Three-echelon Supply Chain Research Articles

Engine option in aircraft purchase: Company strategies and policy implications

Airlines select an aircraft’s engine simultaneously while purchasing the aircraft. Usually, an aircraft model has multiple engine options, but recently the number of engine options has been decreasing. We develop a three-echelon supply chain model including engine manufacturers, aircraft manufacturers, and airlines to analyze the engine options in airlines’ new aircraft purchases. We show that given an airline’s existing fleet composition, larger fleet size and higher aircraft production efficiency would make it more likely to choose multiple engine options. In contrast, higher production costs and engine substitutability would lead to a shift to a single engine or hinder the airline’s fleet expansion. In the model, we highlight the impact of MRO (Maintenance, Repair, Overhaul) costs with economies of scale on airline engine option decisions as well. In particular, our theoretical findings suggest that the airline would buy more aircraft that are the majority types in their existing fleets, thus crowding out the minority ones. Our findings provide a reasonable explanation for the homogeneity of engine options and provide policy implications regarding airline expansion and aviation supply chain development.

Managing strategic inventories in a three-echelon supply chain of durable goods

Firms can hold strategic inventory to bargain with their suppliers about wholesale prices. In this paper, we model a three-echelon durable goods supply chain consisting of a supplier, a wholesaler, and a retailer, where the latter two firms can choose to hold strategic inventory. Our major findings are as follows. First, contrary to the literature, our study shows that the retailer's use of strategic inventory benefits all supply chain members, irrespective of whether the wholesaler holds strategic inventory. Second, the strategic inventories of the wholesaler and the retailer suppress each other when the holding cost is low. Otherwise, the retail strategic inventory can stimulate the wholesale strategic inventory. Third, after the retailer employs strategic inventory, introducing wholesale strategic inventory will harm each supply chain member. However, after the wholesaler employs strategic inventory, introducing retail strategic inventory can benefit all members. For the entire supply chain, holding strategic inventory exclusively at the retailer is optimal. Fourth, when product durability is high, the supplier may suffer from the wholesaler's strategic inventory. Additionally, the retailer's strategic inventory can solve the time inconsistency problem associated with durable goods. Finally, we extend our main model to consider multiple retailers and different holding costs. Our analysis confirms the robustness of our major findings and managerial insights.

A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

Innovative decision support tools for perishable food supply chain management

PurposeWe introduce decision support tools aimed at optimizing perishable food supply chain management, effectively balancing conflicting objectives such as the exporter’s product collection cost and the importer’s profit. This involves considering factors like perishability, selling price, discount rate, and order quantity to achieve optimal outcomes.Design/methodology/approachThis study considered a three-echelon supply chain comprising farmers, a single exporter, and a single importer providing a single, random-lifetime, perishable product under deterministic customer demand. The proposed mathematical model derived the optimal order quantity, selling price, and discount rate for the entire supply chain. This integrated optimization model treats both demand and supply sides as a multi-objective problem, employing a nonlinear program and a two-stage capacitated vehicle routing problem formulation. Numerical examples and a case study focusing on Thailand durian supply chain were conducted to illustrate the approach of the proposed model.FindingsTaking into account both the importer’s profit and the exporter’s product collection cost, the proposed integrated supply chain model and tools maximize profitability, minimizes waste, and meets demand by optimizing perishable product collection costs and proposing a discount system for selling prices.Research limitations/implicationsLimited to a single perishable product in a three-echelon international food supply chain. Future research can explore different products and supply chain contexts.Practical implicationsThe tools enhance decision-making for supply chain managers, improving efficiency, reducing costs, and enhancing customer satisfaction in the perishable food industry.Social implicationsThe proposed model aids in local workforce management by forecasting required manpower for upcoming seasons. By factoring in product quality and pricing, it ensures customers receive fresh products at fair prices. Furthermore, the near-zero waste concept enhances storage conditions at importers' facilities, contributing to improved environmental hygiene.Originality/valueThe integrated model and decision support tools offer a novel approach to address complexities and conflicting objectives in perishable food supply chains, providing practical insights for researchers and practitioners.

Three-echelon circular economic production–inventory model for deteriorating items with imperfect quality and carbon emissions considerations under various emissions policies

Traditional production systems have a sole focus of maximising economic outcomes but due to the world’s ever-growing awareness about environmental issues, circular production systems, with a dual focus of maximising economic output and minimising environmental impact, have become prevalent in recent years. These types of production systems incorporate circular economy initiatives in their operational processes with the aim of reducing their carbon footprints. However, this is a voluntary measure to reduce carbon footprints and for this reason, various governments have introduced regulatory measures to involuntarily reduce carbon footprints of production systems. These regulatory measures include carbon tax, cap, cap-and-trade and cap-and-offset policies that are designed to reduce carbon emissions from production processes. With this in mind, this paper proposes an integrated sustainable production–inventory model for a three-echelon supply chain for imperfect quality deteriorating items with a circular economy indicator under various carbon emissions regulations. Using four different combinations of functions for both the demand rate and unit gross profit and four different carbon emissions regulations, 16 variants of the model are solved and analysed using heuristic algorithms. The model variants are not only aimed at optimising inventory replenishment decisions but also the circularity economy indicator of the deteriorating inventory. Of the 16 variants, the highest profit is achieved for the logistic demand and linear profit functions combination under cap-and-offset, despite a circularity indicator of 81.75%. The results also show that carbon emissions policies are effective at reducing the supply chain’s carbon footprint, albeit with a slight impact on the profit.

Adaptive continuous barrier function-based super-twisting global sliding mode stabilizer for chaotic supply chain systems

Supply chains face various uncertainties due to the dynamic and unstable nature of today's worldwide market. Introducing these uncertainties to both the upstream and downstream elements of supply chains adds complexity and nonlinearity to them. This paper investigates the dynamic behavior of a chaotic three-echelon supply chain system and designs a finite-time global nonlinear super-twisting sliding mode controller based on an adaptive continuous barrier function technique for stabilizing the defined system under external disturbances and model uncertainties. This method dynamically adapts the controller parameters according to the system's current state using parameter adaptation algorithms. The adaptive barrier function is used to ensure the system's stability, while the super-twisting algorithm is used to speed up the convergence rate and robustness of the system. Moreover, the genetic optimization algorithm has been employed to attain the optimal values of parameters, thereby enhancing the performance of the designed controller. Ultimately, the effectiveness of this research's proposed approach has been evaluated through simulation in the MATLAB-Simulink environment and experimental testing using the Baseline Real-Time Speedgoat test platform. These analyses are paramount and helpful for strategic decision-makers because they allow visualization and control of the states/dynamics of the entire supply chain. The internal parameters of the supply chain are used as control parameters, and different significant states are discovered to achieve synchronization.

How to improve the energy-saving performance of China's transport sector? An input–output perspective

The transport sector proves a major energy consumer in China, but improving energy-saving performance in China's provincial transport sector from the lifecycle perspective remains unresolved. Thus, this study employs the environmentally extended multi-region input–output (MRIO) method, structural path analysis, and the newest MRIO table of China from 2017, to investigate how to improve the energy-saving performance from final demand structure, supply chain, and pathway perspectives. The relevant results are threefold. (1) Regarding the final demand structure level, the embodied energy consumption of China's transport sector is predominantly driven by investment from the production side, while that of the consumption side is primarily caused by exports. (2) At the supply chain level, production-side embodied energy consumption primarily occurs along a three-echelon supply chain, while that from the consumption side mostly occurs via a two-echelon supply chain. (3) At the pathway level, the production-side energy-saving performance of China's provincial transport sector is dominated by two pathways along the construction sector, including transport sector → construction sector → final demands, and transport sector → intermediate inputs → construction sector → final demands, while that of the consumption side is chiefly determined by three pathways along internal transportation chains.

Reducing emissions from production and distribution in three-echelon supply chains

Reducing carbon emissions is one of the key goals of contemporary supply chains and logistics. This paper aims to formulate a novel three-echelon (supplier-3PL-retailer) supply chain model comprising green technology investment, variable demand, variable lead time, a dynamic shipping strategy, and an option for vehicle selection. The demand function considers the product's freshness index and maximum lifetime, along with the availability of stocks. Besides, the delivery from the 3PL to the retailer is dynamically determined based on previous shipment information. A green vehicle is selected using a multi-criteria decision-making (MCDM) technique based on q-alternatives with n-characteristics for the transportation process and mathematically we link this selection process to traditional three echelon supply chain optimization problem. The results show that combining green investment with an appropriate dynamic shipping strategy by using a selected green vehicle reduces the supply chain's emissions and total integrated cost. Furthermore, reducing lead time without compromising other supply chain factors stimulates demand of customers and produces fewer emissions with a significant reduction in the total integrated cost. A real-case-based study shows the performance of the proposed model and method.. The study illustrates that 47.744% of the carbon emissions in Scenario 1 and 47.53% in Scenario 2 are reduced when an emission reduction strategy is implemented. In terms of total integrated costs our proposed model could save 4.85% cost in Scenario 1 while 4.96% for Scenario 2, which is significant.

Payment policy for a three-echelon supply chain management under advertisement-driven demand

Payment and selling are two important policies for a supply chain management. All participating supply chain players can earn profit based on the successful implementation of these two policies. The payment policy provides buyers some extra time to pay for the product. This research introduces a single-supplier, a single-manufacturer, and multi-retailer-based three-echelon supply chain management under advertising and payment policies. Product delivery among supply chain players is made with the help of a transportation policy. The transportation policy is environment-friendly and helps retailers with their cost management. It is demonstrated that advertising positively influences sales through an advertisement-driven market demand for the product. The model aims to reduce supply chain cost and maximizes profit by considering a single-setup multiple-delivery policy, variable transportation cost, variable carbon emissions costs, and trade-credit policy. The objective function is optimized for cases: Case 1 and Case 2, based on the payment period. A classical optimization method is employed to obtain the solution of the model. A numerical example, sensitivity analysis, and graphical representations are given to illustrate the model. Results show that Case 2, where cycle time is greater than the payment period, is 45.36% more profitable than Case 1.

Long-term emission reduction strategy in a three-echelon supply chain considering government intervention and Consumers’ low-carbon preferences

This paper investigates a three-echelon supply chain that consists of a supplier, a manufacturer, and a retailer. Owing to the government intervention and consumers’ low-carbon preferences, the market share is influenced by the carbon emission reduction technology improvement of the supplier and manufacturer and the low carbon advertisement of the retailer. We then construct the differential game model of three-echelon supply chain from the perspectives of decentralized, governmental intervention, supplier cost sharing and governmental intervention, and retailer cost sharing and governmental intervention. Main results are as follows: (1) the manufacturer and supplier's active investment in emission reduction technologies and retailer's advertisement will consistently increase the profits of supply chain members; (2) given that the government subsidizes the manufacturer's carbon emission reduction technology through tax exemption or tax reduction, the manufacturer will always fall into a prisoner's dilemma once the government's carbon emission limit exceeds a certain threshold range; (3) consumer preferences will have a positive effect on manufacturer and supplier's emission reduction strategies, whereas it has nothing to do with retailer's advertisement, and retailer's investment in advertising only depends on manufacturer's subsidy policy; (4) compared with decentralized scenario, Pareto improvement can always be realized whether the manufacturer chooses to cooperate with the supplier or retailer. Our results can provide theoretical basis for the carbon emission strategy of three-echelon supply chain members in low-carbon environment.

Integrating production, replenishment and fulfillment decisions for supply chains: a target-based robust optimisation approach

In this paper, a three-echelon supply chain problem under demand uncertainty is considered. The problem is formulated as a multiperiod two-stage stochastic optimisation model. The first stage, consisting of production and replenishment decisions, is integrated with the second stage, which comprises reactive fulfillment decisions, allowing seamless determination as demands are revealed over time. The demand in each period is characterised by an uncertainty set based on the nominal value and demand bounds. We propose a target-based robust optimisation (TRO) approach to determine the most robust planning with respect to a pre-specified cost target. The proposed TRO approach can trade off the total cost (performance) and model feasibility in the presence of demand perturbation (robustness) by fine-tuning the cost target. The robust counterpart is converted to a quadratically constrained linear programming (QCLP) problem, which can be solved by commercial solvers. Numerical experiments demonstrate that the TRO approach can outperform traditional robust optimisation methods in terms of both cost and feasibility against demand uncertainty by enabling precise adjustment of the cost target. Importantly, the TRO approach provides a flexible means to strike a balance between performance and robustness metrics, making it a valuable tool for supply chain planning under uncertain conditions.

Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains

Supply chain digital twin has emerged as a powerful tool in studying the behavior of an actual supply chain. However, most studies in the field of supply chain digital twin have only focused on what-if analysis that compares several different scenarios. This study proposes a data-driven evolutionary algorithm to efficiently solve the service constrained inventory optimization problem using historical data that generated by supply chain digital twins. The objective is to minimize the total costs while satisfying the required service level for a supply chain. The random forest algorithm is used to build surrogate models which can be used to estimate the total costs and service level in a supply chain. The surrogate models are optimized by an ensemble approach-based differential evolution algorithm which can adaptively use different search strategies to improve the performance during the computation process. A three-echelon supply chain digital twin on the geographic information system (GIS) map in real-time is used to examine the efficiency of the proposed method. The experimental results indicate that the data-driven evolutionary algorithm can reduce the total costs and maintain the required service level. The finding suggests that our proposed method can learn from the historical data and generate better inventory policies for a supply chain digital twin.

Achieving resilient chaos suppression and synchronization of fractional-order supply chains with fault-tolerant control

We present a novel fault-tolerant prescribed performance control approach for fractional-order economic and supply chain systems. While faults and limitations are common in the economic realm, existing control methods for supply chains have not addressed these issues while they lack prescribed performance guarantees. To fill this gap, we propose a fault-tolerant control strategy that can ensure prescribed bounds for fractional three-echelon supply chains, even in the presence of faults and actuator failures. We provide a detailed design procedure and stability analysis, and validate our approach through simulations of supply chain control and synchronization. Our results demonstrate the efficacy of the proposed model.

An inventory analysis in a multi-echelon supply chain system under asymmetry fuzzy demand: a fmincon optimization

In this article, the three-echelon supply chain model is developed with a single supplier, a single vendor and a single buyer in a fuzzy environment. The three-stage inspection technique is considered at the end of the vendor's process to supply high-quality products to the buyer. Moreover, the demand rate of the product in the market is often assumed to be uncertain, and accordingly, in this paper, the uncertainty is handled using fuzzy numbers. The primary goal of this model is to minimize the overall inventory cost relative to the asymmetry hexagonal fuzzy demand. Additionally, a backorder strategy is incorporated to gain customer satisfaction and reduce lost sales. To validate this model, numerical examples are tested and constrained non-linear optimization (i.e. fmincon solver) is performed to derive the optimal solution for the decision variables. Finally, conclusions with potential future directions are provided.

Integrating information, financial, and material flows in a chemical supply chain

We address the flows involved in a real supply chain and discuss how they can be integrated for supply chain management. A scheduling model is proposed based on a State-Task Network representation to schedule order transactions and manufacturing operations in a make-to-order chemical plant. The proposed model is compared to scheduling models that focus on either the order transactions or the manufacturing operations. The advantage of the integrated approach is found in the accuracy of the solutions attained, whereas the siloed models produce suboptimal or infeasible solutions. Each model is compared in a stochastic discrete event simulation environment. An integrated scheduling model is also presented, which includes information, financial, and material flows along a three-echelon supply chain. This integrated model serves as a starting point for developing decision-support systems that take a more comprehensive view of the complex relationships amongst the different flows involved in real supply chains.

A gamified teaching–learning based optimization algorithm for a three-echelon supply chain scheduling problem in a two-stage assembly flow shop environment

The supply chain scheduling problem, which is an integrated production scheduling and distribution through the batch delivery system, has been studied by researchers for more than two decades. So far, all researches in this regard have studied the two-echelon supply chain network. The first novelty of this research is that, for the first time, the three-echelon supply chain scheduling problem, including supplier, manufacturer and customer, is investigated considering two separated batch delivery systems for transportation, simultaneously. To do so, a two-stage assembly flow shop environment is used. In the first stage, there are a number of decentralized suppliers who are responsible for manufacturing components. The components are dispatched to the second stage (assembly stage) with the first batch delivery system and after assembly, the orders are dispatched to the customer through secondary batch delivery system. The purpose of this problem is to minimize the cost of total completion time plus batch delivery costs. First, a mixed integer linear programming model (MILP) is presented that is able to solve small-size instances in a logical time. The second novelty of this research is that, to solve large-size instances, a novel gamified teaching–learning based optimization (GTLBO) algorithm is developed with applying a number of dominance rules and gamification tools. The computational results indicate the superiority of the new TLBO variant over its standard form and two well-known methods, i.e. GA and PSO, especially by applying the dominance rules.

A Game Theory Approach for Supply Chain Coordination Model with Incentive Mechanisms of Discount and Delay in Payments

The uncertain nature of supply chains is one of the key challenges managers, and researchers encounter in decision-making. Accordingly, this paper proposes a three-echelon supply chain in which demands are uncertain. The proposed supply chain has three participants, including supplier, manufacturer, and retailer, while three decentralized, centralized, and coordination models have been formulated to maximize participants’ profits. In the decentralized model, both the manufacturer and retailer independently determine the level of investment and order quantity regarding scenario-based demands. The centralized model determines the optimal order quantity and investment amounts for the whole network. However, these amounts may be different from optimal values for all participants. As such, using game theory, a bilevel adjustable contract based on wholesale price has been proposed as an incentive for players to participate in the coordination plan. Results show that the coordination model outperforms others by reducing the network’s costs and increasing profits simultaneously.

Optimizing supply chain strategies for deteriorating items and imperfect manufacturing under carbon emission regulations

In recent years, the reduction of carbon emissions from supply chains has become a critical concern. This paper presents a three-echelon supply chain inventory model that considers carbon emissions from manufacturing, transportation, storage, and disposal of deteriorating products. The model includes an inspection by the manufacturer to prevent the supply of defective goods and utilizes a third-party logistics company (3PL) that employs a single setup, multiple delivery system. The aim is to apply various regulatory strategies, such as carbon tax, carbon offset, and carbon cap-and-trade, to minimize expected total cost and decrease carbon emissions. The model determines delivery numbers, intervals, and quantities for the 3PL and buyer, as well as the manufacturing quantity for the manufacturer, based on buyer demand. The study concludes that the carbon cap-and-trade strategy is the most effective of the three regulatory strategies analyzed. The proposed model helps supply chain managers reduce their carbon footprint by making informed decisions on inventory, transportation, storage and disposal strategies. This study's findings provide valuable insights to policymakers on effective carbon regulation policies and the development of sustainable and cost-effective supply chain management practices.

Sustainable supply chain design under correlated uncertainty in energy and carbon prices

This paper aims to provide an improvement in the modeling of supply chain designs by incorporating correlated uncertainty among multiple parameters, resulting in a more resilient design. A new methodology to generate forecasts for historically correlated time series, regardless of their underlying probability distributions, is presented and applied to generate scenarios for energy and carbon prices, which historically proved to be correlated. These scenarios are then used in a stochastic computation to obtain a three-echelon supply chain design in Europe maximizing the economic performance. The emissions were monetarized through the incorporation of the European Union cap-and-trade emissions trading system into the model. The social impact of the supply chain network is measured in terms of the direct, indirect and induced jobs it creates, which are proportional to the economic performance. By combining the developed methodology with data mining algorithms, a reduction in the number of required scenarios by more than 90% was achieved. The numerical case study moreover shows that the stochastic design ensures an average reduction of emissions by more than 3 ktons compared to the use of a deterministic approach. In comparison, the computation of a stochastic supply chain design without parameter correlation takes 5 times longer.

An optimal strategy for forecasting demand in a three-echelon supply chain system via metaheuristic optimization

An optimal strategy for forecasting demand in a three-echelon supply chain system via metaheuristic optimization