The effect of foreknowledge of demand in case of a restricted capacity: the single-stage, singleproduct case with lost sales

Foreknowledge of demand is useful in the control of a production-inventory system. Knowingthe customer orders in advance makes it possible to anticipate properly. It is an importantcondition to produce and deliver the right quantity of the right product “just-in-time”. Itreduces the need of safety stock and spare capacity. But the question of the effectiveness offoreknowledge is not an easy one. Having foreknowledge of the customer orders does notremove the demand uncertainty completely. The effect of foreknowledge has to be consideredin a stochastic dynamic setting. The subject of this paper is the effect of foreknowledge incombination with a restricted production capacity. The lost-sales case is considered. The mainresult is that for high utilization rates and small forecast horizon, the inventory reduction dueto foreknowledge is equal to (1- pi).h, with h the forecast horizon

Demand uncertainty and lot sizing in manufacturing systems: the effects of forecasting errors and mis-specification

This paper proposes a methodology for examining the effect of demand uncertainty and forecast error on lot sizing methods, unit costs and customer service levels in MRP type manufacturing systems. A number of cost structures were considered which depend on the expected time between orders. A simple two-level MRP system where the product is manufactured for stock was then simulated. Stochastic demand for the final product was generated by two commonly occurring processes and with different variances. Various lot sizing rules were then used to determine the amount of product made and the amount of materials bought in. The results confirm earlier research that the behaviour of lot sizing rules is quite different when there is uncertainty in demand compared to the situation of perfect foresight of demand. The best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. In addition the choice of lot sizing rule between ‘good’ rules such as the EOQ turns out to be relatively less important in reducing unit cost compared to improving forecasting accuracy whatever the cost structure. The effect of demand uncertainty on unit cost for a given service level increases exponentially as the uncertainty in the demand data increases. The paper also shows how the value of improved forecasting can be analysed by examining the effects of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high forecast error variance, improved forecast accuracy should lead to substantial percentage improvements in unit costs

A forecast-driven tactical planning model for a serial manufacturing system

We examine tactical planning for a serial manufacturing system that produces a product family with many process steps and low volumes. The system is subject to uncertainty in demand, in the supply of raw materials, and in yield at specific process steps. A multi-period forecast gets updated each period, and demand uncertainty is realised in terms of forecast errors. The objective of the system is to satisfy demand at a high service level with minimal operating costs. The primary means for handling the system uncertainty are inventory and production flexibility: each process step can work overtime. We model the trade-offs associated with these tactics, by building a dynamic programming model that allows us to optimise the placement of decoupling buffers across the line, as well as to determine the optimal policies for production smoothing and inventory replenishment. We test the model using both data from a real factory as well as hypothetical data. We find that the model results confirm our intuition as to how these tactics address the trade-offs; based on these tests, we develop a set of managerial insights on the application of these operating tactics. Moreover, we validate the model by comparing its outputs to that from a detailed factory simulation

A dynamic model for requirements planning with application to supply chain optimization

Includes bibliographical references (p. 40-41).Funded by IBM Thomas J. Watson Research Center and the NSF Strategic Manufacturing Initiative.Stephen C. Graves, David B. Kletter and William B. Hetzel

Designing a robust production system for erratic demand environments.

Production systems must have the right type of material in the right quantities when required for production. They must minimize the work in progress while ensuring no stock-outstock-out occurs. While these twin opposing goals are achievable when demand is stable, they are difficult to realize under an erratic demand pattern. This dissertation aims to develop a production system that can meet erratic demands with minimal costs or errors. After a detailed introduction to the problem considered, we review the relevant literature. We then conduct a numerical analysis of current production systems, identify their deficiencies, and then present our solution to address these deficiencies via the ARK (Automated Replenishment System) technique. This technique is applied to a real-world problem at Methode Engineering ©. We conclude by detailing the scientific benefit of our technique and proposing ideas for future research

The Use of Capacity and Inventory in a Rate-Based Planning and Scheduling System to Achieve Strategic Goals in Industrial Applications

In pull or lean manufacturing, the final production schedule is in the form of takt time (drumbeat). All internal and external suppliers are driven by pull signals to feed the production rate. However, variability can be a problem for this drumbeat as the plan should not change more than the ability of the suppliers’ capability to respond. The supply chain should have sufficient flexibility to react quickly to changes in demand, while minimizing week-to-week production variability. Current planning and scheduling systems do not produce a plan that minimizes fluctuations. If the schedule is frozen for several periods, they are slow to react to changes in demand, which eventually produces many changes in production and inventory (the bullwhip effect). When these systems do not freeze the schedule, variation in the forecast and demand yield nervousness, making the planning difficult. Rate-Based Planning and Scheduling (RPBS) has been proposed as an alternative to current scheduling techniques. But for the most part, it has remained a concept rather than a method that can be implemented. The philosophy behind RBPS is to allow flexibility to adjust the schedule gradually for the near future, and more for periods farther into the future. If flexibility boundaries are defined strategically, the manufacturer will have the ability to respond to changes in demand, yet the schedule will be smooth and long term forecasts for the production rate will anticipate requirements from external suppliers. This dissertation consolidates previous material on RBPS for the first time. In addition, it introduces two algorithms (Retailer Smoothing and Production Smoothing) for RBPS. The Production Smoothing technique focuses on leveling production. Whereas, the Retailer Smoothing model allows the customer to create forecasted orders and then limits how much these orders may change. Through statistical experiments and simulations, the impact of the factors such as the standard deviation of demand, the length of the planning period, and the amount of flexibility in the plan are investigated. Irrelevant factors were eliminated as data from further simulations were compiled into tables. The goal of the tables is to allow practitioners to use one of the RBPS strategies with the appropriate levels of the RBPS factors by weighing the impact of capacity and inventory. For the Retailer Smoothing technique, the closer production follows demand and the shorter the flex fences, less inventory is needed as production will shift more. But as demand varies more, production changes and inventory level will increase significantly. On the other hand, Production Smoothing minimizes production changes by constraining flexibility and lengthening the planning period. This will, in turn, increase inventory. Also, as companies update their plan more frequently, more variation is added to the system which will vastly increase the inventory needed to buffer the production swings

Inventory Investment, Internal-Finance Fluctuation, and the Business Cycle

macroeconomics, inventory investment, internal-finance fluctuation, business cycle