Three-stage Supply Chain Research Articles

A quadratic-linear bilevel programming approach to green supply chain management

Green Supply Chain Management requires coordinated decisions between the strategic and operational organization layers to address strict green goals. Furthermore, linking CO2 emissions to supply chain operations is not always easy. This study proposes a new mathematical model to minimize CO2 emissions in a three-layered supply chain. The model foresees using a financial budget to mitigate emissions contributions and optimize supply chain operations planning. The three-stage supply chain analyzed has inbound logistics and handling operations at the intermediate level. We assume that these operations contribute to emissions quadratically. The resulting bilevel programming problem is solved by transforming it into a nonlinear mixed-integer program by applying the Karush-Kuhn-Tucker conditions. We show, on different sets of synthetic data and on a case study, how our proposal produces solutions with a different flow of goods than a modified linear model version. This results in lower CO2 emissions and more efficient budget expenditure.

Sustainable-resilient-responsive supply chain with demand prediction: An interval type-2 robust programming approach

This study configures a novus multi-objective three-stage supply chain network that aims at optimizing sustainability, resilient and responsive measures, simultaneously. At first, a novel prediction model based on Markov chain, Dempster-Safer evidence theory and Shapely value is developed under trapezoidal interval type-2 fuzzy ambience to predict the demand of future scenarios. The prediction model prevents undesired loss occurred due to illogical demand consideration. To encounter interval type-2 fuzziness as well as risk in decision making, an unprecedented trapezoidal interval type-2 fuzzy robust possibilistic programming approach is introduced. The DEcision MAking Trial and Evaluation Laboratory method is suggested in the framework of trapezoidal interval type-2 fuzzy to prioritize the objective function by scrutinizing the interrelationship among different objective function components. As a result, the subjectiveness in the decision making framework is reduced. The generated configuration is then analysed using a blended approach that merges multi-choice reference goal programming with utility functions and particle swarm optimization. The integrated approach produces high-quality solution in lesser computational time. The insightful explorations are delivered based on a case study of hydrogen fuel supply chain in India. The outcomes show how resilient tactics are quite effective and can significantly enhance social, environmental, and economic aspects. It is determined that the best results for supply chain are obtained when the suggested sustainable, resilience and responsiveness tactics are used jointly. Moreover, the sensitivity analysis results suggest that the objective functions are substantially affected by the responsive level; therefore, managers ought to envisage the trade-off between responsiveness and other objective functions.

Optimal shipping, collaboration, and outsourcing decisions in a hybrid cross-docking supply chain

Motivated by the supply chain of our oil-field service industry partner, we study shipping, collaboration, and outsourcing decisions in a decentralized, three-stage supply chain consisting of suppliers, a hybrid cross-dock facility, and oil well facilities. Unlike pure cross-docking, which transships arriving products quickly downstream, hybrid cross-docking allows for inventory to remain at the cross-dock for multiple periods. We formulate multi-period, optimization models to minimize costs of different members in a hybrid cross-docking supply chain and establish structural properties of optimal solutions. We make use of those results to identify conditions under which hybrid cross-docking is more cost efficient than pure cross-docking. Our results provide managerial insights regarding when a hybrid cross-dock should be enabled, and the value of the resulting cost savings. We also quantify the value of collaboration among different stages in the supply chain. Upstream collaboration results in 1% to 9% average cost savings for the cross-dock, while downstream collaboration generates 4% to 13% in average cost savings for oil well facilities, depending on the number of products and their holding cost. We also develop a Stackelberg pricing game between a logistics company and oil well facilities seeking to lower their costs by outsourcing their transportation and inventory operations. We identify the structure of oil well facilities’ best response to the price of outsourcing services, as well as the structure of the logistics provider’s optimal pricing policy. Our findings and models, based on current literature, provide application focused tools that allow managers to improve cross-docking operations in their supply chains, realize the benefits of collaborations, and make better outsourcing decisions.

Strategic insights into recovery from supply chain disruption: A multi-period production planning model

Supply chain disruption has become an interesting topic for many researchers recently. Although disruption has been previously modelled, unexpected disruptions are unavoidable. Thus, a reactive strategy is still needed. This article proposes an efficient production recovery strategy for the three-stage supply chain network consisting of manufacturers, distribution centres, and retailers when facing production disruptions. The manufacturers produce multiple products with different production priorities. All manufacturing plants are fully coordinated and able to produce all types of products. Multi-production periods are considered. The model describes the optimal production and distribution allocation before and after disruption (recovery period). The recovery model revises the production and distribution allocation based on the remaining available capacity in the respective production period. The recovery model determines what unfulfilled demand will be assigned either as backorders in the next production period or considered as lost sales. Our results show that the proposed recovery model produces a lower total cost than the non-recovery strategy. Further, the simulation confirms that the longer the disruption duration is, the higher the cost-saving efficiency is. In addition, the demand to capacity tightness ratio is the most impactful parameter to decide on backorder or lost sales strategy.

Role of Distribution Centers Disruptions in New Retail Supply Chain: An Analysis Experiment

The convergence of physical stores and e-commerce has led to the emergence of a new retail business mode in the retail industry. In today’s world, new retail supply chains face the potential risks of disruption caused by natural and man-made disasters, and epidemics. In this paper, we simulate a three-stage new retail supply chain consisting of suppliers, manufacturers, and a retailer with online and offline channels in the AnyLogistix simulation and optimization software. We develop a simulation model to analyze the effects of various supply chain node disruptions on new retail supply chain performance and service level with consideration of four scenarios: disruption-free; manufacturer disruption; warehouse center disruption; offline store disruption. The main results show that supply chain node disruptions have negative impacts on the performance and service level. Besides, the warehouse center disruption has the most devastating effect on this new retail supply chain. Overall, this paper provides insights for decision-makers to consider disruption issues when designing resilient new retail supply chains.

Impact of carbon emission policy combinations on the optimal production-inventory decisions for deteriorating items

This study is the first to propose a cross-border production-inventory model for deteriorating items under different carbon emission policy combinations by considering a global supply chain system with a single manufacturer from abroad and a single domestic retailer. An integrated three-stage supply chain that includes material supply, manufacturer’s production and delivery, and retailer’s ordering and sales is designed. Two carbon policy combinations are considered: (1) the manufacturer faces a carbon cap-and-trade policy, and the retailer faces a carbon tax policy; (2) the manufacturer faces a carbon tax policy and the retailer faces a carbon cap-and-trade policy. The main purpose is to determine the optimal material supply, production, and delivery strategies for the manufacturer and the optimal replenishment strategies for the retailer to maximize the joint total profit of the supply chain in each situation, while observing the solutions’ carbon reduction benefits. This study uses mathematical programming to find optimal solutions for the manufacturer and the retailer, with numerical examples presented to demonstrate the solution procedures for comparing different carbon policy combinations. The results show that under different policy combinations, fluctuations in carbon prices, carbon taxes, and exchange rates have different effects on the profits of the entire supply chain and each member, as well as the carbon reduction’s social benefits. This is expected to provide enterprise or supply chain decision makers, especially in multinational enterprises, with valuable references for opting for a green supply chain.

Adapting supply chain operations in anticipation of and during the COVID-19 pandemic

This article investigates the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic. In generalized terms, we contribute to the understanding of the effect of preparedness and recovery decisions in a pandemic setting on supply chain operations and performance. In particular, we examine the impact of inventory pre-positioning in anticipation of a pandemic and the adaptation of production-ordering policy during the pandemic. Our model combines three levels, which is not often seen jointly in operations management literature, i.e., pandemic dynamics, supply chain design, and operational production-inventory control policies. The analysis is performed for both two- and three-stage supply chains and different scenarios for pandemic dynamics (i.e., uncontrolled propagation or controlled dispersal with lockdowns). Our findings suggest that two-stage supply chains exhibit a higher vulnerability in disruption cases. However, they are exposed to a lower system inertia and show positive effects at the recovery stage. Supply chain adaptation ahead of a pandemic is more advantageous than during the pandemic when specific operational recovery policies are deployed. We show that it is instructive to avoid simultaneous changes in structural network design and operational policies since that can destabilize the production-inventory system and result in higher product shortages.

Synchronization of a Supply Chain Model with Four Chaotic Attractors

In this article, we construct a three‐stage supply chain model using a system of differential equations to reveal the interplay among producers, distributors, and end customers. On the one hand, information about the products and production is exchanged between each stage in the supply chain system. Such information affects the behaviors of each stage. On the other hand, the transport of products between the stages of the supply chain affects the behavior of the system. Our findings are summarized as follows: First, we find that the supply chain model is a system with four chaotic attractors. Second, we explore the synchronization of such a chaotic system. Third, according to the characteristics of the chaotic system, we design a variety of simple control laws to realize the synchronization of two chaotic systems with the same structure. These control laws for the chaotic system are proved to realize local asymptotic synchronization or global exponential synchronization. Numerical simulations are conducted to confirm that the designed controls work well.

Coordinating a three-stage supply chain using buy-back and revenue sharing contracts

In this research, a composite contract model is developed for coordinating a three-stage supply chain which consists of a manufacturer, a distributor and a retailer. The relationship between the distributor and the retailer is coordinated using a revenue sharing contract, while the relationship between the manufacturer and the distributor is coordinated using a buy-back contract so as to encourage the distributor to order more. Mathematical models for these contracts are derived and analysed through numerical experiments to identify the optimal order quantities, the coordination conditions, the effects of the contract parameters and to identify the best possible contract parameter values which can lead to a win-win situation.

Ecodesign Strategy for Demand-Oriented Electrical and Electronic Products

Governments of various countries have formulated relevant EPR environmental regulations for environmental pollution caused by electrical and electronic products, and enterprises mainly respond to this regulation through product ecodesign strategies. In view of this, this paper takes a three-stage supply chain system composed of a manufacturer, a retailer and a third-party recycler as the research object, and develops a demand-oriented product ecodesign strategy for five scenarios under different environmental regulations, including eco-input subsidy, sales subsidy, consumption subsidy and recycling subsidy. This study finds that the manufacturer does not actively engage in product ecodesign if the government does not implement subsidy policies; when the government implements subsidy policies such as eco-input subsidy, sales subsidy, or consumption subsidy, the manufacturer will design a high-level ecological product. However, under the recycling subsidy policy, the manufacturer will design a low-level ecological product. These results suggest that different subsidy policies may lead to different eco-product strategies of the manufacturer. In particular, the recycling subsidy policy can encourage a recycler to recycle actively, thus reducing the environmental pollution cost of a manufacturer, but the manufacturer is reluctant to improve the ecological level of the finished product.

Making an integrated decision in a three-stage supply chain along with cellular manufacturing under uncertain environments: A queueing-based analysis

Today’s complicated business environment has underscored the importance of integrated decision-making in supply chains. In this paper, a novel mixed-integer nonlinear mathematical model is proposed to integrate cellular manufacturing systems into a three-stage supply chain to deal with customers’ changing demands, which has been little explored in the literature. This model determines the types of vehicles to transport raw materials and final parts, the suppliers to procure, the priorities of parts to be processed, and the cell formation to configure work centers. In addition, queueing theory is used to formulate the uncertainties in demands, processing times, and transportation times in the model more realistically. A linearization method is employed to facilitate the tractability of the model. A genetic algorithm is also developed to deal with the NP-hardness of the problem. Numerous instances are used to validate the effectiveness of the modeling and the efficiency of solution procedures. Finally, a sensitivity analysis and a real case study are discussed to provide important management insights and evaluate the applicability of the proposed model.

Pricing of a Three-Stage Supply Chain with a Big Data Company

Pricing of a Three-Stage Supply Chain with a Big Data Company

انتخاب هم‌زمان تأمین‌کنندگان و تعیین سیاست موجودی زنجیره‌های تأمین غیرقطعی چندمحصولی با رویکرد بهینه‌سازی مبتنی بر شبیه‌سازی

I‌n‌c‌r‌e‌a‌s‌i‌n‌g b‌u‌s‌i‌n‌e‌s‌s c‌o‌m‌p‌e‌t‌i‌t‌i‌v‌e‌n‌e‌s‌s f‌o‌r‌c‌e‌d c‌o‌m‌p‌a‌n‌i‌e‌s t‌o i‌m‌p‌r‌o‌v‌e t‌h‌e‌i‌r b‌u‌s‌i‌n‌e‌s‌s. T‌h‌e‌r‌e‌f‌o‌r‌e, i‌n‌d‌u‌s‌t‌r‌y m‌a‌n‌a‌g‌e‌r‌s h‌a‌v‌e f‌o‌u‌n‌d t‌h‌a‌t i‌t i‌s n‌e‌c‌e‌s‌s‌a‌r‌y t‌o f‌o‌c‌u‌s o‌n t‌h‌e s‌u‌p‌p‌l‌y o‌f p‌r‌o‌d‌u‌c‌t‌s i‌n a‌c‌c‌o‌r‌d‌i‌n‌g t‌o t‌h‌e w‌i‌s‌h‌e‌s o‌f t‌h‌e c‌u‌s‌t‌o‌m‌e‌r t‌h‌e q‌u‌a‌l‌i‌t‌y a‌n‌d t‌h‌e c‌o‌s‌t o‌f t‌h‌e‌i‌r i‌n‌t‌e‌n‌d‌e‌d u‌s‌e. W‌e c‌o‌n‌s‌i‌d‌e‌r a t‌h‌r‌e‌e-s‌t‌a‌g‌e s‌u‌p‌p‌l‌y c‌h‌a‌i‌n i‌n‌v‌o‌l‌v‌i‌n‌g m‌u‌l‌t‌i‌p‌l‌e s‌u‌p‌p‌l‌i‌e‌r‌s, m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e‌r p‌r‌o‌d‌u‌c‌i‌n‌g m‌u‌l‌t‌i‌p‌l‌e t‌y‌p‌e o‌f p‌r‌o‌d‌u‌c‌t a‌n‌d a f‌i‌n‌a‌l c‌u‌s‌t‌o‌m‌e‌r. A‌t e‌a‌c‌h o‌r‌d‌e‌r, t‌h‌e m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e‌r r‌e‌c‌e‌i‌v‌e‌s t‌h‌e r‌a‌w m‌a‌t‌e‌r‌i‌a‌l f‌r‌o‌m a s‌u‌p‌p‌l‌i‌e‌r b‌a‌s‌e‌d o‌n o‌p‌t‌i‌m‌a‌l p‌o‌l‌i‌c‌y i‌n a l‌o‌t o‌f s‌i‌z‌e Q. T‌h‌e p‌r‌o‌d‌u‌c‌t‌i‌o‌n p‌r‌o‌c‌e‌s‌s c‌o‌u‌l‌d p‌r‌o‌d‌u‌c‌e a‌d‌d‌i‌t‌i‌o‌n‌a‌l n‌o‌n‌c‌o‌n‌f‌o‌r‌m‌i‌n‌g p‌r‌o‌d‌u‌c‌t‌s, t‌h‌e m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e‌r c‌o‌u‌l‌d b‌e u‌n‌a‌v‌a‌i‌l‌a‌b‌l‌e d‌u‌e t‌o f‌a‌i‌l‌u‌r‌e‌s (f‌o‌l‌l‌o‌w‌i‌n‌g a g‌e‌n‌e‌r‌a‌l t‌i‌m‌e-t‌o f‌a‌i‌l‌u‌r‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n), a‌n‌d r‌e‌p‌a‌i‌r o‌p‌e‌r‌a‌t‌i‌o‌n‌s (f‌o‌l‌l‌o‌w‌i‌n‌g a g‌e‌n‌e‌r‌a‌l t‌i‌m‌e-t‌o-r‌e‌p‌a‌i‌r d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n) p‌r‌o‌p‌o‌s‌e a c‌o‌n‌t‌r‌o‌l p‌o‌l‌i‌c‌y, w‌h‌i‌c‌h c‌o‌o‌r‌d‌i‌n‌a‌t‌e‌s s‌u‌p‌p‌l‌i‌e‌r s‌e‌l‌e‌c‌t‌i‌o‌n, r‌e‌p‌l‌e‌n‌i‌s‌h‌m‌e‌n‌t, p‌r‌o‌d‌u‌c‌t‌i‌o‌n a‌n‌d q‌u‌a‌l‌i‌t‌y i‌n‌s‌p‌e‌c‌t‌i‌o‌n d‌e‌c‌i‌s‌i‌o‌n‌s. U‌p‌o‌n r‌e‌c‌e‌p‌t‌i‌o‌n, t‌h‌e m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e‌r a‌p‌p‌l‌i‌e‌s a s‌i‌m‌p‌l‌e l‌o‌t-b‌y-l‌o‌t a‌c‌c‌e‌p‌t‌a‌n‌c‌e-s‌a‌m‌p‌l‌i‌n‌g p‌l‌a‌n w‌i‌t‌h a‌t‌t‌r‌i‌b‌u‌t‌e‌s. T‌h‌i‌s p‌l‌a‌n i‌s c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌z‌e‌d b‌y a r‌a‌n‌d‌o‌m s‌a‌m‌p‌l‌e o‌f s‌i‌z‌e n a‌n‌d a‌n a‌c‌c‌e‌p‌t‌a‌n‌c‌e c‌r‌i‌t‌e‌r‌i‌o‌n c. B‌a‌s‌e‌d o‌n t‌h‌i‌s i‌n‌s‌p‌e‌c‌t‌i‌o‌n p‌l‌a‌n i‌f t‌h‌e n‌u‌m‌b‌e‌r o‌f n‌o‌n-c‌o‌n‌f‌o‌r‌m‌i‌n‌g i‌t‌e‌m‌s d f‌o‌u‌n‌d i‌n t‌h‌i‌s s‌a‌m‌p‌l‌e i‌s e‌q‌u‌a‌l t‌o o‌r l‌e‌s‌s t‌h‌a‌n c t‌h‌e l‌o‌t w‌i‌l‌l b‌e a‌c‌c‌e‌p‌t‌e‌d. O‌t‌h‌e‌r‌w‌i‌s‌e, t‌h‌e l‌o‌t w‌i‌l‌l b‌e r‌e‌f‌u‌s‌e‌d a‌n‌d r‌e‌t‌u‌r‌n‌e‌d t‌o i‌t‌s o‌r‌i‌g‌i‌n‌a‌l s‌u‌p‌p‌l‌i‌e‌r a‌n‌d t‌h‌e‌n a n‌e‌w o‌r‌d‌e‌r i‌s p‌l‌a‌c‌e‌d. A‌t t‌h‌i‌s i‌n‌s‌t‌a‌n‌t t‌h‌e m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e‌r i‌s n‌o‌t o‌b‌l‌i‌g‌e‌d t‌o k‌e‌e‌p t‌h‌e s‌a‌m‌e s‌u‌p‌p‌l‌i‌e‌r. H‌e c‌a‌n c‌h‌o‌o‌s‌e a‌n‌y o‌n‌e t‌h‌a‌t o‌f‌f‌e‌r‌s b‌e‌t‌t‌e‌r r‌e‌p‌l‌e‌n‌i‌s‌h‌m‌e‌n‌t c‌o‌n‌d‌i‌t‌i‌o‌n‌s. I‌n t‌h‌i‌s w‌o‌r‌k. w‌e a‌i‌m t‌o d‌e‌t‌e‌r‌m‌i‌n‌e t‌h‌e o‌p‌t‌i‌m‌a‌l c‌o‌n‌t‌r‌o‌l p‌o‌l‌i‌c‌y f‌o‌r s‌u‌p‌p‌l‌i‌e‌r s‌e‌l‌e‌c‌t‌i‌o‌n r‌e‌p‌l‌e‌n‌i‌s‌h‌m‌e‌n‌t a‌n‌d o‌p‌t‌i‌m‌a‌l q‌u‌a‌l‌i‌t‌y c‌o‌n‌t‌r‌o‌l d‌e‌c‌i‌s‌i‌o‌n‌s t‌h‌a‌t m‌i‌n‌i‌m‌i‌z‌e‌s t‌h‌e t‌o‌t‌a‌l c‌o‌s‌t w‌h‌i‌c‌h i‌n‌c‌l‌u‌d‌e‌s i‌n‌v‌e‌n‌t‌o‌r‌y, b‌a‌c‌k‌l‌o‌g, i‌n‌s‌p‌e‌c‌t‌i‌o‌n, r‌e‌p‌l‌e‌n‌i‌s‌h‌m‌e‌n‌t a‌n‌d p‌r‌o‌d‌u‌c‌t‌i‌o‌n c‌o‌s‌t‌s. C‌o‌o‌r‌d‌i‌n‌a‌t‌i‌n‌g d‌e‌c‌i‌s‌i‌o‌n‌s a‌l‌o‌n‌g t‌h‌e e‌n‌t‌i‌r‌e c‌h‌a‌i‌n i‌s c‌r‌i‌t‌i‌c‌a‌l s‌i‌n‌c‌e i‌t r‌e‌q‌u‌i‌r‌e‌s d‌e‌t‌e‌r‌m‌i‌n‌i‌n‌g c‌a‌r‌e‌f‌u‌l‌l‌y w‌h‌a‌t t‌o m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e a‌s w‌e‌l‌l a‌s w‌h‌a‌t w‌h‌e‌n a‌n‌d f‌r‌o‌m w‌h‌o‌m t‌o o‌r‌d‌e‌r. S‌o s‌i‌m‌u‌l‌a‌t‌i‌o‌n-b‌a‌s‌e‌d o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n i‌s o‌n‌e o‌f t‌h‌e m‌e‌t‌h‌o‌d‌s f‌o‌r s‌o‌l‌v‌i‌n‌g t‌h‌i‌s p‌r‌o‌b‌l‌e‌m. N‌u‌m‌e‌r‌i‌c‌a‌l e‌x‌a‌m‌p‌l‌e‌s s‌h‌o‌w t‌h‌e e‌f‌f‌i‌c‌i‌e‌n‌c‌y o‌f t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌e‌t‌h‌o‌d.

A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk.

This study develops a mathematical model to mitigate disruptions in a three-stage (i.e., supplier, manufacturer, retailer) supply chain network subject to a natural disaster like COVID-19 pandemic. This optimization model aims to manage supply chain disruptions for a pandemic situation where disruptions can occur to both the supplier and the retailer. This study proposes an inventory policy using the renewal reward theory for maximizing profit for the manufacturer under study. Tested using two heuristics algorithms, namely the genetic algorithm (GA) and pattern search (PS), the proposed inventory-based disruption risk mitigation model provides the manufacturer with an optimum decision to maximize profits in a production cycle. A sensitivity analysis was offered to ensure the applicability of the model in practical settings. Results reveal that the PS algorithm performed better for such model than a heuristic method like GA. The ordering quantity and reordering point were also lower in PS than GA. Overall, it was evident that PS is more suited for this problem. Supply chain managers need to employ appropriate inventory policies to deal with several uncertain conditions, for example, uncertainties arising due to the COVID-19 pandemic. This model can help managers establish and redesign an inventory policy to maximize the profit by considering probable disruptions in the supply chain network.

Implementation of a Multi-Objective Working Capital Management Strategy for Financial Supply Chains

The issue of adequate application of quantitative supply chain (SCF) solutions for cooperative working capital management becomes more and more important in terms of globalization and growing competition between supply chains (SCs). Authors address the problem by developing models for cooperative working capital management (WCM) through SCF adoption for the case of the three-stage supply chain. The grounding for the optimization is multicriteria approach. The multi-objective working capital optimization model (Ivakina et al., 2021) allows to nd optimal solution regardless initial financial and liquidity position of SC. In the article, we use this model to quantitatively implement a multi-objective working capital management strategy on the cases of real supply chains. The results obtained in the paper indicate that the model of working capital optimization with concurrent use of multiple supply chain finance solutions can provide an optimal solution for all the cases considered in the research. It allows to decrease the total financial costs on working capital and supply chain finance solutions making individual ones not worse and at the same time achieve greater liquidity.

The Multi-Objective Model of Working Capital Optimization

In recent years, the topic of supply chain finance has gained a lot of attention from academia, but still, there are a lot of unexplored areas. For example, the literature demonstrates a clear gap of adequate application of numerous supply chain finance solutions for collaborative working capital management (Gelsomino et al., 2019). This issue becomes more and more important, specifically in terms of globalization and growing competition between supply chains when liquidity and profitability improvement is of paramount relevance. Companies focusing on their individual supply chain issues and taking their own interests into account rather than understanding the whole supply chain and collaborating with their partners are missing fruitful areas for improvements (Wuttke et al., 2016). The authors address this gap by developing a model for collaborative working capital management through supply chain finance adoption for the case of the three-stage supply chain. At the same time, the process of working capital optimization is received as a multi-objective problem. The results obtained indicate that the model of working capital optimization with concurrent use of multiple supply chain finance solutions is able to provide an optimal solution for all the cases considered in the research. It allows to decrease the total financial costs on working capital and supply chain finance solutions making individual ones not worse and at the same time achieve greater liquidity.

Considering the traceability awareness of consumers: should the supply chain adopt the blockchain technology?

Blockchain technology is an emerging technology developed in recent years. It has powerful information traceability function. The blockchain technology plays an important role in monitoring product quality and responding to product safety problems. Under considering the traceability awareness of consumers and the cost of using the blockchain technology, should the supply chain adopt the blockchain technology? The research on this issue deserves great attentions. In this paper, for a three-stage supply chain consisting of a supplier, a manufacturer and a retailer, we study the optimal pricing strategies of the supply chain considering the traceability awareness of consumers in two scenarios. These two scenarios are: scenario N (i.e., the supply chain does not adopt the blockchain technology) and scenario B (i.e., the supply chain adopts the blockchain technology). On this basis, we discuss the conditions that the supply chain adopts the blockchain technology by comparing the optimal profits of the supply chain and its members in two scenarios. Further, we discuss the problem of supply chain coordination when adopting the blockchain technology. The results show that it is conditional for the supply chain to adopt the blockchain technology, and the condition is related to the traceability awareness of consumers, the production costs of the supplier and manufacturer, and the cost of using the blockchain technology. We also find that under a certain condition, the revenue sharing contract can realize a Pareto improvement for the supply chain that adopts the blockchain technology.

An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier’s reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

Assessing site integration into semi-continuous production, distribution and liquidity planning of supply chain networks

Abstract In order to strengthen competitiveness, the pool of available network sites in supply chains of processing industries with 24/7 operations should be adjusted permanently. However, an integration of additional sites is to be assessed thoroughly by the network managers as an exact coordination with existing partners must be ensured within multi-day planning horizons. On the one hand, continuous-time scheduling of network-wide operations exclusively allows for using time-based assessment criteria such as the minimum production time to be provided by the integrated sites. On the other hand, the assessment needs to be based on the calculation of network-wide liquidity, which must not fall below its previous value after the realization of an integration. A realistic consideration of financial planning needs to include both credits and financial investments not being based on given interest rates, but on cash flow series characterized by arbitrary compositions of payments and repayments occurring after any intervals of time periods. Thus, it is to be implemented by discrete-time modeling. As a result of merging both methods of assigning decisions to appropriate points on the time axis, a semi-continuous mixed-integer linear model is developed in the paper on hand. By interrelating three different shapes of this model, the minimum and maximum scope of an integration can be determined. The approach is validated for an illustrative example of a three-stage supply chain network, which is extended by two or three additional production sites. The computability of the problem on a high-performance cluster is evaluated in a scenario analysis including 50 test instances with randomly generated data.

Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems

In this work a multi-objective optimisation framework of genetic programming (GP) in the modelling of bullwhip effect and NSA for centralised and decentralised supply chain systems has been proposed. The individual and interactive effect of these four input factors has been investigated on bullwhip effect and NSA by adapting the parametric and sensitivity approach on the formulated models. The appropriate settings of dominant input factors (batch ordering and demand signal processing for a decentralised chain, demand signal processing and rationing shortage gaming for a centralised chain) are suggested to optimise the bullwhip effect and NSA of three-stage supply chain simultaneously. The implications and advantages of proposed optimisation framework will be useful for business practitioners to monitor and supervise the sudden demand amplification that generally faced by them in the supply chains.