Replenishment Lead Time Research Articles

A Simulation-Based Bayesian Network Approach to the Joint Decision of the International Transportation Mode and Safety Inventory Policy

Uncertainty in international transportation can have a significant impact on inventory costs and customer service levels in a global supply chain network. A very limited number of studies in the literature have addressed the international transportation mode and safety inventory policy (ITM-SIP) issues simultaneously. This study formulates the problem and proposes a simulation-based Bayesian network (SBN) approach for the joint decisions on ITM-SIP, where Bayesian networks are used to dynamically estimate international replenishment lead times for railway and maritime transportation modes. We implement our approaches in an international automotive spare parts supply chain experiencing fluctuations in its shipping supply market and with dynamic demand. For this joint decision problem, we build an international supply chain network simulation model in which the time-delay uncertainties of key bottleneck nodes in maritime and railway transportation routes are considered. The simulation experiment results show the performance improvement of the SBN approach in substantially reducing total costs and achieving service levels. We also provide insights into the value of decision tactics for responding to different fluctuation modes in the shipping supply market in the global supply chain network.

A hybrid LSTM method for forecasting demands of medical items in humanitarian operations

The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns.

Joint Discount and Replenishment Parametric Policies for Perishable Products

We consider a joint discount and replenishment problem in a discrete periodic review fashion for the sale of a perishable product, characterized by limited deterministic shelf life, replenishment lead times, and stochastic demand. Customers decide what to buy according to a linear discrete choice model, balancing price and perceived quality, uniquely determined by the residual shelf life. The decisions we consider are: How many new items to order, the age of the items to be discounted, and how much discount to offer. In this context, we compare a set of policies mixing the constant order policy and the base stock one with some easy discounting policies, optimizing their parameters using a simulation-based optimization framework. To evaluate their performance in terms of revenue and quantity of scraped items, we consider four realistic instances for a grocery retailer characterized by products of different shelf life and variance of demand. Experiments show that best results are achieved by a base stock policy that discounts products of different ages based on a threshold: If the quantity of the inventory of a given age is greater than a threshold it applies a discount, otherwise no discount is proposed. In the presented configurations, this policy increases the average reward compared to policies that do not discount.

Using Blockchain to Improve Buffer-Stock-Sharing and Combat Cheating Behaviors Under Virtual Pooling

Blockchain is a disruptive technology, which is crucial for business operations. In this article, we analytically explore how two manufacturers can achieve efficient buffer stock sharing using the blockchain technology (BCT). We first build an analytical basic model with a deterministic lead time for material replenishment and quantify the benefit of adopting a buffer stock sharing scheme. In the absence of BCT, we demonstrate the natural occurrence of a cheating problem. We analytically derive the overall value of blockchain technology (OVBCT) for the buffer stock sharing scheme and highlight the conditions under which it is increasing or decreasing in demand uncertainty. We also show how the buffer stock service level can be improved with the use of BCT. To show robustness of the analytical findings, several extended cases are explored. Novel buffer stock division rules are then generated under the proposed buffer stock sharing scheme, which makes the alliance profitable. In addition, an n-manufacturers alliance is further analytically explored. We find that the core findings from the basic model continue to hold in the extended models. Finally, we establish the conditions for achieving Pareto improvement with the use of BCT by considering the logistics services adopted.

Optimal analysis of a multi-server queue with preprocessing time and replenishment inventory

ABSTRACT Recently, e-commerce has had the advantage of lower rental expenses. Convenient online payment has rapidly grown and provides a great business opportunity for enterprises. Inventory management plays a critical role in the competitiveness of most electronic business enterprises. Before the products hit the store shelves or are available online to customers, the quantity and quality of the products should be confirmed and inspected. Thus, in addition to the replenishment lead time, the preprocessing time has essential effects on inventory management. To reduce the inventory cost and determine a proper replenishment policy, we propose a multi-server inventory queue with a preprocessing time, which is seldom mentioned by the existing studies. The steady-state probabilities, system stability conditions, and expressions of several critical system characteristics are derived. Subsequently, the operating cost function is developed to determine the optimal reorder point and an appropriate number of servers with the minimum cost. The established numerical results and sensitivity analysis help managers identify critical variables and enhance operational efficiency.

Setting fulfillment-time guarantees for accepting customer orders in a periodic-review base-stock inventory system

Retailers are increasingly promising a maximum fulfillment time window for fulfilling customer orders. Motivated by such guarantees, we consider an infinite-horizon periodic-review base-stock inventory system that imposes an endogenously determined fulfillment-time limit for accepted orders. The inventory position is reviewed every fixed T periods and an order is placed to bring the system inventory up to R, the target base stock. The replenishment lead time is a constant of L periods. In each period, the realized consumer demand is accepted only if it can be filled within a guaranteed time limit of τ periods. By incorporating a fulfillment-time limit, the model allows both backorders and lost sales. The objective is to find the strategy that minimizes the long-term average cost per unit time by selecting the base stock and guaranteed-delivery time limit. The behavior of the optimal R with respect to the guaranteed service time τ is investigated, and subsequently, the best value of τ is identified. Interestingly, when τ is greater than or equal to L, the best value of τ is independent of the holding cost and the base stock R. Regularity conditions are developed that assure the cost function is unimodal in R, guaranteeing the optimal base-stock solution can be found easily for a broad family of demand distributions. The solution procedure is extended to the case when the mean customer demand depends on the fulfillment time window promised by the retailer. We find greater customer sensitivity to the fulfillment time leads to a decrease in the optimal fulfillment time guarantee.

Admission control for a capacitated supply system with real-time replenishment information

Control towers can provide real-time information on logistic processes to support decision making. The question however, is how to make use of it and how much it may save. We consider this issue for a company supplying expensive spare parts and which has limited production capacity. Besides deciding on base stock levels, it can accept or reject customers. The real-time status information is captured by a k-Erlang distributed replenishment lead time. First we model the problem with patient customers as an infinite-horizon Markov decision process and minimize the total expected discounted cost. We prove that the optimal policy can be characterized using two thresholds: a base work storage level that determines when ordering takes place and an acceptance work storage level that determines when demand of customers should be accepted. In a numerical study, we show that using real-time status information on the replenishment item and adopting admission control can lead to significant cost savings. The cost savings are highest when the optimal admission threshold is a work storage level with a replenishment item halfway in process. This finding is different from the literature, where it is stated that the cost increase of ignoring real-time information is negligible under either the lost sales or the backordering case. Next we study the problem where customers are of limited patience. We find that the optimal admission policy is not always of threshold type. This is different from the literature which assumes an exponential production lead time.

KINERJA SUPPLIER BAHAN PANGAN DI RUMAH SAKIT X DEPOK

Supplier performance is an indicator that can be used as a first step for the supplier’s success in the relationship with customers in the future. Supplier with bad performance might disturb the business activity, whereas the good one would impact positively to company success. The purpose of this research is to analyze food supplier performance of RS X Depok including the impacts for the hospital. This research is using descriptive methodology with qualitative approach. Data collection was performed by doing some structured interviews. The chosen supplier performance indicators to be used to analyze the food supplier performance were Replenishment Lead Time, On-Time Performance, Supply Flexibility, Delivery Frequency, Supply Quality, Inbound Transportation Cost, Pricing Terms, Information Coordination Capability and Supplier Viability. The result of this research showed both suppliers had a good performance. Supplier BJ was excellent in every aspect of supplier performance. Meanwhile, Supplier AM showed a good performance in almost every aspect but has supply quality shortcomings. Sometimes, the meat weight was inappropriate so it had to be exchanged and the dirty eggshell had to be cleaned several times. Based on their performance, the food suppliers also positively impact the hospital.

Robust dual sourcing inventory routing optimization for disaster relief.

This paper considers the problem that a depot replenishes several shelters by aerial and land transportation modes for disaster relief. There are two distinguishing features of our problem: one is routing decisions determine replenishment lead times; the other is that we introduce dual sourcing policy into the inventory routing problem. A robust optimization model is proposed to determine the optimal replenishment quantity, replenishment mode, and transportation routes. Then, we decompose the problem into a routing master-problem and a set of inventory sub-problems. A tractable closed-form solution for sub-problem is derived. We further develop an adaptive large neighborhood search algorithm to solve the problem. To demonstrate the feasibility of the algorithm, we conduct a series of numerical experiments on the benchmark test suite with different scales and compare the performance of the proposed algorithm with a genetic algorithm.

Causes and consequences of bullwhip effect on the boutique industry of Dhaka city

The phenomenon of the bullwhip effect (BWE) has become a pressing concern in contemporary supply chain management. Every echelon of the supply chain faces the negative consequences of BWE somehow. So, it is crucial to determine the reasons responsible for the BWE to mitigate the consequences. The boutique industry in Bangladesh is a rapidly growing industrial sector. In this study, we focused on finding the reasons and consequences of the bullwhip effect on the boutique industry in Dhaka city. The main targets of this study are to examine the underlying reasons for the BWE, identify the most significant causes from the perspective of Dhaka city, and determine the major consequences of the bullwhip effect. Studies of previous literature and consultation with experts have identified sixteen common causes behind the bullwhip effect. This study uses a survey-based method; respondents are chosen through clustered sampling. Necessary data have been collected with a semi-organized inquiry form. Among all the 16 causes, six causes are found to be the most significant causes from the perspective of retailers and wholesalers. SPSS Version 26 has been used for statistical analysis to make the final decision. We also found ten consequences commonly faced by these two echelons of the boutiques' supply chain because of the bullwhip effect. These are high inventory costs, workforce wastages and higher labor costs, higher replenishment lead-time, higher transportation costs, tension in the buyer-supplier relationship, product unavailability, loss of profit, poor customer service, etc.

Optimal stocking policies for inventory systems with uncertain returns

ABSTRACT The purpose of this paper is to enlarge the scope of existing EOQ-type models by incorporating uncertain product returns that may include time or quantity restrictions. In this inventory control problem, the item’s demand is deterministic and occurs at a known constant rate over time. Product returns arrive according to a Poisson process. The amount returned to the seller at each return epoch is a random or fixed fraction of the accumulated number of units (net of previous returns) that have been demanded so far within a stock cycle. The ordering policy is a continuous review policy that orders a fixed quantity every time an order is placed. The replenishment lead time is instantaneous, and shortages are not allowed. The objective is to find optimal order quantities by minimising the long-run average total costs per unit time. Costs include a fixed plus proportional cost per order, and a holding cost per unit held in inventory per unit time. A level-crossing approach is used to derive a closed-form expression for the expected ordering and inventory holding costs as a function of the order size and other relevant parameters.

Performance bound for myopic order-up-to inventory policies under stationary demand processes

We consider a single-item infinite-horizon inventory system operating in discrete time and study the performance of the popular myopic order-up-to policy when demand is driven by a general autoregressive moving average (ARMA) stationary process. We derive a suboptimality bound for a system that operates under full demand backlogging, linear holding and backordering costs, and a constant replenishment lead time. We illustrate our results for the case in which demand follows an ARMA(1,1) process, which includes two commonly used demand models, MA(1) and AR(1), as special cases.

Lost sales obsolescence inventory systems with positive lead time: a system-point level-crossing approach

AbstractIn this article, we provide a comprehensive analyses of two continuous review lost sales inventory system based on different replenishment policies, namely $(s,S)$ and $(s,Q)$. We assume that the arrival times of demands form a Poisson process and that the demand sizes have i.i.d. exponential distribution. We assume that the items in stock may obsolete after an exponential time. The lead time for replenishment is exponential. We also assume that the excess demands and the demands that occurred during stock out periods are lost. Using the system point method of level crossing and integral equation method, we derive the steady-state probability distribution of inventory level explicitly. After deriving some system performance measures, we computed the total expected cost rate. We also provide numerical examples of sensitivity analyses involving different parameters and costs. As a result of our numerical analysis, we provide several insights on the optimal $(s,S)$ and $(s,Q)$ policies for inventory systems of obsolescence items with positive lead times. The better policy for maintaining inventory can be quantified numerically.

Asymptotic Optimality of Base-Stock Policies for Perishable Inventory Systems

We consider periodic review perishable inventory systems with a fixed product lifetime. Unsatisfied demand can be either lost or backlogged. The objective is to minimize the long-run average holding, penalty, and outdating cost. The optimal policy for these systems is notoriously complex and computationally intractable because of the curse of dimensionality. Hence, various heuristic replenishment policies are proposed in the literature, including the base-stock policy, which raises the total inventory level to a constant in each review period. Whereas various studies show near-optimal numerical performances of base-stock policies in the classic system with zero replenishment lead time and a first-in-first-out issuance policy, the results on their theoretical performances are very limited. In this paper, we first focus on this classic system and show that a simple base-stock policy is asymptotically optimal when any one of the product lifetime, demand population size, unit penalty cost, and unit outdating cost becomes large; moreover, its optimality gap converges to zero exponentially fast in the first two parameters. We then study two important extensions. For a system under a last-in-first-out or even an arbitrary issuance policy, we prove that a simple base-stock policy is asymptotically optimal with large product lifetime, large unit penalty costs, and large unit outdating costs, and for a backlogging system with positive lead times, we prove that our results continue to hold with large product lifetime, large demand population sizes, and large unit outdating costs. Finally, we provide a numerical study to demonstrate the performances of base-stock policies in these systems. This paper was accepted by Victor Martinez de Albéniz, operations management. Funding: J. Bu was partially supported by a Hong Kong Polytechnic University Start-up Fund for New Recruits [Grant P0039585]. X. Gong was partially supported by a Chinese University of Hong Kong (CUHK) Direct Grant [Grant 4057147] and the Hong Kong Research Grants Council (RGC) General Research Fund [Grant CUHK14500120]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4400 .

On the inventory performance of demand forecasting methods of medical items in humanitarian operations

The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. While effective response to emergency results in inventory build-up which saves human lives, excess inventories could be intentionally burnt or donated which is costly for humanitarian organizations. Henceforth, linking demand forecasting to the inventory control task is shown to be a significant scope to offer a higher performance. In this vein, it is key to accurately select adequate forecasting methods. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with stock keeping units (SKUs) characterized with an intermittent demand in industrial contexts. To do so, we conduct an empirical study by means of data related to 1254 SKUs managed in three warehouses of a major humanitarian organization based in Geneva, Middle-east and Africa. The investigation is carried out to compare the inventory performance of three parametric and two bootstrapping methods when used with an order-up-to-level inventory control policy. The results demonstrate the high performance of the bootstrapping methods in achieving higher service levels. The investigation enables to gain insights on the forecasting method that should be selected under particular assumptions on the demand and the lead-time value.

Age-based preventive maintenance with multiple printing options

In today's economic context, production systems must be readily available and machinery downtime kept to a minimum. Maintenance and spare parts inventory management play a vital role in achieving these goals, and preventive maintenance has increasingly been considered in maintenance policies. Additive manufacturing (AM) has recently been combined with preventive maintenance, and thus represents an emerging research direction. However, few studies have as yet been conducted in this research stream, and we intend to fill this gap. Our study makes three main contributions. First, we address the main limitations of two current models (i.e., assuming that no failure occurs during the replenishment lead time of the spare parts). Second, we propose a new maintenance policy that considers two printing options with different levels of reliability and unitary purchase costs. Third, we develop a decision support system (DSS) to assist managers in deciding whether to implement a preventive maintenance policy that includes AM or conventional manufacturing (CM) parts. We take an interdisciplinary approach to conducting a parametrical analysis where we consider real data on the reliability of CM and AM parts, in addition to the impact of post-processing operations and optimization routines. We find that AM-based preventive maintenance policies are favored when the MTTF and the backorder costs are low and when the failure and maintenance costs are high. These findings have been incorporated into the DSS, which provides thresholds for every parameter to guide practitioners in choosing between AM and CM parts for preventive maintenance, without requiring time-expensive calculations.

A Markovian approach to modeling a periodic order‐up‐to‐level policy under stochastic discrete demand and lead time with lost sales

AbstractIn this paper, a periodic order‐up‐to‐level policy is developed by using a Markovian approach. Customer demand is modeled by using an empirical distribution. Unmet customer demand is considered as lost sales. The internal storage capacity is limited, and excessive units are stored in external storage space. Replenishment lead time is discrete and stochastic. Because of the characteristics of the demand and lead time, the demand during the lead time and the demand during the protection interval are empirical. The Markovian approach is used to estimate the system measures of performance, including the expected inventory on‐hand, expected lost sales, and expected over‐storage. The successive overrelaxation algorithm is implemented to overcome the dimensional issue of the Markov model. The optimal policy that minimizes the total cost is obtained by using a customized golden section search procedure. The proposed method is applied to a real dataset of consumable products. In addition, the effectiveness of the approach is demonstrated by a comparison with a simulation. The results indicate that the model from our approach can accurately describe the behavior of the periodic order‐up‐to‐level review system.

Smart Policies for Multisource Inventory Systems and General Tandem Queues with Order Tracking and Expediting

We study an inventory system with multiple supply sources and expediting options. The replenishment lead times from each supply source are stochastic, representing congestion and disruption. We construct a family of smart ordering and expediting policies that utilize real-time supply information. Such dynamic policies are generally difficult to evaluate, because the corresponding supply system is a tandem queue with state-dependent arrivals and routing, whose queue-length steady-state distribution is usually not in product form. Our main result is to identify two appealing special cases of the general policy, Policy-M and Policy-E, which possess simple product-form solutions and lead to closed-form performance measures. Policy-M retains full sourcing flexibility, but ignores upstream congestion in making expediting decisions. Policy-E only orders from the normal, farthest source, but makes expediting decisions based on both upstream and downstream information. A numerical study shows that the best Policy-M leads to a lower average cost than the best Policy-E in almost all cases. Also, implementing the best Policy-M parameters, the general policy only performs slightly better than Policy-M. These observations reveal the value of combining sourcing flexibility with some, but limited, dynamic expediting. Our findings are equally applicable to the equivalent tandem queue. They thus may aid dynamic routing and expediting decisions for online retailers and logistics providers, among others.

Impact of uncertain demand and lead-time reduction on two-echelon supply chain

This paper develops a continuous-review vendor-buyer supply chain (SC) model wherein the lead-time (taken as replenished) is considered as a factor affected upon by the time stamp required for setup and production followed by transportation. Here, the production time indicates the interaction between the lot-size and lead-time. Assuming the existence of an opportunity with the buyer of reducing the replenishment lead-time. The buyer receives normally distributed stochastic lead-time demands from its customers. Due to the stochastic nature of lead-time demand, shortages may arise at the buyer’s side which is fully backlogged. We presume imperfection production at the vendor’s end, which leads to the generation of a certain ratio/percentage of defective products, which results in additional warranty costs for the vendor. This study intends to uncover the best policy that minimizes the system’s total expected cost. A solution algorithm with some lemmas is provided which helped in finding the optimal solution and to prove the uniqueness of the solutions. Findings demonstrate that a reduction in lead-time can effectively lower safety stock as well as the total cost.

Dual-Sourcing, Dual-Mode Dynamic Stochastic Inventory Models: A Review

We review academic research on dual-sourcing (or dual-mode) dynamic stochastic inventory models, which study the replenishment of inventory from two sources (or two transportation modes) in the presence of demand uncertainty. The classic assumption, also adopted here, is that the two sources (modes) differ by replenishment leadtimes and costs. We cover both discrete and continuous-time models. A distinguish feature of our review from others is that our review is more technically detailed and provides self-contained proofs of several fundamental results in the dual-sourcing literature. We also highlight theory advanced recently by using asymptotic analysis.