Variable Lead Time Research Articles

Markov-modulated analysis of a spare parts system with random lead times and disruption risks

Spare parts supply chains are highly dependent on the dynamics of their installed bases. A decreasing number of capital products in use increases the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk appears to present itself through varying lead times coupled with supply disruptions. To model the nonstationary supply-side risk, we consider an exogenous Markov chain that modulates random lead times and disruption probabilities. Assuming that order crossovers do not occur, we prove the optimality of a state-dependent base stock policy. Later, we conduct an impact study to understand the value of considering stochastic lead times and supply disruption risk in spare parts inventory control. Our results indicate that the coupled effect of random lead times and disruptions can be larger than the summation of individual effects even for moderate lead time variances. Also, the effect of nonstationarity on total cost can be as large as the summation of all risk factors combined. In addition to this managerial insight we present a procedure for supply risk mitigation based on an empirical model and our mathematical model. Experiments on a real business case indicate that the procedure is capable of reducing costs while making the inventory system more prepared for disruptions.

The value of lead time reduction and stabilization: A comparison between traditional and collaborative supply chains

This work quantifies the financial impact of the mean and the variability of production and shipping lead times on multi-echelon supply chains. We combine agent-based modelling and Taguchi methods, through which we develop a framework for supporting entrepreneurial investment decisions. A throughput-based analysis reveals that decreasing mean lead times improves the internal operation of production and distribution systems, while reducing lead time variability enhances the satisfaction of consumers. In this regard, we contrast traditional and collaborative supply chains. We find that the latter are not only more profitable than the former, but also more robust to variations in lead times.

Lean, agile and leagile healthcare management – A case of chronic care

ABSTRACTHealthcare services face a dual challenge of being lean and agile at the same time. This paper proposes an approach for implementation of leagility in healthcare services, especially chronic care like cardio-diabetes. The Multi-Dimensional Scaling method was used to visualize the dissimilarities among the selected inventory categories, which suggested us that selection of a supply chain strategy based on demand and lead time variability only, is not sufficient. Using focus group discussion additional factor influencing the strategy were determined and Analytical Hierarchy Process was used to find out the priority of these additional factors. Finally, a two-stage rule was developed to classify the categories into lean, agile and leagile groups. The study concluded that lean strategy is suitable for oral medicines, nursing- items and food supplements while leagile strategy is suitable for insulin, assistive –technologies, and diabetic footwear. The study appends Supply Chain Matrix proposed by Martin Christopher and proposes a more comprehensive method for implementation of leagile strategy in case of healthcare.

An empirical application of lean management techniques to support ETO design and production planning

Engineer To Order (ETO) companies have to face the difficulty to manage high uncertainty in customer requirements, and, consequently, to organize all the activities for efficiently and effectively answering customer orders. In these contexts, planning the activities of design, production and assembly departments is a complex task, also given the variability of lead times and the difficulties in forecasting resources workload. Literature has mainly devoted attention to planning tools for standardized make to stock environments, often neglecting ETO companies’ needs. This paper aims to present a methodology for supporting design, production and assembly planning in an ETO context. The methodology combines project requirements planning with lean management tools, i.e. Asaichi and visual control and management tools. It has been successfully applied in a company showing its potential for increasing planning performance, company flexibility and, in the end, delivery performance.

A vendor–buyer inventory model with imperfect production considering investment to reduce lead time variability

This research develops an integrated single-vendor single-buyer inventory model considering imperfect production process and investment to reduce lead time variance. In the model, we consider a sys...

Learning and screening errors in an EPQ inventory model for supply chains with stochastic lead time demands

This paper investigates the role of variable lead time, learning in production and screening errors in a vendor–buyer supply chain with defective items. The vendor–buyer supply chain is modelled for supplying a single item considering that the lots from vendor may contain some defective items. It is assumed that demand during lead time follows a normal distribution. Moreover, the production time at vendor’s facility is assumed to follow learning whereas buyer’s screening for defective items is prone to errors as well. Numerical examples are presented to illustrate the impact of different variables in the model. The analysis shows that delay in transportation lead time forces the buyer to carry more inventories to avoid shortages. Further, Type I error has a major impact on this cost. It was found that learning in production keeps on reducing the total cost of the supply chain up to a threshold.

Development of a novel lot sizing model with variable lead time in supply chain environment

Supply chain management (SCM) addresses the management of materials and information across the entire chain from suppliers to producers, distributors, retailers, and customer. The theory of supply chain management suggests that lead time reduction is a pioneer to the use of market mediation to reduce transaction uncertainty in the chain, which can be conceptualized as the primary goal of supply chain management. In the past few decades, scholars gave ample attention about the impact of inventory on SCM. This paper relates to the development of a lot sizing model for a single component multiple delivery system with variable demand and lead time of a multinational transformer company. Two models and the modification were developed on the basis of the following assumptions. For first model distribution of demand is normal, distribution of procurement lead time is exponential and the quantity is coming in a single lot. For second model distribution of demand is normal distribution of ‘procurement’ and ‘administrative delay’ lead time is exponential and the quantity is coming in a single lot. Modification of the first model has been done by taking the effect of multiple deliveries in the models and correcting the Re-order point as obtained from the previous models. The results were observed by the second model and analysis has been done for different parametric conditions. The effect of multiple deliveries is also taken into account. The optimum re-order point and economic ordering quantity with various different inputs have been discussed.

Sustainable EOQ under Lead-Time Uncertainty and Multi-Modal Transport

In this paper, we examine a sustainable economic order quantity (S-EOQ) problem with a stochastic lead-time and multi-modal transportation options. With the S-EOQ, decisions of order quantities, as well as a reorder point could be influenced by various factors, including unit price, stock-out cost, lead-time variability and emission costs. For a better understanding, we present a mathematical model of the concomitant S-EOQ problem and, using numerical experiments, explore various scenarios to determine the effects of incorporating sustainability considerations into the traditional inventory model on operational decisions including the choice of transportation modal combination and the sourcing decisions

Incorporating Order Crossover Information into Service-Oriented Base Stock Policy Decisions

We investigate the impact of order crossover on a periodic, order-up-to (R,S) inventory system where attaining a target service level is the focus. Order crossover, when orders arrive in a sequence different from that in which they were originally placed, is increasingly likely to occur in modern supply chains. One reason for this is the increasing pressure to focus on service performance, which we refer to as the “Amazon Service Effect”. Many service-oriented changes to order placement and fulfillment strategies will increase the chance of order crossover. While the majority of inventory control studies, including crossover-focused studies, tend to be cost-oriented, in practice many inventory systems are driven by service goals. To explore the use of crossover information in inventory policy decision-making we adopt the “effective lead-time” (ELT) approach (Hayya et al., 2008) to representing order lead-times where order crossover is present. We develop a hybrid discrete-event/continuous simulation model that utilizes a unique structure to provide a detailed representation of a stochastic inventory system and collect precise information about order crossover, effective lead-time, service performance, and related operating characteristics. We first address two crossover related assumptions that have not been directly addressed in the literature. We find that order crossover clearly impacts service-oriented inventory system performance (much as it does cost-oriented systems) and that a protection period of R ELT time units is appropriate for an R,S inventory policy under crossover. We then explore five methods of including ELT information into the process of determining the order-up-to level (S) for a periodic, order-up-to (R,S) inventory policy. We perform a full-factorial simulation study under various conditions of target service level, demand variance, and lead-time variance, for a total of 375 scenarios. We find the method used to include effective lead-time information into the decision significantly impacts service performance. We compare and contrast the different methods, discuss issues such as data requirements and the sources of error, and address the research and managerial implications of our findings.

Consignment Inventory Model with Variable

This paper describes an optimal methodology for consignment inventory based supply chain with variable controllable lead times comprising a two-level supply chain involving one vendor and multi buyers. It evaluates joint total expected cost of vendor and buyer, simultaneously optimize quantitative decision variables and illustrates the significance of production to demand ratio. Numerical examples are presented to illustrate the benefit of the proposed model and the effects of changes on the cost and parameters are studied.

Economic Design of Multi-echelon Inventory System with Variable Lead Time and Service Level Constraint in a Fuzzy Cost Environment

Normally, human based industrial decision making involves both quantitative and qualitative input factors such as ordering cost, setup cost, etc. Thus the practitioners should be careful in accounting flexibility in the input factors. This paper optimizes an inventory system by employing mathematical model with variable lead time and service level constraint (SLC) in a fuzzy cost environment. Fuzziness is established by allowing the cost components imprecise and vague to certain extent. Trapezoidal and triangular fuzzy numbers are used to represent these characteristics. We also use signed distance method to defuzzify the fuzzy joint total expected cost and differential calculus optimization technique is adopted to find all optimal solutions to the model. Numerical results highlighting the sensitivity in the decision variables are also described.

Appointment Lead Time Policy Development to Improve Patient Access to Care.

Patient access to care has been a known and continuing struggle for many health care providers. In spite of appointment lead time policies set by government or clinics, the problem persists. Justification for how lead time policies are determined is lacking. This paper proposed a data-driven approach for how to best set feasible appointment target lead times given a clinic's capacity and appointment requests. The proposed approach reallocates patient visits to minimize the deviation between actual appointment lead time and a feasible target lead time. A step-by-step algorithm was presented and demonstrated for return visit (RV) and new patient (NP) types from a Pediatric clinic excluding planned visits such as well-child exam and the same day urgent appointments. The steps are: 1. Obtain appointment requests; 2. Initialize a target lead time; 3. Set up an initial schedule; 4. Check the feasibility based on appointment availability; 5. Adjust schedule backward to fill appointment slots earlier than the target; 6. Adjust schedule forward for appointments not able to be scheduled earlier or on target to the later slots; 7. Trial different target lead times until the difference between earlier and later lead time is minimized. The results indicated a 59% lead time reduction for RVs and a 45% reduction for NPs. The lead time variation was reduced by 75% for both patient types. Additionally, the opportunity for the participating clinic to achieve their organization's goal of a two-week lead time for RVs and a two-day lead time for NPs is discussed by adjusting capacity to increase one slot for NP and reduce one slot for RV. The proposed approach and study findings may help clinics identify feasible appointment lead times.

Impact of lead time variability in supply chain risk management

In this article, we study the impact of lead time variability on the performance of supply chain risk management in the beer industry. The stakeholders considered are the empty aluminum can supplier, the brewery and the distributor. The stochastic lead time model developed is an extension of the previously developed base model (with deterministic lead time) under which commodity price risk and demand uncertainty are managed via an integrated risk management approach using operational methods and financial derivatives. Simulation-based optimization is used to model and analyze such a complex system. We find that lead time variability does not always deteriorate the supply chain performance. Only high levels of lead time variability are found to justify more the need for a coordinated supply chain. The findings also reveal the need for using different hedging strategies to manage products flows across the supply chain under stochastic lead time versus the deterministic case. Managerial insights for the supply chain studied are argued based on experimental design findings.

Inventory record inaccuracy – The impact of structural complexity and lead time variability

This article presents a rigorous prospective analysis of the impact of Inventory Record Inaccuracy (IRI) on complex multi-echelon Supply Chains (SCs). Specifically, key operational factors (i.e., the magnitude of the error, frequency of the inventory audits and lead time variability) and SC structure are systematically assessed. We find that the detrimental effects of IRI are exacerbated by the structural complexity of the SC and lead time variability. Furthermore, we show how the efficacy of countermeasure strategies may vary depending on SC configuration and operational conditions. These results allow us to provide interesting managerial recommendations to guarantee investment in prevention and correction strategies.

A hybrid approach for cost-optimized lateral transshipment in a supply chain environment

Purpose – When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers. Design/methodology/approach – This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration. Findings – The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions. Practical implications – The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times. Originality/value – The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.

The Impact of Inventory Management on Stock-Outs of Essential Drugs in Sub-Saharan Africa: Secondary Analysis of a Field Experiment in Zambia.

ObjectiveTo characterize the impact of widespread inventory management policies on stock-outs of essential drugs in Zambia’s health clinics and develop related recommendations.MethodsDaily clinic storeroom stock levels of artemether-lumefantrine (AL) products in 2009–2010 were captured in 145 facilities through photography and manual transcription of paper forms, then used to determine historical stock-out levels and estimate demand patterns. Delivery lead-times and estimates of monthly facility accessibility were obtained through worker surveys. A simulation model was constructed and validated for predictive accuracy against historical stock-outs, then used to evaluate various changes potentially affecting product availability.FindingsWhile almost no stock-outs of AL products were observed during Q4 2009 consistent with primary analysis, up to 30% of surveyed facilities stocked out of some AL product during Q1 2010 despite ample inventory being simultaneously available at the national warehouse. Simulation experiments closely reproduced these results and linked them to the use of average past monthly issues and failure to capture lead-time variability in current inventory control policies. Several inventory policy enhancements currently recommended by USAID | DELIVER were found to have limited impact on product availability.ConclusionsInventory control policies widely recommended and used for distributing medicines in sub-Saharan Africa directly account for a substantial fraction of stock-outs observed in common situations involving demand seasonality and facility access interruptions. Developing central capabilities in peripheral demand forecasting and inventory control is critical. More rigorous independent peer-reviewed research on pharmaceutical supply chain management in low-income countries is needed.

A technical note on periodic inventory model with controllable lead time under service level constraint

In this article, a periodic review inventory model having variable lead time, along with the service level constraint has been discussed. The demand during the protection interval is assumed to follow the Normal distribution. A wide range of expected shortages during the protection interval has been considered, with their corresponding values of safety factor. Keeping this in mind, we have returned to both Ouyang and Chuang (2000) and Liang et al. (2008) model. Further, it has been observed that both the models do not succeed in providing the solution for service level other than 98% and 99%. In the light of this fact, we revisit the solution procedure of both the papers and are able to obtain the result for any level of service. Detail comparative analysis has also been presented.

The (p, q, r, l) model for stochastic demand under Intuitionistic fuzzy aggregation with Bonferroni mean

This paper investigates a hill type economic production-inventory quantity (EPIQ) model with variable lead-time, order size and reorder point for uncertain demand. The average expected cost function is formulated by trading off costs of lead-time, inventory, lost sale and partial backordering. Due to the nature of the demand function, the frequent peak (maximum) and valley (minimum) of the expected cost function occur within a specific range of lead time. The aim of this paper is to search the lowest valley of all the valley points (minimum objective values) under fuzzy stochastic demand rate. We consider Intuitionistic fuzzy sets for the parameters and used Intuitionistic Fuzzy Aggregation Bonferroni mean for the defuzzification of the hill type EPIQ model. Finally, numerical examples and graphical illustrations are made to justify the model.

Determination of inventory for mining production with a real options approach and comparison with other classic methods

Safety stock of mining production is kept to prevent ore supply from stock-out, due to the existence of high uncertainty in mining operations and various unforeseen geological conditions. The downstream of mining normally runs in a relatively constant status which tolerates limited fluctuation from the upstream. An unreliable ore supply would cause the insufficient equipment utilisation and economical loss, and even the shutdown of processing plant. Since the rise of inventory management in the 1980s, there have been many studies conducted for enterprises to optimise their inventory and fulfil their clients’ demands. However, those studies mostly focus on the retail industry. The research interests are mainly in the variability of demand and lead time, and the modelling of a multi-echelon chain, which are not often suitable for mining production. Mining production has its own features, which requires a tailor-made policy of safety stock for itself. Real options technique is one suitable method to determine the safety stock of mining production. In this paper, it first analyses the flaws of the existing techniques of calculating safety stock to be used in mining production, and then elaborates the development of a new safety stock policy, based on a real options technique. By a case study, it analyses real data of mining production from Kittilä mine, illustrates the use of the new method and compares this new method with the conventional methods. It shows that the real options approach can provide mining practitioners with a more accurate and reasonable stock level, and more profit.

Single vendor-buyer integrated inventory system for imperfect production with variable lead time

Single vendor-buyer integrated inventory system for imperfect production with variable lead time