The MPS parameterization under lead time uncertainty

This paper examines supply planning in an MRP environment for assembly systems under lead time uncertainties. Indeed, inventory control in a supply chain is crucial for companies who wish to satisfy their customer demands on time as well as controlling costs. A common approach is to use the MRP techniques. However, these techniques are based on the supposition that lead times are known. In an actual supply chain the lead times are often random variables. Therefore, we develop an efficient exact model to aid in MRP parameterization under lead time uncertainties, more precisely to calculate planned lead times when the component procurement times are random. The aim is to find the values of planned lead times which minimize the sum of the average component holding cost and the average backlogging cost. The developed approach is based on a mathematical model of this problem with discrete decision variables and a branch and bound algorithm.

There has been a dramatic increase over the past decade in the number of firms that source finished product from overseas. Although this has reduced procurement costs, it has increased supply risk; procurement lead times are longer and are often unreliable. In deciding when and how much to order, firms must consider the lead time risk and the demand risk, i.e., the accuracy of their demand forecast. To improve the accuracy of its demand forecast, a firm may update its forecast as the selling season approaches. In this article we consider both forecast updating and lead time uncertainty. We characterize the firm's optimal procurement policy, and we prove that, with multiplicative forecast revisions, the firm's optimal procurement time is independent of the demand forecast evolution but that the optimal procurement quantity is not. This leads to a number of important managerial insights into the firm's planning process. We show that the firm becomes less sensitive to lead time variability as the forecast updating process becomes more efficient. Interestingly, a forecast‐updating firm might procure earlier than a firm with no forecast updating. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009

LT variance or LT mean reduction in supply chain management: Which one has a higher impact on SC performance?

Supply planning under uncertainties in MRP environments: A state of the art

In this study, we investigate a continuous review inventory model to reduce lead time, yield variability and setup cost simultaneously through capital investments. We assume that the backorder rate is depending on the length of the lead time through the amount of shortages. We also assume that lead time demand's distribution is not known but its first and second moments are known. We apply minimax distribution free procedure to minimise the expected total annual cost. By using logarithmic investment function we describe the relationship between the reduction in lead time, yield variability and setup cost with capital investment. This function was used in many existing models. Our main aim is to determine the optimal capital investment and ordering policies that minimises the expected total annual cost. To find out the optimal solution, an algorithm is given. With the help of this algorithm, optimal capital investment and ordering policies are wrought out. Numerical examples are given to elucidate the model. Our proposed model greatly differs from the model existing in the literature (the model by Lin and Hou (2005)) viz: (1) In the above model, yield variability and setup cost were reduced through capital investment. In our model we reduce yield variability setup cost and also the lead time, which plays a vital role in any business. By reducing lead time we can improve the service level to the customer so as to increase the competitive edge in business. (2) In the model (the model by Lin and Hou (2005)), it was assumed that lead time demand follows normal distribution. But in our model we take the distribution of lead time demand as distribution free. That is, it can follow any distribution which is more general. (3) In the above model (the model by Lin and Hou (2005)), shortages are completely backlogged. But we consider partial backlogging and take the backlogging rate as 0 ≤ B ≤ 1. If we set backlogging rate B = 1 we get the above model. That is, the above model is particular case of our model. (4) We also assume that the backorder rate depends on the length of the lead time through the amount of shortages. If the lead time is longer then shortage accumulation is higher. The patience of customers will result in failure in business since some customers may turn to some other supplier. Hence, the backorder rate will be reduced. This assumption is very realistic.

TECHNICAL NOTEA note on lead time and distributional assumptions in continuous review inventory models

Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management

Demand and lead time uncertainties have significant effects on supply chain behaviour. In this paper, we present a single-product three-level multi-period supply chain with uncertain demands and lead times by using robust techniques to study the managerial insights of the supply chain inventory system under uncertainty. We formulate this problem as a robust mixed-integer linear program with minimised expected cost and total cost variation to determine the optimal (s, S) values of the inventory parameters. Several numerical studies are performed to investigate the supply chain behaviour. Useful guidelines for the design of a robust supply chain are also provided. Results show that the order variance and the expected cost in a supply chain significantly increase when the manufacturer’s review period is an integer ratio of the distributor’s and the retailer’s review periods.

In this paper, the authors studied the effects of process and component/part commonality and uncertain factors (viz. lead time, machine breakdown etc.) on deliverable parameters of a multi product, multi-period and multistage dependent demand manufacturing environment. The throughput of finished products and average production time of the system are examined for various experimental scenarios on the basis of existing manufacturing setup of a company consisting of multiple end products. Simulation package, namely WITNESS is used to simulate/analyze the situations of the production lines. Simulation models are developed, verified and validated with the historical data from the company. It is found that inclusion of common components in manufacturing system is always beneficial over the non-commonality environment, especially i) in uncertain situations, ii) for long procurement lead time of components and iii) when the number of parts increase in the system. Impacts of machine breakdown on system outcomes are higher than that of the lead time variation. The combination of uncertain factors has more impact on outcomes (throughput and average production time) compared to the individual factor. Commonality has a better control over the machine breakdown than lead time uncertainty. Component commonality is more effective for a production environment under uncertain situations than the process commonality.

This paper investigates the impact of setup cost reduction on an inventory policy for a continuous review mixture inventory model involving controllable backorder rate and variable lead time with a service level constraint, in which the order quantity, setup cost, and lead time are decision variables. Our objective is to develop an algorithm to determine the optimal order quantity, setup cost, and lead time simultaneously, so that the total expected annual cost incurred has a minimum value. Furthermore, four numerical examples are provided to illustrate the results, and the effects of system parameters are also included for decision making.

Inventory management and control is concerned with the acquisition and storage of materials required for supporting various business operations. Lead time reduction is another important production activity in an integrated inventory control. Lead time plays a vital role and has been a topic of interest for many authors in inventory organization. Lead time is a necessary aspect in any supply chain management and inventory management system. The time gap between placing of an order and its actual arrival in the inventory is known as lead time. In most of the literature dealing with inventory problems, either in deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable, which therefore, is not subject to control. But, in numerous sensible circumstances, lead time can he reduced by an additional crashing cost; in other words, it is controllable, so in this article, we have considered the lead time crashing cost is an exponential function of lead time. A solution procedure is developed to find the optimal solution. In this article, both lead time and the order quantity are considered as the decision variables. The behaviour of the model is presented graphically. The result is illustrated with the help of a numerical example. Finally, Graphical representation is presented to illustrate the model. The solution procedure with the help of the software Matlab 2008 is furnished to determine the optimal solution.

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided.

Excess inventory will lead to high inventory costs caused, among others, by damage and possible loss of stored goods embedded capital; on the other hand, a list is needed to ensure fulfillment of needs and reduce demand fluctuations and supply fluctuations to avoid loss of profit opportunities. This study aims to manage the uncertainty of usage and lead time in a wooden toy company, which can result in an excess or shortage of raw materials in the inventory system. It is necessary to control inventory to ensure optimal inventory; that is, needs can be met with minimal inventory costs and adequate service levels. The result, optimal condition is achieved if the company orders wood when the amount of inventory is (r) = 24,422 units with an order quantity of (Q) = 10,010 units each time you place an order, with a total inventory cost in 2023 of IDR 362,156,168, this policy will result in a service level (η) = 95.14%.

SUMMARY Traditionally, upon solution, independent demand inventory models result in the determination of a closed form for the economic lot size. Generally, this is obtained from the result that holding costs and setup costs are constant and equal at the optimum. However, the experience of the Japanese indicates that this need not be the case. Specifically, setup cost may be reduced by investing in reduced setup times resulting in smaller lot sizes and increased flexibility. Various authors have investigated the impact of such investment on classical lot sizing formulas which has resulted in the derivation of modified relationships. A common assumption of this research has been that demand and lead time are deterministic. This paper extends this previous work by considering the more realistic case of investing in decreasing setup costs where lead time is stochastic. Closed form relationships for optimal lot size, optimal setup cost, optimal total cost, etc. are derived. Numerical results are presented for...

Influence of lot size and planned lead time on service level and inventory for a single-stage production system with advance demand information and random required lead times