Parts verification for multi-level-dependent demand manufacturing systems: a recognition and classification structure

This research has developed and implemented a part recognition and classification structure to execute parts verification in a multi-level dependent demand manufacturing system. The part recognition algorithm enables the parent and child relationship between parts to be recognised in a finite-capacitated manufacturing system. This algorithm was developed using SIMAN simulation language and implemented in a multi-level dependent demand manufacturing simulation model. The part classification structure enables the modelling of a multi-level dependent demand manufacturing between parts to be carried out effectively. The part classification structure was programmed using Visual Basic Application (VBA) and was integrated to the work-to-list generated from a simulated MRP model. This part classification structure was then implemented in the multi-level dependent demand manufacturing simulation model. Two stages of implementation, namely parameterisation and execution, of the part recognition and classification structure were carried out. A real case study was used and five detail steps of execution were processed. Simulation experiments and MRP were run to verify and validate the part recognition and classification structure. The results led to the conclusion that implementation of the recognition and classification structure has effectively verified the correct parts and sub-assemblies used for the correct product and order. No parts and sub-assemblies shortages were found, and the quantity required was produced. The scheduled release for some orders was delayed due to overload of the required resources. When the loading is normal, all scheduled release timing is adhered to. The recognition and classification structure has a robust design; hence it can be easily adapted to new systems parameter to study a different or more complex case

Determination of safety stocks in a lost sales inventory system with periodic review, positive lead-time, lot-sizing and a target fill rate

An approximation for the fill rate, i.e. the percentage of demand being delivered from inventory on hand immediately, is derived for items in a periodic review inventory control system with lost sales. We assume demand is stochastic and discrete, lead-times are positive and replenishments are made in multiples of a given fixed case pack size. Most literature on inventory control systems assumes that unmet demand is backordered.The major reason for this is that the analysis of a general lost sales inventory system is known to be hard. To find an approximation for the fill rate, given a safety stock, we start with existing analytical approximations. By applying linear regression, we slightly modify these existing approximations. The new approximation is tested for a wide set of parameters and performs very well: the average approximation error for the fill rate is only 0.0028 and the standard deviation of the approximation error is 0.0045. Since the approximations are very fast,this result enables inventory controllers dealing with a lost sales inventory system to set safety stocks in accordance with the target service level set by their management in an effectiveway. The results of our study also show that the assumption that the lost sales system can simply be approximated by a backordering system if the target fill rate is at least 95%, may lead to serious approximation errors. These errors are particularly large when the lead-time is large or demand uncertainty is low and when on average there is at least one replenishment order outstanding

The expected outdating versus the fill rate for perishable products in a periodic review inventory system

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Quantifying the potential to improve on food waste, freshness and sales for perishables in supermarkets

\u3cp\u3eThe focus of this paper is on improving the performance of fresh departments in supermarkets by reducing food waste, increasing freshness and/or increasing sales. First, two concepts will be introduced to quantify the improvement potential. Next, these concepts will be applied on empirical data for 3 product categories in 27 stores from 3 large retailers in Europe. The two concepts to quantify the improvement potential are called the Fresh Case Cover and the Efficient Frontier. The Fresh Case Cover is defined as the case pack size divided by the average demand during the store shelf life. A regression analysis shows that this single variable explains 42% of the variation in waste. The Efficient Frontier represents a lower bound on the waste needed in a store for any given On-Shelf Availability (OSA). It is demonstrated how the Efficient Frontier can be used to quantify the benefits from supply chain improvement projects and to evaluate fresh departments within a store. To quantify product freshness, an exact expression is derived and an approximation is developed and tested. To quantify waste an existing approximation is generalized. The results show that the improvement potential is very large. For example, increasing the store shelf life with just one day results in 43.1% less waste and 17% more freshness (or in 3.4% higher OSA) and unpacking in the DC results in 34.8% less waste and 1.6% more freshness (or in 2.0% higher OSA). Improving the store replenishment and execution is especially beneficial for medium and large stores.\u3c/p\u3

Stochastic inventory models for a single item at a single location

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Seriegroottebepaling als capaciteit een dominante factor is

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Prestatiemeting in de transport- en distributiesector

In BRAVO is onder andere onderzocht welke factoren het succes van transport- en distributiebedrijven bepalen. Uit dit onderzoek kwam een vijftal aandachtspunten naar voren, die van belang zijn voor het management van deze bedrijven: concentreer op één/enkele segmenten, zoek de juiste klanten, richt automatisering op een betere service, werk pro-actief aan relaties met klanten en collega’s en last but not least: meet en verbeter uw operationele kengetallen. In dit artikel wordt ingegaan op deze aandachtspunten, waarbij vooral het laatste aandachtspunt ‘het belang van het meten en verbeteren van operationele en daarmee samenhangende financiele kengetallen’ nader wordt uitgewerkt. Er is steeds meer aandacht in de literatuur en in de praktijk voor de samenhang tussen financiële en operationole indicatoren (Jorissen, 1995). In BRAVO is voor de transport- en distributiesector een aantal modellen ontwikkeld met behulp waarvan de relatie tussen operationole en financiële prestatie-indicatoren kan worden weergegeven. AIs illustratie wordt een van deze modellen gepresenteerd

How to use aggregation and combined forecasting to improve seasonal demand forecasts

Standard forecasting methods that are designed to cope with seasonal demand often are no longer applicable in practice. Due to growing assortments and shorter product life cycles, demand data may show too high variation or may be insufficient to construct reliable forecast models at the individual item level. In this article we present alternative forecasting methods that are based on using demand information from a higher aggregation level and on combining forecasts. Sales data from two prominent Dutch wholesalers are used to illustrate the drawbacks of the standard seasonal forecasting methods and to demonstrate the potential of the new methods. The average reduction in forecast error (in terms of MSE) turns out to be three times as large as reported in earlier studies on common seasonal patterns