Service level

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. 

The assessor should establish whether there is complete agreement and understanding between toller and the client about the requirements to be met (service level liabilities and so on). 

Define the service levels that you intend to provide up to and after warranty expires. 

FRESHMAN CHEMISTRY is arguably an important course, one that needs to be viewed as a contribution beyond a service level. It affords the opportunity to make the case, to many students of varied disciplines, of why chemistry is the central science and is responsible for virtually all of the high-tech developments they encounter or read about. The course should be a vehicle to attract more students to chemistry. More importantly, it should instill greater respect for and appreciation of chemistry by students who will not necessarily specialize in it. In our view, this function is particularly important for engineering students, as they will frequently use the basic ideas in freshman chemistry in their professional lives, yet they often wonder where the connection is while they are exposed to these ideas in the classroom. 

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. 

In the first section we discussed random demand. Then we calculated the conditional demand and now finally we define conditional random service and conditional random shortage. These concepts are very important for optimization of service levels under capacity constraints. 

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. 

In the process industries, service is often measured by beta service, which reflects that a customer will accept a partial delivery if the full ordered amount is not available in time. Due to the fact that we always mean beta service level in this chapter, we omit the word beta from further descriptions. 

The following definitions result from the simple observation that the minimum of demand and available stock will be always delivered and therefore can be taken to measure the service level. 

The service density defined above and illustrated in Figure 6.6 is a real variable that describes the distribution of the corresponding random variable. The density as a function is not continuous because it has a point mass at s = 35, the available inventory in the example, because the service is always exactly s if the demand is at least s. As a result, the service level distribution jumps to the value 100% at 35 because with 100% probability the service is 35 or less. 

The mean service resulting from a random demand and an inventory is often divided by the mean demand and then called the service level. It is always within 0 and 1 and is usually written as a percentage value. Because the usage of this indicator implicitly assumes that partial deliveries are allowed, it is known as /S-service level in order to distinguish it from the a-service level, which denotes the proportion of completely serviced orders. 

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... 

Optimal Lot Size and Service Level Resulting from Buffer Size... 

Optimal Lot Size A - - - Optimal Lot Size B 8 Service Level A - - - 6 Service Level B ... 

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. 

The definition of the cost model is of crucial importance for controlling the behavior of the S N P optimizer. One of the central questions is whether to maximize service level, which usually means using high penalties for non and late delivery, or to maximize profits, which requires the use of realistic sale prices. In the case study scenario, the nondelivery cost levels reflect real sale prices sufficiently close to enable a profit maximization logic. 

Most of the authors except Chopra/Meindl share the objective to minimize costs in the inter-company supply chain between companies with given demand and customer service level. Chopra/Meindl support the objective of value maximization, where it is later proposed to distinguish this objective with the term value chain management... 

Which indicators, mutual service level and bonus-malus agreements have to be agreed and measured between companies ... 

When the form of the change in a parameter with time has been established and a suitable measure to represent that form selected, the relation with the level of the degradation agent is needed to allow extrapolation to the service level. Generally, measurements need to be made at several agent levels to establish a model with reasonable confidence. Typically five levels are considered satisfactory. However, it should be noted that when extrapolation is to be made over several decades of time the uncertainty of the prediction will be large, even if the measured data looked very consistent. Estimates of uncertainty should always be made (see Section 9.3). 

The levels indieated in the following checklist may be characterized as follows Level 1 is typically used for pumps in general services. Level 2 comprises performance and material requirements and is more stringent than Level 1. Level 3 items should be considered for pumps in critical services. 

High Service Levels (Logistics, Technical, and Regulatory)... 

Service level agreements verified against the contract and approved... 

Change Any variation or alteration in form, state, or quality. It includes additions, deletions, or modifications impacting the hardware or software components used affecting operational integrity, service level agreements, or the validated status of applications on the system. 

---