Lead Time Uncertainty Research Articles

Investigating the dynamic and contingent effects of the US import air freight market throughout the phases of the COVID‐19 disruption

AbstractAir freight is a fast and reliable mode of transportation in global supply chains. Because of its speed and reliability, air freight has been argued to help firms respond to supply chain disruptions. Yet, the literature lacks a quantitative analysis that examines the dynamics of air freight demand throughout the phases of a disruption as well the moderating factors that influence air freight demand throughout these phases. Drawing on the microanalytic view of property rights theory applied to the dynamics of air freight demand during the COVID‐19 disruption, the study hypothesizes that importers adjust air freight demand based on considerations of demand and lead time uncertainty and that these dynamics will be contingent on heterogeneous product characteristics. Specifically, the study hypothesizes that air freight demand will increase more for products used as intermediate inputs, and that products, regardless of their value, will have higher air freight demand during the large surge in demand after reopening from the initial COVID‐19 lockdowns. The hypotheses are tested using archival import data and other publicly available sources with panel regression models that include product and quarter fixed effects. The study finds support for these hypotheses.

Global multi-sourcing network design with inventory planning under uncertainty

ABSTRACT In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side.

To Wait or Not to Wait: Optimal Ordering Under Lead Time Uncertainty and Forecast Updating

To Wait or Not to Wait: Optimal Ordering Under Lead Time Uncertainty and Forecast Updating

Design and optimization of dynamic reliability-driven order allocation and inventory management decision model.

Efficient order allocation and inventory management are essential for the success of supply chain operations in today's dynamic and competitive business environment. This research introduces an innovative decision-making model incorporating dependability factors into redesigning and optimizing order allocation and inventory management systems. The proposed model aims to enhance the overall reliability of supply chain operations by integrating stochastic factors such as demand fluctuations, lead time uncertainty, and variable supplier performance. The system, named Dynamic Reliability-Driven Order Allocation and Inventory Management (DROAIM), combines stochastic models, reliability-based supplier evaluation, dynamic algorithms, and real-time analytics to create a robust and flexible framework for supply chain operations. It evaluates the dependability of suppliers, transportation networks, and internal procedures, offering a comprehensive approach to managing supply chain operations. A case study and simulations were conducted to assess the efficacy of the proposed approach. The findings demonstrate significant improvements in the overall reliability of supply chain operations, reduced stockout occurrences, and optimized inventory levels. Additionally, the model shows adaptability to various industry-specific challenges, making it a versatile tool for practitioners aiming to enhance their supply chain resilience. Ultimately, this research contributes to existing knowledge by providing a thorough decision-making framework incorporating dependability factors into order allocation and inventory management processes. Practitioners and experts can implement this framework to address uncertainties in their operations.

Robust Optimization for Perishable Product Distribution under Uncertainty of Multimodal Transportation Services

Unusual logistics service uncertainties resulting from recent mega-disruptions, such as COVID-19 outbreak or the Russia-Ukraine conflict, are placing increasing pressure on practitioners and researchers to develop robust solutions to mitigate these uncertainties. Perishable products, such as fresh produce, medicine, and vaccines, which deteriorate quickly and have short shelf-lives, are particularly vulnerable to the lead-time uncertainty from the global logistics services, which causes huge waste or even fatal outcomes. Moreover, modeling such time uncertainties using probability distributions is challenging due to the lack of historical data on unprecedented and complex disruptions. The literature on distribution and transportation service planning for perishable products under unknown probability distributions is also scarce. This paper aims to provide shippers with robust solutions for distribution planning of perishable products considering available transportation services within a multimodal transportation network under uncertainty of transit time including waiting time at sites and transportation time between sites. A robust model is proposed to minimize the total distribution costs composed of transportation, transshipment, and deterioration costs. Our model applies the budget of uncertainty to adjust the degree of conservatism in the solutions. A variable neighborhood search (VNS) algorithm is devised to solve the robust model. Finally, computational experiments are carried out to evaluate the performance and applicability of the proposed model and algorithm.

Product availability and stockpiling in times of pandemic: causes of supply chain disruptions and preventive measures in retailing.

The coronavirus pandemic in 2020 brought global supply chain disruptions for retailers responding to the increased demand of consumers for popular merchandise. There is a need to adapt the existing supply chain models to describe the disruptions and offer the potential measures that businesses and governments can take to minimize adverse effects from a retail logistics perspective. This research analyses the possible reasons for supply and demand disruptions using a mathematical model of a retail supply chain with uncertain lead times and stochastic demand of strategic consumers. The established concepts of supply chain management are applied for the model analysis: multi-period inventory policies, bullwhip effect, and strategic consumers. The impact of the pandemic outbreaks in the model is two-fold: increased lead-time uncertainty affects supply, while consumer stockpiling affects demand. Consumers' rational hoarding and irrational panic buying significantly increase retailers' costs due to higher safety stock and demand variability. The bullwhip effect further exacerbates the disruption. The research contributes to the recent literature on business response to supply chain disruptions by developing a model where both retailers and consumers decide on the order quantity and reorder point during a pandemic outbreak. Buying limits, continuous inventory review, government rationing, substitutability, and omnichannel fulfillment are the measures that can limit the damage of supply chain disruptions from stockpiling during the pandemic. Effective communication and price and availability guarantees can mitigate the negative impact of panic buying.

Replenishment strategies for lost sales inventory systems of perishables under demand and lead time uncertainty

We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.

Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty

Supplier reliability is a critical issue for manufacturing companies. Delivery delays from suppliers create backlog and firefighting on the shop floors. To avoid disruption and hedge against supplier lead time uncertainty, companies rely on diversification, multi-sourcing, and safety lead times. In multi-sourcing, the buyer might order the same product (raw material) from different suppliers. The design of a robust and cost-efficient purchasing/ordering plan in multi-sourcing is a complex task which has a strong impact on the performance of the company. In this study, we investigate the use of robust optimization for the integrated lot-sizing and supplier selection problem under lead time uncertainty. More specifically, we use polyhedral budgeted uncertainty sets. The resulting model determines the ideal lot sizes to minimize the total costs taking into consideration suppliers’ reliability and prices. To solve this problem, a row and column generation approach is proposed. To alleviate scalability issues, we enhance the row and column generation through a robust counterpart formulation, and we propose an efficient fix-and-optimize approach. Our extensive computational experiments show that the fix-and-optimize approach yields good quality solutions within a reasonable amount of computational time. We provide insights into supplier diversification based on the risk profile of the decision-maker. One of the conclusions is that an extremely risk-averse decision-maker selects a single supplier, namely the most reliable one even if it does not offer the lowest price.

Supply planning and inventory control under lead time uncertainty: a literature review and future directions

The majority of publications in the scientific literature investigate stochastic demand processes with constant order lead times. In practice, inventory management software like MRP is used by companies even if ignored the uncertainty of lead times. In this work, we investigate the influence of this type of uncertainty and existing solutions proposed in the scientific literature. To do this, ARTIREV tool is used to identify the major schools of thought competing in the filed and highlight the current and emerging research themes. One conclusion is obvious from this preliminary work. Even though lead times are very disruptive to supply chain, as seen during the Covid crisis, most of the work is still only interested in the uncertainties related to demand.

Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints

Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses.Objective: This paper aims to minimise total inventory cost in SCM by applying a multi-agent-based machine learning called Reinforcement Learning (RL).Methods: The ability of RL in finding a hidden pattern of inventory policy is run under various constraints which have not been addressed together or simultaneously in previous research. These include capacitated manufacturer and warehouse, limitation of order to suppliers, stochastic demand, lead time uncertainty and multi-sourcing supply. RL was run through Q-Learning with four experiments and 1,000 iterations to examine its result consistency. Then, RL was contrasted to the previous mathematical method to check its efficiency in reducing inventory costs.Results: After 1,000 trial-error simulations, the most striking finding is that RL can perform more efficiently than the mathematical approach by placing optimum order quantities at the right time. In addition, this result was achieved under complex constraints and assumptions which have not been simultaneously simulated in previous studies.Conclusion: Results confirm that the RL approach will be invaluable when implemented to comparable supply network environments expressed in this project. Since RL still leads to higher shortages in this research, combining RL with other machine learning algorithms is suggested to have more robust end-to-end SCM analysis. Keywords: Inventory Policy, Multi-Echelon, Reinforcement Learning, Supply Chain Management, Q-Learning

A Replenishment Inventory Model with a Stock-Dependent Demand and Age–Stock-Dependent Cost Functions in a Random Environment

This paper investigates an [Formula: see text] continuous-review perishable inventory model with a stock-dependent Poisson demand process, full backordering (with an extension for lost sales) and uncertainty in lead time and shelf life. Four types of costs are considered: a fixed cost of an order and each outdated item; age-dependent costs of an item (i.e., holding and salvage costs), given by a function of its remaining shelf life; and a delay cost of a backlogged demand unit, which is a function of its delay duration. Applying the supplementary variable technique, we obtain the joint probability-density function of the number of items in the system and the remaining time and thereby obtain the optimal parameters minimizing the long-run average total cost. Numerical experiments show that supply chain profits are enhanced by integrating the age components into replenishment decisions, and ignoring the shelf age- and delay-dependent costs may result in a substantial loss (up to 25%). It further appeared that estimating the lead-time distribution by an exponential one is significantly more costly, in particular as the c.v. differs from 1. In contrast, an exponential shelf life may provide a good heuristic for other shelf-life distributions.

Financial Assessment on Designing Inventory Policy by Considering Demand, Lead Time, and Defective Product Uncertainties: A Monte Carlo Simulation

Inventory is one of the main components in supply chain. However, it is not easy to design inventory policy under uncertainties. The frequent occurrence of overstocks increases the company's financial expenditure. Otherwise, stockout decreases customer satisfaction and damage the company's image. This study aims to provide monte carlo model to design inventory policy with the aim of maximizing net income with a variety of uncertainties, one of the uncertainties is defective product because of the travel from suppliers. To handle the complexity and uncertainty of problem, a Monte Carlo simulation is used with spreadsheet-based representation. To test the reliability of the model, guitar company is used as relevant use case with uncertainty adhered ‘the greater number of order quantity, the greater likely the defective guitar will be’. The verification & validation process, experimental design, and alternative selection are also done with statistical tests. Based on the simulation result, it is known that changing the reorder point to 80 and the order quantity to 90 gives the best result which can increase net income by 0.44% compared to the initial net income. In addition, the number of stockouts has decreased.

Consideration of streamflow forecast uncertainty in the development of short-term hydropower station optimal operation schemes: A novel approach based on mean-variance theory

The uncertainty of streamflow forecast will cause the ineffectiveness on the short-term hydropower station optimal operation (SHSOO) and lead to the risk of producing less hydropower than planned. Traditionally, stochastic optimal theory represented by the stochastic dynamic programming model (SDPM) is commonly used for SHSOO, which is focus on the uncertainty during a single lead time and lack of an overall consideration of multiple independent lead times of differing length. Thus, this paper proposes a short-term hydropower station optimal operation model based on mean-variance theory (MV-SHSOOM) for reducing operational risk. The proposed multi-step model considers the forecasting uncertainty of different lead times as an integral component to guide a station’s optimal operation. Specifically, the forecast uncertainty was integrated by the joint probability distribution of forecast error using a multivariate hydrologic uncertainty processor (HUP) at first. Then, possible observed streamflow sequences were obtained using the joint probability distribution of forecast error and taken as input to the short-term optimal operation model of the hydropower station. Finally, the mean-variance theory was applied to construct the optimal operation model for balancing the power generation and operational risk. The proposed model was applied to the Jinxi hydropower station, China, to determine its performance. The results show that: 1) the dependent structure among the forecast errors at different lead times is significant in terms of Spearman correlation, and the joint probability distribution of forecast error can be effectively described by a multivariate HUP; 2) the proposed MV-SHSOOM is superior to the traditional deterministic dynamic programming model (DDPM) and SDPM, and can more effectively reduce operational risk while increasing power generation.

A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms

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.

An Extensive Study on Logistics Providers in FSCM using AHP Technique

The growth of the Logistics business has caused many firms from different industries to enter the field. A logistics provider is an external provider who manages, controls, and delivers logistics activities maintains warehouse and other necessities on behalf of a supplier. They collect outbound shipments from manufacturers & consolidates shipments in their distribution centres and delivers to the customers through an effective routing of transport. Lead time is the most repetitive risk faced by the logistics providers and hence bench marking the major focused variables is must. This results in lowering the uncertainty in Leadtime. Researchers have spoken about challenges faced in evaluating the process. This research indents to identify the variables which are considered for the evaluation process of the operations of logistics providers. A primary survey will be conducted using the identified variables and then subjected to Analytical Hierarchy Process to funnel down the major focused variables. These variables will be benchmarked and be considered as standard variables which will cause a direct impact on the daily operations and result in variation in profitability. This paper will give a clear perspective regarding the evaluation of Logistics providers and their various policies incorporated

Exploring risk pooling in hospitals to reduce demand and lead time uncertainty

Nearly every eighth German hospital faces an elevated risk of bankruptcy. An inappropriate use of inventory management practices is among the causes. Hospitals suffer from demand and lead time uncertainty, and the current COVID-19 pandemic worsened the plight. The popular business logistics concept of risk pooling has been shown to reduce these uncertainties in industry and trade, but has been neglected as a variability reduction method in healthcare operations research and practice. Based on a survey with 223 German hospitals, this study explores how ten risk pooling methods can be adapted and applied in the healthcare context to reduce economic losses while maintaining a given service level. The results suggest that in general risk pooling may improve the economic situation of hospitals and, in particular, inventory pooling, transshipments, and product substitution for medications and consumer goods are the most effective methods in the healthcare context, while form postponement may be unsuitable for hospitals due to the required efforts, delay in treatments, and liability issues. The application of risk pooling in healthcare requires willingness to exchange information and to cooperate, adequate IT infrastructure, compatibility, adherence to healthcare laws and regulations, and securing the immediate treatment of emergencies. Compared to manufacturing and trading companies, hospitals seem to currently neglect the variability reducing effect of risk pooling.

Combating lead-time uncertainty in global supply chain's shipment-assignment: Is it wise to be risk-averse?

In global supply chains, high uncertainty coming from liner shipping forces manufacturers who commit to on-time delivery to face great losses from tardiness or earliness. To find a reliable operational solution for shipment assignment, realizing the trade-off between the operations cost and reliability is highly essential. In this paper, a risk-hedging policy for shipment assignment is proposed. We incorporate risk aversion into the stochastic optimization model where the objective is to minimize the total deterministic operations cost and the weighted value-at-risk. The closed-form expressions of value-at-risk are derived and some structural properties of the optimization problem are revealed. Linearization techniques are utilized to make the proposed model tractable and solvable by an exact algorithm. Our computational studies further demonstrate the cost efficiency on systems reliability improvement through the risk hedging approach with job combination. It is interesting to uncover that being moderately risk-averse is wise, but possessing a very high risk-averse attitude is doing more harm than good as the increase of deterministic operations cost is much more significant than the decrease of value-at-risk. Being risk neutral may also be unwise as the chance of achieving the optimal expected total cost may be very low. In the extended models, we relax the assumptions and further consider scenarios with correlated shipping lead-times and stochastic exchange rate.

ANALISA PERBAIKAN KINERJA PENGIRIMAN PRODUK R754046 DI PT PELANGI ELASINDO DENGAN PENDEKATAN SAFETY STOCK

PT Pelangi Elasindo is one of the leading manufacturers in Indonesia specializing in webbing production, shoelaces, cords, high quality Molded Pulp, elastics and tape. As one of the vendors for leading shoe factories in the world, the accuracy of shipping goods both in terms of quantity and time is very important because this is a benchmark for calculating shipping performance. There was a delay in delivery at PT Pelangi Elasindo on the R7540460000 Molded Cardboard Shape size 2 product, making delivery performance decrease. Delivery performance in January is 93% while the minimum delivery performance standard is 95%. This study aims to analyze the factors that delay the delivery of goods to customers and improve delivery performance. Based on the results of the analysis, the factors that cause the delay in delivery are damage to the machine, manual cutting and demand variations and uncertainty of lead time. Among these factors, demand variations and lead time uncertainty are the most influential factors, so to improve delivery performance, safety stock must be prepared to avoid stock outs and delivery delays. Based on the results of the calculation of the safety stock that must be prepared is 8,403 prs per week. Key words : Delivery, performance, safety stock,improve

Supplier Replacement Model in a One-Level Assembly System under Lead-Time Uncertainty

Supplier selection/replacement strategies, purchasing price negotiation and optimized replenishment policies play a key role in efficient supply chain management in today’s dynamic market. Their importance increases even more in Industry 4.0. In this paper, we propose a joint model of replenishment planning and purchasing price negotiation in the context of supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. The real component lead times are stochastic. There is consequently a non-negligible risk that the assembly process may be stopped if all components for assembly are not delivered on the due date. This incurs inventory-related costs, holding and backlogging, which should be minimized. We consider a set of suppliers characterized by their prices and the probability distributions of their lead-times, and we present a model and an approach that optimize not only replenishment policy, but also purchasing prices. For a given unit, it is possible to model several alternative suppliers with alternative pricing and lead-time uncertainties, and evaluate their impacts on the total cost: composed of holding, backlogging and purchasing costs for the assembly system. The findings of this study indicate that it can be beneficial to pay suppliers an additional purchase cost in order to reduce the holding and backlogging costs related to uncertainty. In consequence, decision makers can use the proposed approach to negotiate prices and delivery delays or to select suppliers.

An Application of Goal Programming Variants to Evaluate Outsourcing Strategies in the Clothing Industry

International outsourcing has provided the immense cost savings to the clothing companies. Today, the companies are reconsidering the reshoring and relocation due to the factors such as increasing cost, lead time uncertainty, and demand nature. In such situations, the domestic destinations provide capacity flexibility. Choice of the compatible strategy for a given situation prevents the associated loss in the business. This paper evaluates the outsourcing and reshoring phenomenon based on realistic outsourcing objectives in the textile supply chain. This paper develops a goal programming model to analyze the outsourcing strategies for cost and capacity flexibility. Minimization of the capacity inflexibility was considered as the capacity flexibility. Cost and capacity inflexibility were used as two objective functions. The optimization model was solved using the three important variants of goal programming. Comparison of results from three different techniques provides several insights for precise outsourcing decision making. For instance, the companies can choose a compatible strategy to maintain the cost and capacity flexibility at an acceptable level. Instead of only the cost saving, the reshoring decision making should be based on all the real criteria. The key focus of this paper is the clothing industry. The results may be different for other manufacturing sectors. Due to the increased globalization, the textile companies have been facing various challenges such as outsourcing destination selection for maximum profit and the reshoring debates. To address such challenges, this study provides various insights to the textile industry based on realistic objectives of outsourcing (i.e., cost and capacity flexibility). It is difficult to quantify the flexibility. This paper develops new ideas to quantify the capacity flexibility. The application of three different variants of goal programming provides the alternative solutions.