By Nick T. Thomopoulos
This publication describes numerous quantitative tools which are very important to making plans and keep watch over within the operations of the economic global, from providers to production crops to distribution facilities and to the buyers and shops. the themes contain: forecasting, measuring forecast errors, picking out the order volume, security inventory, while and what kind of stock to fill up, all this for person goods and for a distribution community the place the goods are housed in a number of destinations. additional quantitative equipment are: production regulate, just-in-time, meeting, statistical method regulate, distribution community, offer chain administration, transportation and opposite logistics. The equipment are confirmed, sensible and potential for many applications.
The fabric in Elements of producing, Distribution and Logistics provides themes that individuals wish and will be aware of within the paintings position. The presentation is simple to learn for college kids and practitioners. there's no need to delve into tricky mathematical relationships, and numerical examples are provided all through to lead the reader on purposes. Practitioners might be in a position to follow the equipment discovered to the platforms of their destinations, and the common expert will wish the booklet on their bookshelf for reference. every body in expert enterprises like APICS, DSI and INFORMS; MBA graduates, humans in undefined, and scholars in administration technology, enterprise and commercial engineering will locate this e-book valuable.
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Additional info for Elements of Manufacturing, Distribution and Logistics: Quantitative Methods for Planning and Control
This chapter shows how to estimate the standard deviation for a 1-month forecast error, on the various forecasting methods described in Chap. 1. In addition, the chapter describes another measure, called the coefficient of variation, denoted as cov, that is a relative way to measure the accuracy of the forecasts. The cov is the ratio of the standard deviation over the most current level. The closer cov is to zero, the more accurate the forecast. 00 or higher, the forecast errors are more like an exponential distribution.
1 Forecasts The future months are denoted as τ¼1, 2, . , and the demand history is used to generate the forecasts for each of the future months. A fit of the flow from the history demands is generated and is projected forward to become the forecasts for the future months. The flow pattern is usually from one of the following patterns: horizontal, trend or seasonal. Should forecasts for the 12 future months be needed, the forecasts are then denoted as: fτ τ ¼ 1–12. For notation sake, t ¼ 1 to N identifies the history months, and τ ¼ 1 to N` identifies the future months.
The return quantity is entered in the database as a return demand. The return demand could occur from any of several events: the wrong part is shipped to the customer, the wrong quantity is shipped to the customer, one or more of the units are damaged, and so forth. The demand history should be adjusted prior to forecasting to account for the returned units. In the ideal situation, an algorithm is developed to search the prior months of the demand history to identify when the demand was originally recorded.