Production service level

Perhaps the best way to manage product service levels is to take into account both the protit contribution and the individual product demand. 

Production service level measures the fraction of production orders completed on time and in full. 

Logistics—If a company is geographically located such that it can not meet the desired service level and deliveiy requirements of its customers, tolling may provide a local production and distribution site. Shorter transportation routes for hazardous chemicals or wastes may also be a consideration. 

This article gives a short introduction to methods and tools based upon stochastic models that are applicable in supply chain management in order to give the reader a flavor of the potential of such methods. Typical terms we will deal with are service level, lot size, and production capacity. 

Random service levels result when random demands meet available inventories. Throughout this chapter we assume that the available inventory is not random but has a known value. This is justified because in many cases the production process is almost deterministic when compared with the varying demand. 

This section deals with production lines for more than one product. In the process industries it is often a problem to assign the capacity of one production line to several products, all or some of which have uncertain demand. We want to optimize the overall service level for such a production line. 

Consider two products A and B with the same demand of 20 units per time period, the same buffer size of 60 units, and the same production speed and set up time. The only difference is that product A is only sold in single units, but product B has 80% of orders of 1 unit and 20% of orders of 10 units. It is intuitively clear that product B will have a lower service level because is has a larger variance of the demand. It is not immediately clear that both products have different optimal lot sizes. The optimal lot size, i.e., the lot size resulting in maximal service, is 20 units for product A which results in a /3-service level of 98.6% (Figure 6.8). The optimal lot size for product B is 15 units which results in a not particularly good /3-service level of 90.6%. In order to achieve a /S-service level of 98.6% one would need a buffer of size 165 units, with a corresponding optimal lot size of 33. 

Service Level of Two Products which Differ In Order Pattern Only... 

As the service level depends on the production capacity and the production strategy, it is obvious that both should be optimized. This means first to choose the optimal capacities for the production lines and the buffers (warehouses, tanks, silos) when planning a new factory or an extension. 

An individual product servicing allowance of 58 000 for three products, 46 000 for a further three products, 35000 for a further three products, and a 23000 allowance for the 10th and subsequent products. These allowances only apply to products with NHS sales greater than 100 000 per annum. These figures, agreed in October 1999, are subject to adjustment based on level of inflation. 

The only way to avoid this is by strict analysis of the supply chain from the customer order to final product delivery. Definition of the optimized (theoretical) process and sequential work towards a high service level approach allow the identification of gaps, and of opportunities which might not always be the cheapest (ship versus train versus plane) but could be the most effective way to reduce capital costs and shorten planning scope - an important aspect, especially in volatile customer markets with long production processes on the (chemical) supplier side. As in the case of CIP, this needs clear parameters, KPIs, commitment from all players, and regular tracking. The most important parameters are the lead time for all products, optimal lot sizes, replenishment points, and safety inventories. 

While one production head might feel satisfied with a standard fault clearance time of three hours, another might insist on a service level of thirty minutes. 

Critical to understanding customer satisfaction and perceived service quality is a clear understanding of expectations. Expectations are internal standards used by customers when evaluating a product or service. While they may initially influence a decision whether or not to purchase a product and from whom, they also play a critical role in consumers judgments of service quality and satisfaction. Conceptual models have suggested that expectations consist of several different elements desired service level, adequate service level, predicted service level, and a zone of tolerance (Zeithaml, Berry, and Parasuraman, 1993 see also Parasuraman, Zeithaml, and Berry, 1994a). 

A procedure should be developed describing how to handle requests by regulators for documentation. Where requested, access to master (or copies of) documents (including raw data such as test evidence) should be provided within reasonable time frames, normally 24 to 48 h depending on circumstances. The Canadian Health Products and Food Branch Inspectorate, for instance, requires records to be accessible within 48 h. The FDA has similar requirements for off-site paper-based archives. Service Level Agreements between central support functions and sites should define the service levels for access to documentation. 

In addition to technology trends, which operate at the product, service, and solution level, Altshuller, Mann, and others have also developed 36 business trends—but these still represent a relatively new body of research and are yet to be rigorously validated. 

Fundamental understanding of soil physical, chemical, and biological interfacial interactions at the molecular level is essential to understanding the behavior of metals and metalloids in the pedosphere and to restoring terrestrial ecosystem health on the global scale. Future research on this extremely important and exciting area of science should be stimulated to sustain and enhance ecosystem productivity, services, and integrity and the impact on human health and prosperity. 

Supply chain networks Simultaneous maximization of (1) participants expected profits, (2) average safe inventory level (for plants, distribution centers and retailers), (3) average customer service levels (for retailers), (4) robustness of selected objectives to demand uncertainties and fair profit distribution. A two-phase fuzzy decisionmaking method Chen and Lee (2004) extended the smdy of Chen et al. (2003) by including uncertainty in product demands and prices. Cheu et al. (2003b) Cheu and Lee (2004)... 

As a consequent further development of LCA and LCE, LCS is expected to become the basis for future integrated sustainability decision support on the level of products / services and processes. It is a tool, developed for use in industry, but is expected to find application in policy making, too. 

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