focus on the methods and approaches of intermittent, rather than fast, demand forecasting
Intermittent Demand Forecasting No prior knowledge of intermittent demand forecasting or inventory management is assumed in this book. The key formulae are accompanied by worked examples to show how they can be implemented in practice. For those wishing to understand the theory in more depth, technical notes are provided at the end of each chapter, as well as an extensive and up-to-date collection of references for further study. Software developments are reviewed, to give an appreciation of the current state of the art in commercial and open source software.
“Intermittent demand forecasting may seem like a specialized area but actually is at the center of sustainability efforts to consume less and to waste less. Boylan and Syntetos have done a superb job in showing how improvements in inventory management are pivotal in achieving this. Their book covers both the theory and practice of intermittent demand forecasting and my prediction is that it will fast become the bible of the field.” —
, Professor, University of Nicosia, and Director, Institute for the Future and the Makridakis Open Forecasting Center (MOFC).
“We have been able to support our clients by adopting many of the ideas discussed in this excellent book, and implementing them in our software. I am sure that these ideas will be equally helpful for other supply chain software vendors and for companies wanting to update and upgrade their capabilities in forecasting and inventory management.”—
, VP, Research and Development, Blue Yonder.
“As product variants proliferate and the pace of business quickens, more and more items have intermittent demand. Boylan and Syntetos have long been leaders in extending forecasting and inventory methods to accommodate this new reality. Their book gathers and clarifies decades of research in this area, and explains how practitioners can exploit this knowledge to make their operations more efficient and effective.”—
, Professor Emeritus, Rensselaer Polytechnic Institute.
, the traditional formula gives a shortage of five units at the end of the third week. However, this is not accurate. A total demand of 12 can have arisen only from a demand of four in each of the three weeks because the distribution in Table 3.7shows that four is the maximum weekly demand. Therefore, the demand in the first two weeks must have been for eight units, giving a shortage (and backorder) of one unit at the end of the second week if
. The shortage of five units at the end of the third week is actually the sum of one unit backordered in the second week and a further four units backordered in the third week. To count this as five would be to double count the unit that was short in the second week and is still short in the third week.
periods (still assuming that
). However, if there are some backorders, then these should not be added on to any further backorders that may arise in the next period. This motivated the development of a revised formula, proposed by Sobel (2004), for calculating the fill rate when the review interval is of one period:
is the demand over the lead time,
is the demand in the single period just after the completion of the lead time, and the other notation is unchanged. This formula overcomes the problem of double counting if demand is always non‐negative, and is independent and identically distributed. To show how the formula works in practice, we recalculate the fill rate for
using Sobel's formula, keeping the lead time as two weeks, as shown in Table 3.9.
,
,
).




)
)
)
, after depletion of the lead time demand (zero if the lead time demand is for eight units).