International Journal of Supply and Operations Management
Volume:11 Issue: 2, Spring 2024

The purpose of this research is to determine the correlation between supply chain visibility, supply chain sustainability and the implementation of new technologies in the supply chain domain. Its significance is huge as it opens new roads of understanding the above correlation in order to assist supply chain managers to adapt to more sustainable supply chain operations This research is following the systematic literature review approach. Therefore, is following specific methods/frameworks that include a systematic screening process with inclusive/ exclusive criteria, the implementation of the SALSA method, the CASP checklist, the PRISMA 2009 statement checklist with 3 items, while the findings were divided into 4 major thematic categories. The findings highlighted a strong indirect correlation between supply chain visibility and supply chain sustainability. This correlation is facilitated via the implementation of new technologies, like RFIDs, blockchain, A.I. etc. Although a lot have been written in terms of supply chain visibility and sustainability and of course, about the new technologies, their combination in the light of the strong correlation between visibility and sustainability in a supply chain have not been discussed a lot till now. The implications of the research, from a managerial point of view, are huge as they can be the springboard for the development of new approaches/ operational frameworks towards more sustainable supply chains. Until now, supply chain visibility, although was a priority for supply chain professionals, was not implemented with an eye in supply chain sustainability, but instead was focused more on operational aspects. Now, with the use of new technologies, both are possible.

Multiple Criteria Decision Analysis (MCDA) sorting models are highly relevant for solving real-world problems. Thus, in the literature, the great majority of MCDM methods tackled the choice or ranking problems unlike the sorting approaches although assigning alternatives to predefined homogeneous categories (classes) presents a complex problem. Thus, in this paper, we tackled the sorting problematic using the EDAS “Evaluation based on Distance from Average Solution” method. It is used for ranking alternatives, in a decreasing order, according to their Appraisal Scores (AS). Nevertheless, the current version of EDAS method cannot deal with sorting problems. Since a great majority of real-world decision-making problems are modeled as sorting ones, we proposed a new sorting MCDM method called EDAS-Sort to cope with decision problems requiring assigning alternatives to predefined and ordered classes. Given that we dealt with classes defined by their boundary profiles, the proposed method is called EDAS-Sort-B. To demonstrate and underline it, we presented a case study on a bank agency located in Sfax, Tunisia which aims to assign clients requesting loans to three predefined and ordered categories:  very solvent, solvent, and doubtful according to various criteria. Therefore, the head of the bank agency (decision maker) will gain insight on the client's profile and whether he is trustful or not to repay the loan. Thus, the EDAS-Sort-B is effective for solving problems requiring assigning alternatives to predefined and ordered categories. Thereupon, the main advantage of EDAS-Sort-B is to help the DM “Decision Maker” to take a real-time decision related to alternatives’ assignment.

Credit risk consists is the expectation of losses stemming from the inability of a borrower to repay a loan. For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower’s characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVM-LSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management.

Digital marketing has become vital to businesses' marketing strategies in today's technology and social media era. However, the effectiveness of digital marketing campaigns largely depends on accurately identifying the target audience. This study aims to implement the simulated annealing initiative algorithm for digital marketing, as well as audience classification and optimum target audience selection. Traditional methods of target audience identification, such as demographic, geographic, and psychographic segmentation, are only sometimes effective in identifying the most responsive audience. Therefore, advanced techniques such as clustering, genetic, and simulated annealing algorithms have been proposed to identify the optimum target audience. The heuristic simulated annealing algorithm is one of the most promising techniques for optimum target audience identification. It is widely used in combinatorial optimization problems and applied in various fields such as engineering, economics, management, and computer science. In this research, a digital marketing campaign is implemented for a new line to sell training courses in empowerment and competency in human resource management within the mining industry. After conducting market research, we have identified five critical segments: age, gender, income group, place of residence, and level of university education. The number of customers at each customer journey stage was 740 people in brand development, email, and advertising campaigns, of which 620 people are in the "Awareness" stage, 431 people in the "Interest" stage, 261 people in the "Consideration" stage, 203 people in the "Intend" stage, 179 people in the "Purchase" and finally, 179 People were evaluated in the "loyalty" stage for the case of educational service company. The results show we should target 20% of our marketing efforts towards the 18-24 age group, 30% towards females, 20% towards high-income individuals, 10% towards rural areas, and 20% towards University education level in BSc. The best cost per conversion we obtain is 78.105×106 Rials. The results show that the simulated annealing algorithm can be valuable for identifying the optimum target audience in digital marketing campaigns. By considering the entire customer journey and allowing for more complex audience targeting, the algorithm can help companies optimize their marketing strategies and maximize their profits.

The changing factors of supply chain management include the evolution of technology in market conditions, the transformation of business practices, new expectations of partners in the supply chain, and demand for more value-added from the end-user consumer. Manufacturing organizations require more flexibility to maintain a competitive advantage as well as to operate in a dynamic environment. As the complexity increases, uncertainty and levels also increase in the supply chain. Hence, risk management has become a major issue in the supply chain and plays a significant role in the supply chain performance and the continuity of the organization's dynamics. In the paper, the process of supply chain risk management has been gone through and a risk mitigation model has been presented. The goal of the paper is to expand the proposed model of Kirilmaz & Erol in which the number of commodities increases. In the first step of the suggested method, a procurement plan is provided by a linear planning model, taking into account cost constraints. In the second step, the plan is revised by considering risk criteria for planning. The transition of orders is made to reduce the risk from high risky suppliers to less risky suppliers. The process of supply chain risk management has been performed by an electromotor company in the Middle East. We use the Kirilmaz & Erol model to validate the proposed model.

In the era of Industry 4.0, choosing suppliers for online commerce is of utmost importance and calls for the application of efficient, data-centric techniques. Businesses are under increasing pressure to improve their supply chain management strategies and select the best suppliers in the online commerce environment of Industry 4.0. Traditional approaches, however, sometimes don't include a thorough assessment of suppliers across several dimensions. In order to close this gap, this paper proposes a new approach that combines Data Envelopment Analysis (DEA) with the Simple Multi-Attribute Rating Technique (SMART). The first phase is applying DEA to determine how effective suppliers are using the data that has been gathered. DEA offers a quantitative indicator of how efficiently providers convert their inputs into outputs. This combination score enables rating suppliers while simultaneously considering multi-attribute evaluation and quantifying efficiency assessment, then using a number of different criteria, providers are evaluated using the SMART approach. The findings of this analysis help to improve supplier selection procedures in the context of online commerce, which falls under the purview of Industry 4.0.

The study aims to examine the adoption of Industry 4.0 technologies in the transportation industry and their impact on enhancing sustainability. A systematic literature review was conducted to identify the application and impact of Industry 4.0 in the transportation industry within the context of sustainability. The results showed that Industry 4.0 technologies have significantly contributed to sustainability in the transportation sector. Blockchain technology. Internet of Things (IoT). Artificial intelligence (AI), big data analytics (BDA), Radio Frequency Identification (RFID), Global Positioning System (GPS), and the use of robotics and sensors are found to be the most prominent technologies used in the transportation industry.  The results also indicated some key challenges which include higher investment costs, cyber security threats, technological integration, and insufficient qualified and skilled human resources. The study provides valuable insight to businesses and policymakers. Industry 4.0 technologies have the crucial potential to resolve transportation and logistics disruptions and improve sustainable performance. Future studies could investigate to fully understand the long-term effects of Industry 4.0 on sustainability in the transportation sector and the potential for new technologies to further improve transportation systems and explore the barriers to adopting Industry 4.0 technologies in different contexts.