Supply Chain Lead Time Research Articles

Security Threats, Countermeasures, and Challenges of Digital Supply Chains

The rapid growth ofInformation Communication Technologies (ICT)has impacted many fields. In this context, the supply chain has also quickly evolved toward the digital supply chain where digital and electronic technologies have been integrated into every aspect of its end-to-end process. This evolution provides numerous benefits such as profit maximization, loss reduction, and the optimization of supply chain lead times. However, the use of such technologies has also considerably opened up various security threats and risks which have widened the attack surface on the entire end-to-end supply chain. We present a holistic survey on supply chain security. We discuss the different security issues and attacks that target the different supply chain technologies. Then, we discuss various countermeasures and security solutions proposed by academic and industry researchers to mitigate the identified threats. Finally, we provide some recommendations and best practices that can be adopted to achieve a secure supply chain.

Critical factors affecting performance of logistics operation planning considering interdependency: A case study in automotive aftermarket

Abstract Automotive aftermarket operations cover a wide scope from aftersales car maintenance to the last stage of car's lifecycle, including all activities in aftermarket parts and services. Logistics operation planning in automotive aftermarket suffers from demand uncertainty and inaccurate forecasting issues. These issues lead to not only inappropriate inventory level, but also poor performance of order fulfilment and customer satisfaction. Therefore, this study explores the important antecedents of order fulfilment using decision making trial and evaluation laboratory (DEMATEL) based on thirteen criteria. An automotive aftermarket company handling business in Southeast Asia is employed as the empirical case. The methodology can determine the critical factors in the logistics operations of automotive aftermarket considering interdependency among factors involved in the proposed system. According to the analytical results, the company should improve accuracy of demand planning, supply chain lead time and stock availability superiorly.

Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics

Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

The Multiple Supply Chain Design Problem

It has become widely accepted that delivering diverse products to customers who have different needs by a “one-size-fits-all” supply chain results in lower profits and customer service. Therefore, there is a need to design supply chain systems that can effectively and profitably serve products and customers with diverse characteristics. This paper presents a mathematical model that selects the optimal set of supply chains that match diverse characteristics of products and customers. The model considers various product, customer, and supply chain characteristics, including the required lead time, the supply chain lead times, the customer time sensitivity, and the complexity factor resulting from having multiple supply chains. An example and a sensitivity analysis are used to demonstrate the model's capabilities.

Supply chain vulnerability assessment for manufacturing industry.

In today's business, environment natural and manmade disasters like recent event (Covid 19) have increased the attention of practitioners and researchers to Supply chain vulnerability. Purpose of this paper is to investigate and prioritize the factors that are responsible for supply chain vulnerability. Extant literature review and interviews with the experts helped to extract 26 supply chain vulnerability factors. Further, the relative criticality of vulnerability factors is assessed by analytical hierarchy process (AHP). Critical part supplier; location of supplier; long supply chain lead times; Fixing process owners and mis-aligned incentives in supply chain are identified as the most critical factors among twenty-six vulnerability factors. Research concludes that not only long and complex supply chain but supply chain practices adopted by firms also increase supply chain vulnerability. Relative assessment of vulnerability factors enables professionals to take appropriate mitigation strategies to make the supply chains more robust. This research adds in building a model for vulnerability factors that are internal to supply chain & controllable.

Improving the performance of a Malaysian pharmaceutical warehouse supply chain by integrating value stream mapping and discrete event simulation

Purpose Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique. Design/methodology/approach This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map. Findings The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper. Research limitations/implications This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it. Practical implications This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole. Social implications This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry. Originality/value This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.

Supply chain redesign for lead-time reduction through Kraljic purchasing portfolio and AHP integration

PurposeThe purpose of this paper is to provide a flexible and extensible model for the classification of suppliers, within the purchasing guidelines and market trends of an Italian small company, leader in the production of street lamps. The model is applied to identify critical supply chains with the final objective of lead-time reduction.Design/methodology/approachThe model is obtained by the application of the purchasing portfolio analysis through the construction of Kraljic matrix. Profit impact and supply risk criteria are selected according to the main company requirements, and then prioritized by the analytical hierarchy process (AHP). Finally, supply chain lead-times are analyzed with Gantt diagrams.FindingsThe application of the model allows the determination of company criticalities in terms of high lead-times and of the involved suppliers. The analysis of critical suppliers positioning in the Kraljic matrix allows the definition of some possible strategies to implement for lead-time reduction.Research limitations/implicationsPurchasing portfolio analysis and Kraljic matrix are practical instruments to quickly frame company purchasing situation, but their application is not simple due to the numerous and different factors involved, especially in small and medium enterprises (SMEs), where resource are scarce and several constraints limit operations. The objective of the research is the development of a practical tool for strategic purchasing, simple and robust to be implemented in SMEs, with limited resources and access to quantitative supplier data.Originality/valueEvaluation criteria definition is one of the most difficult phases, such as their univocal and quantitative comparison. The problem of selecting and prioritizing both quantitative and qualitative criteria for suppliers classification is overcome with the combined application of Kraljic matrix and AHP. The newly integration of the two methodologies allows the realization of a reliable and robust model for suppliers classification, which can be easily adapted to company business strategy changes.

Differential evolution to solve the lot size problem in stochastic supply chain management systems

An advanced resource planning model is presented to support optimal lot size decisions for overall performance improvement of real-life supply chain management systems in terms of either total delivery time or total setup costs. Based on a queueing network, a model is developed for a mix of products, which follow a sequence of operations taking place at multiple interdependent supply chain members. At the same time, various sources of uncertainty, both in demand and process characteristics, are taken into account. In addition, the model includes the impact of parallel servers for multiple resources with period dependent time schedules. The corrupting influence of variabilities from rework and breakdown is also explicitly modeled. This integer non-linear problem is solved by standard differential evolution algorithms. They are able to find each product’s lot size that minimizes its total supply chain lead time. We show that this solution approach outperforms the steepest descent method, an approach commonly used in the search for optimal lot sizes. For problems of realistic size, we propose appropriate control parameters for an efficient differential evolutionary search process. Based on these results, we add a major conclusion on the debate concerning the convexity between lot size and lead time in a complex supply chain environment.

How many to produce? The impact of machine flexibility on the performance of a supply chain

This work analyses the impact of machine flexibility on the performance level of a supply chain. A simulation model is developed to reproduce different supply chain configurations, with one player per echelon. Machine flexibility is modelled as the capability of the manufacturer to produce different kinds of products, in response to the customer's requests, and of the supply chain to manage those products. By means of simulation, six supply chain configurations are investigated, resulting from the combination of different numbers of supply chain players (from 3 to 5) and of products managed by the supply chain (1 or 3). For each scenario, we compute some economic outcomes (i.e., cost of holding stocks, stock-out cost, production or order cost and total supply chain cost), as well as the supply chain lead time and the bullwhip effect. Design of experiments and statistical analysis of outcomes are used to assess the significance of the effects measured. Results of the study provide some guidelines about the effect of machine flexibility on the supply chain performance.

A new AATP model with considering supply chain lead-times and resources and scheduling of the orders in flowshop production systems: A graph-theoretic view

The present study attempts to synchronize the scheduling problem with determining the advanced available-to-promise (AATP) in a flowshop system to enhance supplier profitability and service level. In the proposed model the AATP, scheduling and graph theory concept have been combined to find the optimum resource allocation and enable accurate estimations of machines scheduling, production costs and delivery dates. To find the near optimum solutions for the large size problems a genetic algorithm is developed, first the orders are ranked based on their scores which are estimated then the optimum cost is calculated by balancing profitability and constraints such as the availability of the machines or the available material in each workstation. Some computer simulated experiments are provided to evaluate the performance of the proposed algorithm.

Construction of speed-to-market supply chain management model for garment industry

Purpose – The purpose of this paper is to build and provide a step-by-step methodology to develop a speed-to-market (STM) process model for a fashion garment that can quickly respond to the marketing situation and shorten the cycle time from receiving orders to deliver the finished product. The improved STM process model to contrast the “before” and “after” scenarios in order to induce potential benefits such as reducing production lead-time and keeping low inventory. Design/methodology/approach – First, collected garment business management systems and garment manufacture operational process. Second, according to the point-line-plane-volume work improvement to list the garment industry up and down stream improvement projects and to reduce the supply chain lead-time. Third, establish the STM model and use information technology to redesign the garment industry supply chain process. Fourth, amend the STM model process. Fifth, compare the supply chain lead-time of finished STM process and normal process. Findings – After the garment industry implements STM and supply chain re-engineering, the clothing design to sale process can be simplified and reduced time to market. The garment order to buyer lead-time is shortened from 104 to 42 days totally. Practical implications – This research is a practical business re-engineering process and work improvement. The improvement STM model can half the cycle time from receiving orders to deliver the finished garment product. Originality/value – This paper provides a process with a step-by-step methodology to develop a successful speed-to-market model for the fashion garment industry and it is able to serve as a reference model for other industrial supply chain management.

Coordinating supplier׳s reorder point: A coordination mechanism for supply chains with long supplier lead time

In this paper, the issue of the upstream stochastic lead time in supply chain (SC) is investigated. A coordination mechanism is developed for reducing the harmful effect of upstream lead time. The supplier stochastic lead time can substantially harm the whole supply chain service level, especially when it is accumulated with downstream stochastic lead times. In this study, aggregation of both the supplier and the retailer stochastic lead time is analyzed in a two-stage supply chain (SC). To dampen harmful effects of a long aggregate lead time, a ‘per order extra payment’ model is developed for convincing the supplier to increase its reorder point. Numerical experiments show that coordinating the supplier׳s reorder point creates a significant profit for the whole supply chain. In addition, the proposed model is capable of optimizing the supplier׳s reorder point and fairly sharing the extra benefits. Some conditions are also extracted, under which the proposed model shows good performance.

Intermediation in agile global fashion supply chains

Previous studies (such as Fung et al., 2007) have revealed that in industries characterized by high degrees of agility, fragmentation and globalization, such as the fashion sector, trade intermediaries play a significant role in integrating geographically dispersed value-chain activities. The typical tasks of an intermediary include sourcing, supplier quality control, shipping management and distribution. However, their ability to reduce costs and compress time along these activities and enhance the level of agility that fashion retailers are ultimately able to exhibit has received little attention in the literature. As a result, this paper explores the role of intermediaries as sourcing agents in agile fashion supply networks in a global context. The methodology employed involves two primary, exploratory case studies of UK based high street fashion retailers. The paper provides empirical evidence to highlight the role of trade intermediaries in increasing supply chain performance along two dimensions: total supply chain lead time and total supply chain costs.

A vendor–buyer integrated production–inventory model with normal distribution of lead time

Abstract By relaxing the long-term assumption of the deterministic lead time, recently three coordinated vendor–buyer models with exponential distribution of lead time in a two-stage supply chain were presented. The vendor produces a product at a finite rate and delivers the lot to the buyer with a number of equal-sized batches (sub-lots) to meet the deterministic demand. The next batch is ordered when the previous one drops to a reorder point. Shortages were allowed and completely backordered. However, in exponential distribution of lead time, the probability of arrival of a batch earlier is higher than the probability of arrival of a batch late or in the mean lead time. But usually, probability of arrival of a batch earlier or late appears to be smaller than the probability of arrival of a batch in the mean lead time. Thus normal distribution of lead time seems to be a better fit to the problem. Hence their models seem unfit to the concerned problem in practice. Based on this notion, we develop a vendor–buyer integrated production–inventory model following normal distribution of lead time but retaining their other assumptions. To make the model more realistic, set up time per set up of a machine, the highest limit on the capacity of the transport vehicle and the transportation cost and time per batch are imposed. Then we derive an optimal solution technique to the model to obtain minimum expected joint total cost that follows development of the solution algorithm. Extensive comparative studies on the results of some numerical problems are carried out to highlight the potential significance of the present method. Sensitivity analysis to the solutions with variations of some parameter values are also carried out.

Optimising safety stock placement and lead time in an assembly supply chain using bi-objective MAX–MIN ant system

The problem of placing safety inventory over a network, which assembles a product, is a challenging issue in supply chain design (SCD) because manufacturers always want to reduce inventory all over the supply chain (SC). Moreover, the process of designing a SC and then placing inventory, to offer high service level at the lowest possible cost, across a complex SC, is not an easy task for decision makers. In this paper we use the SC representation proposed by Graves and Willems (2000), Manufacturing & Service Operations Management 2 (1), 68–83, where a SC is divided into many supplying, manufacturing, and delivering stages. Our problem is to select one resource option to perform each stage, and based on the selected options to place an amount of inventory (in-progress and on-hand) at each stage, in order to offer a satisfactory customer service level with as low as possible total supply chain cost. A resource option here represents a supplier, a manufacturing plant (production line), or a transport mode in a supplying, manufacturing, or delivering stage, respectively. We developed an approach based on ant colony optimisation (ACO) to minimise simultaneously the total supply chain cost and the products’ lead time to ensure product deliveries without delays. What are new in our approach are the bi-objective function and the computational efficiency of our ACO-based approach. In addition, ACO has not been applied to solve the inventory placement problem. As a validation of the model, we: (a) describe a successful application at CIFUNSA, one of the largest iron foundry in the world, and (b) compare different CPU time instances and metrics about multi-objective optimisation.

Valuing time in supply chains: Establishing limits of time-based competition

Over the past two decades the growth in international trade and the offshore migration of US manufacturing have created global supply chains with longer lead-times and slower response. This suggests that traditional supply chains have encountered limits to time-based competition in which the cost of faster replenishment exceeds the benefits. This paper explores and quantifies those limits to time-based competition in make-to-stock supply chains for functional products (products with stable demand over relatively long life cycles).The marginal value of time is used to define the limits of time-based competition in a supply chain, and we define it as the change in total inventory costs per unit change in supply chain lead-time. To calculate the value of time, we develop a set of simple analytical expressions that apply to most standard reorder-point inventory policies under deterministic and variable lead-times. By not requiring optimal inventory policies and expressing the value of time in terms of the unit cost of the product, we obtain very general results that are essentially product-free. We validate the analytical expressions using data from actual supply chains to simulate the inventory cost effects of changes in the lead-time. The results show that the marginal value of time in a supply chain is surprisingly low; it generally falls within a range of 0.4–0.8% of product unit cost per week.Our analytical models explain why there has been expansive growth in global supply chains for functional products. The key tradeoff in outsourcing is between reduced variable production cost and the increased inventory cost of longer supply chain lead-times. The models show analytically that the incremental inventory cost is extremely small relative to the cost benefit. The growth in global supply chains with long lead-times is cost, rather than time, driven.

Inflationary implications on an inventory with expiration date, capital constraint and uncertain lead time in a multi-echelon supply chain

The present study considers a multi-echelon supply system comprising of a supplier, a retailer and the end customer. The inventory is deteriorating and has a certain expiration date beyond which there is no demand for the item. The practicalities of real business world are embedded in the setup as the supplier’s lead time has been considered, which is a random variable. The effect of inflation has been accounted for to provide economic feasibility to the model. There is an upper limit to the expenses that the supplier can bear, providing him with a capital constraint. An optimal solution has been arrived at, and the sensitivity of the solution shows that the model is very stable.

An easy approach to derive a buyer-vendor EOQ model with changeable lead-time in supply chain

Hsiao and Lin (International Journal of Advanced Manufacturing Technology, published online: 7 April 2004) discussed Stackelberg game in supply chain and investigated the supplier's optimal lead time and the retailer's optimal cycle time. But the authors just derived the approximate solution of the optimal lead time and optimal cycle time. Recently, Huang and Hsu (Journal of Information & Optimization Sciences, Vol. 29 (2008), pp. 305–310) offered an algebraic approach to find the exact optimal lead time and optimal cycle time in the constant marker's demand rate situation. In addition, Huang et al. found that their exact solution is better than Hsiao and Lin's approximate solution from the numerical examples. In this paper, we will offer another simple approach to derive Hsiao and Lin's model and find the exact solution same as Huang and Hsu.

A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain

In this paper, the chance-constraint joint single vendor-single buyer inventory problem is considered in which the demand is stochastic and the lead time is assumed to vary linearly with respect to the lot size. The shortage in combination of back order and lost sale is considered and the demand follows a uniform distribution. The order should be placed in multiple of packets, the service rate limitation on each product is considered a chance constraint, and there is a limited budget for the buyer to purchase the products. The goal is to determine the re-order point and the order quantity of each product such that the chain total cost is minimized. The model of this problem is shown to be an integer nonlinear programming type and in order to solve it, a particle swarm optimization (PSO) approach is used. To assess the efficiency of the proposed algorithm, the model is solved using both genetic algorithm and simulated annealing approaches as well. The results of the comparisons by a numerical example, in which a sensitivity analysis on the model parameters is also performed, show that the proposed PSO algorithm performs better than the other two methods in terms of the total supply chain costs.

Production Lots as Determinant of Paper Production Lead Time Performance

In this research work we are interested about connection between lead time performance, and production order size as well as in how many production lots this order was eventually produced. Based on the system dynamics simulation model, the authors got a priori assumption that production lots have in multiproduct environment better explanation power. Our empirical findings give support for this – number of production lots explain in production environment manufacturing lead time much better than production order size. Further support is gained from supply chain phases, which are analyzed similarly, but as surprise explanation power of production lots decreases, and seems to be significantly lower in more distant markets. It is interesting to note that currently used IT applications of analyzed global case company do not give real time snapshot regarding to the development of overall supply chain lead time.