Component and process commonalities in production system under various uncertain factors

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

ABSTRACTThe pressure to reduce inventory investments in supply chains has increased as competition expands and product variety grows. Managers are looking for areas they can improve to reduce inventories without hurting the level of service provided. Two areas that managers focus on are the reduction of the replenishment lead time from suppliers and the variability of this lead time. The normal approximation of lead time demand distribution indicates that both actions reduce inventories for cycle service levels above 50%. The normal approximation also indicates that reducing lead time variability tends to have a greater impact than reducing lead times, especially when lead time variability is large. We build on the work of Eppen and Martin (1988) to show that the conclusions from the normal approximation are flawed, especially in the range of service levels where most companies operate. We show the existence of a service‐level threshold greater than 50% below which reorder points increase with a decrease in lead time variability. Thus, for a firm operating just below this threshold, reducing lead times decreases reorder points, whereas reducing lead time variability increases reorder points. For firms operating at these service levels, decreasing lead time is the right lever if they want to cut inventories, not reducing lead time variability.

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

In recent years, manufacturing firms have realized that a new, higher level of global competition causes them to compete simultaneously on multiple manufacturing goals, such as quality, delivery, cost, and flexibility. In response to this realization, considerable research now focuses on the relationship of manufacturing improvement programs to manufacturing goals. However, to date, this research has not investigated the specific underlying statistical relationships between manufacturing goals and the shop floor. This study investigates manufacturing lead time linkages with manufacturing programs and manufacturing goals. The basic purpose of this study is to understand and explain how programs affect the elements of manufacturing lead time and how manufacturing lead time affects manufacturing goal capabilities. By understanding these linkages, managers can logically trace the effects of specific programs to their eventual effects on manufacturing goal capabilities. This study's most important finding is that statistical variations in the elements of lead time cause a tendency for certain manufacturing goals to be more difficult to control and achieve than others because of canonical relationships of lead time variances. To control these lead time variances, successful firms concentrate their early program targets first on achieving “fitness for use” quality, followed by delivery reliability, short delivery lead time and cost, current product flexibility, and lastly, new product flexibility. This study mathematically illustrates which improvement programs most affect manufacturing goals through their relationship to manufacturing lead time variance reduction. It suggests that firms improve goal performance by initially targeting improvement through setup time reduction programs, defect reduction programs, and preventive maintenance programs, to facilitate quality improvements. By targeting specific programs and their related lead time variances, firms improve their manufacturing facility competitiveness with minimum obstacles.

Blood bank operation is a facility which involves the process of collecting, testing, processing and storing blood for later use in blood transfusion. Blood bank management faces a crucial problem due to shortage of blood supply that sometimes does not meet the demand. Blood inventory is difficult to organize and control especially in festive seasons when demand is high due to road accidents and casualties. Blood bank management needs to balance the demand and supply to avoid shortage, and since blood is perishable, the management also needs to avoid wastage. To solve this issue, an effective inventory method should be implemented. This study compares two policies: the continuous review policy and the periodic review policy. The continuous review policy observes the monitory level more accurately and on time, while the periodic policy applies only periodically inventory control. This study looks at the relationships between the service level, safety stock and inventory cost that are based on three case studies, which are variable demand and constant lead time, variable lead time and constant demand and variable demand and variable lead time. The result shows that the continuous review policy should be implemented in blood bank management due to lower inventory cost and being more systematic with higher efficiency.

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.

Computational algorithmic procedure of optimal inventory policy involving a negative exponential crashing cost and variable lead time demand

This paper studies a buyer sourcing a product from multiple suppliers under stochastic demand. The buyer uses a (Q, s) continuous review, reorder point, order quantity inventory control system to determine the size and timing of orders. Lead time is assumed to be deterministic and to vary linearly with the lot size, wherefore lead time and the associated stockout risk may be influenced by varying the lot size and the number of contracted suppliers. This paper presents mathematical models for a multiple supplier single buyer integrated inventory problem with stochastic demand and variable lead time and studies the impact of the delivery structure on the risk of incurring a stockout during lead time.

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

We consider cost minimization for an (S, s) continuous-review perishable inventory system with random lead times and times to perishability, and a state-dependent Poisson demand. We derive the stationary distributions for the inventory level using the Queueing and Markov Chain Decomposition (QMCD) methodology. Applying QMCD, we develop an intuitive approach to characterizing the distribution of the residual time for the next event in different states of the system. We provide comprehensive analysis of two main models. The first model assumes a general random lifetime and an exponential distributed lead time. The second model assumes an exponential distributed lifetime and a general lead time. Each model is analyzed under both backordering and lost sales assumptions. We consider a fixed cost for each order, a purchase cost, a holding cost, a cost for perished items, and a penalty cost in the case of shortage. Numerical examples are provided and show that variability of lead time is more costly than that of perishability time. Therefore, after reducing lead time and increasing perishability time, managers should focus on reducing variability of lead time.

The MPS parameterization under lead time uncertainty

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

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.

Min–max distribution free continuous-review model with a service level constraint and variable lead time

Em ambientes globais e altamente competitivos, as cadeias de suprimentos tem como desafio contribuir nos processos de geracao de valor ao cliente e ao acionista, oferecendo nivel de servico elevado e custos totais baixos, bem como retorno satisfatorio ao investimento. Para este fim, a literatura tem proposto a conformacao ou configuracao de cadeias de suprimentos denominadas de “ leagile ”. Esta nova configuracao foi denominada por Naylor, Naim e Berry (1999) de “ leagility ”, por combinar vantagens do pensamento lean encontrado nas cadeias de suprimentos lean (CSL) e da agilidade por meio do conhecimento de mercado e habilidade para responder a demandas cada vez mais flexiveis, elementos que caracterizam a cadeia de suprimentos agil (CSA). A esta perspectiva teorica sao associados outros dois conceitos, o do posicionamento do decoupling point – DCP (ponto de desacoplamento) e postponement (postergacao). Contudo, para se indicar as cadeias de suprimentos leagile como alternativa estrutural para geracao de valor, faz-se necessario desenvolver estudo que vislumbre sua caracterizacao e o modo como podem promover a geracao de valor. Neste sentido, o presente estudo tem como objetivo, a partir de uma pesquisa bibliografica, descrever elementos e praticas que caracterizam cadeias de suprimentos leagile e suas possibilidades de geracao de valor. Como principais resultados, a pesquisa indicou que resposta rapida e oferta diversificada de produtos, associadas a diminuicao de lead time, de defeitos e de custo, sao alguns dos itens que possibilitam geracao de valor em uma cadeia leagile aos clientes e acionistas. O estudo oferece contribuicoes tanto para o conhecimento academico quanto para o pratico e seu aprofundamento aponta para pesquisas futuras de carater empirico.