Supply chain management

Information sharing in supply chains – Interoperability in an era of circular economy

In order to realize the goals of Industry 5.0 (I5.0), which has data interoperability as one of its core principles, the future research in the Supply Chain (SC) visibility has to be aligned with socially, economically and environmentally sustainable objectives. Within the purview of circular economy, this paper indicates various aspects and implications of data sharing in the SCs in light of the published research. Taking into consideration the heterogeneity of data sources and standards, this article also catalogs all the major data-sharing technologies being employed in sharing data digitally across the SCs.

Drawing on the published research from 2015 to 2021, following the PRISMA framework, this paper presents the state of research in the field of data sharing in SCs in terms of their standardization, optimization, simulation, automation, security and more notably sustainability. Using the co-occurrence metric, bibliometric analysis has been conducted such that the collected research is categorized under various keyword clusters and regional themes. This article brings together two major themes in reviewing the research in the field. Firstly, the bibliometric analysis of the published articles demonstrates the contours of the current state of research and the future possibilities in the field. Secondly, in synthesizing the research on the foundations of sustainability within the CRoss Industry Standard Process for Data Mining (CRISP-DM) framework, this article deals with various aspects and implications of information sharing in the SCs. By bringing these two themes together, this paper affords a prospective researcher with the research vis-à-vis the information sharing in SC, starting from the actual data standards in use to the modality and consequence of their application within the perspective of the circular economy. This article, in essence, indicates how all the aspects of data sharing in SCs may be brought together in service of the paradigm of I5.0.

Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy

At the current worrisome rate of global consumption, the linear economy model of producing goods, using them, and then disposing of them with no thought of the environmental, social, or economic consequences, is unsustainable and points to a deeply flawed manufacturing framework. Circular economy (CE) is presented as an alternative framework to address the management of emissions, scarcity of resources, and economic sustainability such that the resources are kept ‘in the loop’. In the context of manufacturing supply chains (SCs), the 6R’s of rethink, refuse, reduce, reuse, repair, and recycle have been proposed in line with the achievement of targeted net-zero emissions. In order to bring that about, the required changes in the framework for assessing the state of manufacturing SCs with regard to sustainability are indispensable. Verifiable and empirical model-based approaches such as modeling and simulation (M&S) techniques find pronounced use in realizing the ideal of CE. The simulation models find extensive use across various aspects of SCs, including analysis of the impacts, and support for optimal re-design and operation. Using the PRISMA framework to sift through published research, as gathered from SCOPUS, this review is based on 202 research papers spanning from 2015 to the present. This review provides an overview of the simulation tools being put to use in the context of sustainability in the manufacturing SCs, such that various aspects and contours of the collected research articles spanning from 2015 to the present, are highlighted. This article focuses on the three major simulation techniques in the literature, namely, Discrete Event Simulation (DES), Agent-Based Simulation (ABS), and System Dynamics (SD). With regards to their application in manufacturing SCs, each modeling technique has its pros and its cons which are evinced in case of data requirement, model magnification, model resolution, and environment interaction, among others. These limitations are remedied through use of hybrids wherein two or more than two modeling techniques are applied for the desired results. The article also indicates various open-source software solutions that are being employed in research and the industry. This article, in essence, has three objectives. First to present to the prospective researchers, the current state of research, the concerns that have been presented in the field of sustainability modeling, and how they have been resolved. Secondly, it serves as a comprehensive bibliography of peer-reviewed research published from 2015–2022 and, finally, indicating the limitations of the techniques with regards to sustainability assessment. The article also indicates the necessity of a new M&S framework and its prerequisites.

Optimization of Multiple Traveling Salesmen Problem by a Novel Representation based Genetic Algorithm

The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that can be described as follows: given a fleet of vehicles with uniform capacity, a common depot, and several requests by the customers, find a route plan for the vehicles with overall minimum route cost (eg. distance traveled by vehicles), which service all the demands. It is well known that multiple Traveling Salesman Problem (mTSP) based algorithms can also be utilized in several VRPs by incorporating some additional constraints, it can be considered as a relaxation of the VRP, with the capacity restrictions removed. The mTSP is a generalization of the well known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. Because of the fact that TSP is already a complex, namely an NP-hard problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to be taken into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding and genetic operators to ensure efficiency. We reviewed how genetic algorithms can be applied to solve these problems, and propose a novel, easily interpretable and problem-oriented representation and operators, that can easily handle constraints on the tour lengths, and the number of salesmen can vary during the evolution. The elaborated heuristic algorithm is demonstrated by a complete realistic example.

Redesign of the Supply of Mobile Mechanics based on a novel Genetic Optimization Algorithm using Google Maps API

If a mobile mechanic has to travel for material, productive time is lost. This paper presents a novel method to reduce activities regarding material handling with extending of serving locations. The design of the supply system can be considered as a complex combinatorial optimization problem, where the goal is to find a route plan with minimal route cost, which services all the demands from the central warehouses while satisfying the capacity and other constraints. We present a multi-chromosome technique for solving the multiple Traveling Salesman Problem (mTSP). The new operators based on a problem-specific representation proved to be more effective in terms of flexibility, complexity and transparency, and also in efficiency than the previous methods. The proposed optimization algorithm was implemented in MATLAB and integrated with Google Maps to provide a complete framework for distance calculation, definition of the initial routes, and visualization. This integrated framework was successfully applied in the solution of a real logistic problem, in the supply of mobile mechanics at one of Hungary's biggest energy providers.

Monte Carlo simulation based performance analysis of supply chains

Since supply chain management is one of the most important management practices that impacts the financial results of services and companies, it is important to optimize and analyze the performance of supply chains. Simulation provides a way to get closer to real life complex situations and uses less simplifications and assumptions than needed with analytical solutions. Monte Carlo simulation based optimization and sensitivity analysis of supply chains can handle uncertainties and stochastic nature of the processes and to extract and visualize relationship among the decision variables and the Key Performance Indicators. We utilized our interactive simulator, SIMWARE, capable to simulate complex multi-echelon supply chains based on simple configurable connection of building blocks. A sensitivity analysis technique has been introduced to extract and visualize the relationships among the decision variables and key performance indicators. The proposed robust sensitivity analysis is based on an improved method used to extract gradients from Monte Carlo simulation. The extracted gradients (sensitivities) are visualized by a technique developed by the authors. The results illustrate that the sensitivity analysis tool is flexible enough to handle complex situations and straightforward and simple enough to be used for decision support.

Determining Optimal Stock Level In Multi-Echelon Supply Chains

Inventory control of Multi-echelon supply chains is a widely researched area. In most cases researchers choose analytic methods to analyze such logistics systems. Simulation is a very useful alternative for analyzing supply chain systems and a well-constructed model can provide a better approach and can give more realistic picture of the complex situation.

We introduced an interactive, configurable simulator to analyze stock levels in a complex supply chain. This new modeling approach (SIMWARE) is capable to simulate complex multi-echelon supply chains where the frequency of stock transfer between the individual levels of the supply chain can be optimized. The authors evaluate different optimization strategies and methods. The introduced SIMWARE method can be used to minimize the environmental impact of the supply chain by minimizing the transportation between the nodes of the supply chain hierarchy. The model provides an optimization methodology where the objective function is the total cost of the supply chain.

Minimization of Off-Grade Production in Multisite Multiproduct Plants by Solving Multiple Traveling Salesman Problem

Continuous multiproduct plants allow the production of several products (product grades). During grade transitions off-spec products are produced. The economic losses and the environmental impact of these transitions are sequence dependent, so the amount of off-grade products can be minimized by scheduling the sequence of the production of different products. Applying parallel production sites (m) increases the flexibility of multiproduct plants. Since market demands are changing, the production cycles of these sites should be re-scheduled in certain intervals. Therefore, our task is to design m production cycles that contains all required products by minimizing the total length of grade transitions. Most production scheduling problems such as the one considered in this paper are NP-hard. Our goal is to solve realistic problem instances in no more than a couple of minutes. We show that this problem can be considered as a multiple traveling salesmen problem (mTSP), where the distances between the products are based on the time or costs of the grade transitions. The resulted mTSP has been solved by multi-chromosome based genetic algorithm. For demonstration purposes, we present an illustrative example. The results show that multiproduct multisite scheduling problems can be effectively handled as mTSPs, and the proposed problem-specific representation based genetic algorithm can be used in wide range of optimization problems.