Lead Time Uncertainty Research Articles

Pre-distribution and post-distribution cross-docking operations

In this paper, we construct analytical models for distribution strategies with two major types of cross-docking, pre-distribution cross-docking (Pre-C) and post-distribution cross-docking (Post-C). A traditional distribution center system is also discussed for comparison purposes. Three models are compared pair-wisely. Analytical results show that, Pre-C is preferred for environments with shorter supply lead time and lower uncertainty of demand, without the benefits of risk-pooling. The Post-C mitigates the weakness of the Pre-C at the expense of higher operations cost spent at the cross-dock. Numerical experiments are conducted to support our results and explore other findings.

Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times

Supply planning for two-level assembly systems under lead time uncertainties is considered. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. A holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the component lead time is a random discrete variable. The objective is to find the release dates for the components at level 2 in order to minimize the expected component holding costs and to maximize the customer service level for the finished product. For this new problem, we consider two multi-objective approaches, which are both based on genetic algorithms. They are evaluated with a variety of supply chain settings, and their respective performance is reported and commented. These two heuristics permitted to obtain interesting results within a reasonable computational time.

A decision support framework for global supply chain modelling: an assessment of the impact of demand, supply and lead-time uncertainties on performance

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.

Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation

A new forecast-based dynamic inventory control approach is discussed in this paper. In this approach, forecasts and forecast uncertainties are assumed to be exogenous data known in advance at each period over a fixed horizon. The control parameters are derived by using a sequential procedure. The merits of this approach as compared to the classical one are presented. We focus on a single-stage and single-item inventory system with non-stationary demand and lead-time uncertainty. A dynamic re-order point control policy is analysed based on the new approach and its parameters are determined for a given target cycle service level (CSL). The performance of this policy is assessed by means of empirical experimentation on a large demand data set from the pharmaceutical industry. The empirical results demonstrate the benefits arising from using such a policy and allow insights to be gained into other pertinent managerial issues.

Studi Eksperimen Faktor-Faktor yang Berpengaruh Terhadap Schedule Nervousness

Schedule Nervousness is defined as the number of time and amount changes in planning of order releases. It is one of the factors affecting execution effectiveness on MRP system. The purpose of this research is to evaluate factors predicted affecting nervousness on MRP system by using simulation method. The main factors considered include forecasting accuracy, orgering, and leadtime uncertainty. This research will also evaluate the effect of selection of lot-sizing decision and cost structure ob MRP system nervousness by using experimental simulation. The result shows that the most affecting factor on schedule nervous-ness is cost structure. According to the result, it concludes that the most effective technique for reducing nervousness, firstly, is to lessen natural order cycle by shortening set up duration and using just-in-time elements. Secondly, is to select lor-sizing method properly.

Multi-echelon Supply Chain Inventory Planning with demand and leadtime uncertainty

The paper discusses a generic model for Multi-echelon Supply Chain Inventory Planning (MSCIP) with demand and leadtime uncertanity considering the Total Supply Chain (TSC) cost, which consists of Supply Chain Inventory Capital (SCIC), Supply Chain Ordering/set-up Cost (SCOC) and Supply Chain Stock-out Cost (SCSC) for a Maximum Allowable Supply Chain Inventory (MASCI). The Differential Evolution (DE/best/1/bin) strategy is used to solve MSCIP problem to determine ordering/production quantity and service level that should be maintained by each member of the supply chain. One case situation is elucidated to reinforce the salient features of the concept. A sensitivity analysis is carried out to know how the TSC is varying along MASCI in different environments.

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.

Optimal supply planning in MRP environments for assembly systems with random component procurement times

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.

Exploring the compatibility of dampening procedures and lot-sizing rules in MRP systems under uncertain operating environments

There are two important operational procedures, dampening procedures and lot-sizing rules, considered in the design of material requirement planning (MRP) systems. Lot-sizing rules have been extensively studied, and dampening procedures are relatively unfamiliar to MRP users. Dampening procedures are heuristic rules that serve as information filters to screen out excessive rescheduling messages generated by MRP in response to uncertain events occurring within or outside of production systems. There are distinctive characteristics of various lot-sizing rules, such as the just-in-time feature of lot-for-lot rules in which no inventories are carried. Similarly, dampening procedures contain certain operational characteristics that are compatible with that of lot-sizing rules. Due to this compatibility, the system performance of MRP is expected to improve if compatible dampening procedure and lot-sizing rule are used in an MRP system. The purpose of this paper is to explore this interesting compatibility by a simulation experiment to see whether it leads to better system performance under such deviations in operating environments as lead time uncertainty or scrap variations. Research results show that the performance of an MRP system is significantly affected by the dampening procedure and lot-sizing rule selected. We provide a general guideline to help MRP users determine how to select a dampening procedure and lot-sizing rule in tandem in order to generate better system performance in MRP systems.

Supply planning for single-level assembly system with stochastic component delivery times and service-level constraint

A problem of inventory control for assembly systems is considered where the component lead times are random. A periodic “lot-for-lot” policy for component supply is studied. The decision variables are component planned lead times. The aim is to minimize the average holding cost for components while keeping a high customer service level for the finished product. When the lead times of the different types of components follow the same distribution probability, and unit holding costs are identical for all components, an efficient algorithm is proposed. For a more general case with arbitrary distribution and holding costs, some properties and a lower bound on the cost function are proved. These results can be useful for the development of efficient exact optimization algorithms as Branch and Bound. This article's models can be employed for MRP parameterization, more precisely for safety lead time or safety stock calculation for each component under component lead time uncertainty.

Responding to Shipment Delays: The Roles of Operational Flexibility & Lead-Time Visibility

We consider a periodic review stochastic inventory system in which shipments procured from a different country are subjected to random delays. For this inventory system, we analyze various response strategies devised to mitigate the risk of demand-supply mismatch resulting from shipment delays. These strategies differ in two dimensions: Operational flexibility, defined by the flexible resource employed; and lead-time visibility, defined by the availability of information on shipment delays. For each of the response strategies, we provide analytical characterization of the optimal inventory policy. By comparing the optimal policies across different levels of operational flexibility and lead-time visibility, our analysis provides insights on operational flexibility, lead-time visibility and their interaction. We provide cost comparison of these strategies, using an extensive set of numerically solved examples. We show that while the operational flexibility mitigates the adverse effect of known long lead-times, the lead-time visibility provides protection from the risk of lead-time uncertainty. Further, we show that operational flexibility and lead-time visibility enhance each other's values, or in other words are complementary in nature.

A study of lead time variation impact on supply chain performance

Lead time (LT) is a core parameter that varies and affects all supply chain (SC) partners. LT uncertainty is known as a kind of supply uncertainty in the SC literature which attracts attention of many researchers. In this paper, impact of LT variability in a serially connected supply chain with 4 levels is investigated. The study includes two main stages: development of a structural model and testing the structural relations by using simulated data. Simulation makes it possible to remove effects of intervener variables in order to measure pure impact of LT uncertainty on SC. Regarding this matter, a structural relationship model with three hypotheses about direct and indirect impact of LT uncertainty on supply chain inventories have been developed. To test the proposed structural relations, simulated data were used. The results of the study show that by increasing the lead time variance, order variances increase while no impact on the bullwhip is observed, i.e. the order variances increase uniformly throughout the entire SC. Furthermore, results show that the increase in the lead time variance will lead to inventory fluctuations. One practical recommendation of this study is to apply an investment strategy to reduce the lead time uncertainty.

An Approach for the MRP Parameterization Under Lead Time Uncertainty: Branch and Cut Algorithm

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 a Supply chain the lead times are often random variables. Therefore, an efficient exact approach to aid in MRP parameterization under lead time uncertainties was developed; more precisely the approach has as objective 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 on a Branch and Cut algorithm.

A Genetic Algorithm for Replenishment of Two-Level Assembly Systems

This paper examines supply planning for two level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. It is assumed also that the component lead time at each level is a random discrete variable. The expected cost is composed of the finished product tardiness cost and the component holding costs. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. A genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms convergence and computation time is investigated.

Public Evacuation Decisions and Hurricane Track Uncertainty

Public officials with the authority to order hurricane evacuations face a difficult trade-off between risks to life and costly false alarms. Evacuation decisions must be made on the basis of imperfect information, in the form of forecasts. The quality of these decisions can be improved if they are also informed by measures of uncertainty about the forecast, including estimates of the value of waiting for updated, more accurate, forecasts. Using a stochastic model of storm motion derived from historic tracks, this paper explores the relationship between lead time and track uncertainty for Atlantic hurricanes and the implications of this relationship for evacuation decisions. Typical evacuation clearance times and track uncertainty imply that public officials who require no more than a 10% probability of failing to evacuate before a striking hurricane (a false negative) must accept that at least 76%—and for some locations over 90%—of evacuations will be false alarms. Reducing decision lead times from 72 to 48 hours for major population centers could save an average of hundreds of millions of dollars in evacuation costs annually, with substantial geographic variation in savings.

Applying the minimum relative entropy method for bimodal distribution in a remanufacturing system

This paper provides a further investigation of a remanufacturing system studied by Tang et al. [2007. Planned lead time determination in a make-to-order remanufacturing system. International Journal of Production Economics 108, 426–435]. Due to the uncertainty of the disassembly lead time, purchasing lead time and yield rate, the probability density function (PDF) of the lead time for obtaining a component takes a bimodality form. In this paper, the minimum relative entropy (MRE) method, in which high-order moments are utilized, has been applied to approximate the lead time distribution. Numerical result shows that the MRE approach captures well the bimodality property. After reinvestigating the remanufacturing system, we verify the statement in Tang et al. that improving the recovery yield in disassembly does not necessarily improve the system performance. However, more accurate optimal decisions and system performance can only be evaluated by the advanced density function approximation method, such as MRE. Using data in an engine remanufacturing case, we further obtain managerial insights for supporting decision making in labor allocation, supplier selection and process investment.

Multi-echelon inventory management in supply chains with uncertain demand and lead times: Literature review from an operational research perspective

Historically, the echelons of the supply chain, warehouse, distributors, retailers, etc., have been managed independently, buffered by large inventories. Increasing competitive pressures and market globalization are forcing firms to develop supply chains that can quickly respond to customer needs. To remain competitive and decrease inventory, these firms must use multi-echelon inventory management interactively, while reducing operating costs and improving customer service. The current paper reviews the literature, addressing multiechelon inventory management in supply chains from 1996 to 2005. The behaviour of the papers against demand and lead-time uncertainty is the key analysis point of the literature review presented here and it is conducted from an operational research point of view. Finally, directions for future research are suggested.

Risk, risk aversion and the optimal time to produce

This paper studies the order-fulfillment process of a supplier producing a customized capital good. When they decide at what time to begin production, suppliers in these industries typically face the following dilemma. On the one hand, their customers expect them to be responsive and the time that they are prepared to wait for the product is much shorter than the time needed to produce and deliver it. On the other hand, it is risky for the suppliers to start production before the customers confirm their orders due to inventory holding costs and the possibility of order cancellation. We offer a model to help understand this dilemma and investigate the comparative statics effects of lead time, lead time uncertainty and the risk aversion of the supplier. Relative to existing work, our model has two novel components: demand distribution updating and the supplier's risk attitude. Previous empirical studies reveal that the supplier is very conservative when commencing the order fulfillment. The studies attribute this to high holding and cancellation costs relative to the delay cost. Our model provides two alternative explanations. First, other than costs, when the supplier starts to produce depends also on how the distribution of the demand arrival time is updated as time progresses. Second, although the effect of increasing risk aversion of the supplier on the optimal time to produce is ambiguous, we show that it plays a major role in the decision and thus it alone may have caused the supplier to be conservative.

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

Inventory control in a supply chain is crucial for companies desiring to satisfy their customers demands as well as controlling costs. This paper examines specifically supply planning under uncertainties in MRP environments. Models from literature that deal with random demand or lead time uncertainties are described and commented. Promising research areas emerge from this survey. It appears that lead time uncertainty has been ignored in the past, in spite of their significant importance. In particular, an interesting topic concerns assembly systems with uncertain lead times, for which the main difficulty comes from the inter-dependence of components inventories. Another promising issue, which is also presented, relates to supply planning under simultaneously demand and lead time uncertainties, which is certainly of great interest for both the academic and industrial communities.

Research Note—The Role of Production Lead Time and Demand Uncertainty in Marketing Durable Goods

Firms often have to make their production decisions under conditions of demand uncertainty. This is especially true for product categories such as automobiles and technology goods where the lead time needed for manufacturing forces firms to make production decisions well in advance of the selling season. Once the firm has produced the goods, the available production volume affects the firm’s subsequent marketing decisions. In this paper, we study the relationship between the firm’s production and marketing decisions for a durable goods manufacturer. We develop a dynamic model of a durable product market in which the demand functions are developed from a micromodeling of consumer utility functions and an equilibrium analysis of consumer strategies. After taking into account the demand uncertainty as well as the potential for cannibalization of future sales, the manufacturer makes its production and sales decisions. We find that the firm’s optimal inventory level is U-shaped in the durability of the product and that the firm suffers a larger loss due to uncertainty when it is leases rather than sells its products. Furthermore, unlike the case for nondurables, for durable goods we find that the effect of uncertainty persists even after the uncertainty has been resolved.