نشریه مدیریت صنعتی
سال چهاردهم شماره 47 (زمستان 1401)

Highly unstable social and economic ecosystems have inevitably resulted in severe momentums and adverse environmental changes. To overcome the mentioned challenge and also increase their competitiveness, most businesses have accepted a resilience approach. Moreover, the presence of so called techno-social Complex Systems has given rise to serious challenges in developing Business Plans. To have accurate planning for the business, social issues, technical processes, and their correlation should be simultaneously addressed. In this work, following a comprehensive review of the previous efforts, a more detailed and sophisticated explanation of how techno-social factors impact  resilience Business model will be investigated. In other words, we aim to develop an improved Business model  model for companies dealing with the mentioned challenges.

Meta-Synthesis method have been used for gathering necessary resources. A complete review of all related references has been carried out. 78 outstanding articles have been selected from all the papers published since 2000. The study and analysis of these 78 references has built the framework of our work.

In this work 85 sub-codes, indicating the impacts on resilience business model, have been successfully extracted. Using these codes and their proximity in definition, 21 core themes were defined in the next part. Finally, the developed themes have been categorized into four main elements of Business Model Ontology: customer, management, infrastructure, financial flows, and recommended value.

The finally presented model, enjoying simultaneous analysis of social parameters and corporate technical processes, has shown to provide more generality in modeling. The model would be a great help for senior managers to desing resilient  business model. In addition, this model can efficiently be used in thorough assessment of resilience in techno-social companies.

Smart product-service systems (SPSS) as a new paradigm was presented recently, which created considerable changes in the industry and production, especially in the production of smart home appliances. Despite many achievements in implementing SPSS, this system faces many challenges in the development and implementation processes. This study aims to identify and analyze the challenges of implementing an SPSS and evaluate and prioritize them as one of the most basic initial steps in implementing the system.
In this paper, first, the challenges of implementing SPSS are identified through literature review, document review, interviews with experts, and the fuzzy Delphi method. Then the best-worst rough-fuzzy method is used to prioritize the identified challenges.
Twenty challenges of implementing SPSS were classified into seven main groups consisting of staff, information security, technology, finance and process, design, infrastructure, and government/market, and evaluated by the best-worst rough-fuzzy method. The main challenges are financial and process, technology, employees, design, market and government, infrastructure and information security, respectively. In the same way, the weight and rank of all twenty challenges classified in these groups were determined. Since evaluating and prioritizing the challenges of implementing the SPSS is a multi-criteria decision-making process and includes uncertainties that may lead to incorrect evaluation results, in this research, the concepts of rough set theory and fuzzy logic, which are efficient in such conditions, are used as the prioritization method. Since the best-worst rough-fuzzy method simultaneously integrates both intra-individual and interpersonal uncertainty and takes advantage of the features of the fuzzy set in managing individual verbal ambiguities and the benefits of the rough set theory in examining the diversity of group preferences, the model has a good performance for finding optimal and fuzzy rough weights of challenges and makes the obtained evaluation more accurate and objective than the best-worst method based on fuzzy logic or rough theory.
Since SPSS is rapidly expanding as a new field in manufacturing and industry, and the development of smart home appliances is one of the waves of technology in the future and will provide new and exciting opportunities for businesses, home appliance manufacturing companies are bound to compete to get their share of these opportunities. Industrial enterprises, especially in the home appliance industry, can be more profitable, and improve their market share in this industry by turning to the integration of products and smart services based on information and communication technology and joining the production companies that use SPSS. Using the results of this study, manufacturing and service companies can more accurately assess the challenges ahead in implementing SPSS and appropriately allocate organizational resources to manage the challenges.

The influence of climate change on the frequency and severity of natural disasters is ever-increasing on a global scale. While natural disasters are unpredictable, emerging technologies can help with their prevention. Technologies allow responders to act sooner rather than later. The importance of the humanitarian supply chain in reducing the suffering and injuries caused by natural disasters is also undeniable. Developing emerging technologies offers an opportunity to improve the efficiency and effectiveness of responses of the humanitarian supply chain to disasters. Since Iran is one of the most natural catastrophe-prone countries in the world, this study aims to identify and prioritize emerging technologies suitable for its humanitarian supply chain. This study could provide solutions for responders and decision-makers to achieve an effective humanitarian supply chain.

This is descriptive survey research and its statistical population consists of five emerging technology experts in the Iranian humanitarian supply chain. To identify and prioritize applications of emerging technologies in the humanitarian supply chain, the Best-Worst method (BWM) was used.

Reviewing the extant academic literature, 12 emerging technologies in the humanitarian supply chain were investigated. After assessing their content validity, experts selected eight relevant technologies for Iran’s humanitarian supply chain; including “Big Data”, “Internet of Things”, “Augmented Reality”, “Smart Robots”, “Artificial Intelligence”, “Gamification”, “3D Printing”, and “Smart Advisors”. In the next step, two sets of questionnaires were distributed among the experts. The questionnaires were collected and analyzed to measure the attractiveness and capabilities of emerging technologies in Iran’s humanitarian supply chain. The weights of technologies' attractiveness were calculated based on five filled-in questionnaires and by the Best-Worst Method and LINGO software. Next, to assess the technical capability of each emerging technology, eight Likert scale questionnaires were distributed. Finally, the gathered research data was analyzed using SPSS 26. Capability-Attractiveness Matrix was then formed. Analyzing areas of the Matrix helped the researchers determine specific actions suitable for each emerging technology. Based on the findings, “Smart Robots”, “3D Printing” and “Smart Advisors” technologies were placed in the “Position protection/Development” area with the highest capability and attractiveness. “Artificial Intelligence” was placed in the “improvement” area with high attractiveness and relatively low capability. “Internet of Things” and “Gamification” had a high capability andrelatively low attractiveness. “Big Data” and “Augmented Reality” with relatively low capability and attractiveness had the least priority in Iran’s Humanitarian Supply Chain.

The critical role that emerging technologies play in disaster preparedness and recovery is increasingly becoming recognized. The findings of the BWM Method and Capability-Attractiveness Matrix can be a good help for decision-makers and key players in the humanitarian supply chain. Natural disaster preparedness and collaboration to reduce impact across the humanitarian supply chain have never been more important. Utilizing these emerging technologies can minimize fatalities and injuries caused by natural disasters. Emerging technologies need to be adapted to various situations that arise during disaster relief.

While the closed-loop supply chain network was created launch, to design, and exploit the flow of materials between chain centers, supply chains face different risks, including operational ones and disruption. Each of such risks can lead to irreparable damage. Therefore, designing or redesigning supply chains to make them resilient against different risks is one of the most important programs that potentially affect the supply chain’s performance. The purpose of this research  is to design a resilient closed-loop supply chain network under the operational risks and disruption conditions by the Mulvey approach in Tehran's food industry companies with multi-products.
In this research, the problem of the resilient closed-loop supply chain is considered as a network of nodes (production sites) that are connected by arcs (paths). The model is formulated as an integer programming, the objective function of which involves maximizing the number of nodes in estimating demand and minimizing costs based on a series of scenarios developed by the Mulvey approach.
In the practical phase, first, a closed-loop supply chain consisting of 10 manufacturers, 300 distributors, and two types of products was considered for modeling. Then, three more important scenarios with probabilities of 0.3, 0.2, and 0.5 were developed to present different amounts of customer demand and facilities capacity depending on the number of disruptions. Solving the problem for the multi-product food companies, using the LP metric model showed that despite no disruption in the supply chain, the robust optimal solutions for the first and second objective functions were equal to 99.484 and 790.50, respectively. In terms of manufactured products; in the first node, the amounts of products 1 and 2 did not change for the first and second scenarios but changed by 1.4 and 2.5 units in the third scenario. In the third node, the amount of product 1 did not change for the first scenario, but for the second and third scenarios, it changed by 10.60 and 6.8, respectively. The amount of product 2 did not change for the first and third scenarios but changed by 7.7 for the second scenario. In the 10th node, the amounts of products 1 and 2 did not change for the second scenario, but the amount of product 1 changed by 2.8 and 2.3 for the first and third scenarios. In addition, the amount of product 2 changed by 10.3 and 2.8 for the first and third scenarios, respectively. In the other nodes, the amounts of the products did not change. For both of the products in different nodes, except in nodes 4, 6, 7, and 9, and product 1 in node 8, some problems had to be fixed.
According to the findings, developing the proposed model reduces operational risks and disruption as the most important causes of inefficiency in the supply chain. So, it is necessary to design robust and resilient supply chains in all industries, especially in the food industry due to its significance.

Food production and supply chains contribute significantly to carbon emissions. The beef supply chain is of those food industry sectors with significant carbon emissions in its entire supply chain. In this study, the beef supply chain is studied by considering carbon emissions under the coordination contracts and the key decisions of supply chain players.

The need to reduce emissions in energy consumption of upstream suppliers and downstream distributors encourages suppliers to do promotional work for manufacturing low-carbon products. Accordingly, the retailers would have the incentive to offer some encouraging schemes to the manufacturers, which stimulates the manufacturer's motivation to reduce emissions. In this research, price sensitivity, level of greenery, and the amount of advertising are considered to investigate the effects of these parameters on chain profitability of the chain and the decisions of the channel members. Cooperative Advertising contracts (CA), Revenue Sharing contracts (RS), and Cost Sharing contracts (CS) are also examined. Examining the decisions of supply chain actors and analyzing the results, the researchers proved that the increase in the effectiveness of green product advertisements pushes up the number of applicants for buying green products and makes the supply chain provide products with higher levels of greening.

Based on the achieved results, corporate advertising contracts increase retail advertising investment and total supply chain profit. They also increase channel effectiveness. This study also discussed the key role of advertising and its effects on the profitability of the channel. The results shied that the best way to increase the demand for green products is to raise the awareness of customers about the environment through advertising. When consumers' sensitivity to the level of the greening of a product increases, they tend to pay higher prices for low-carbon products. In addition, this study asserts that a cost-sharing agreement is also beneficial for companies and the supply chain, since it increases the level of greenery in products, brings higher profits for individual companies, and increases supply chain profits.

This study comes with practical implications that can be used as support for decision-making. It indicated that a revenue-sharing contract with a low-performance difference after cost-sharing is a suitable option for the supply chain. Unlike the cost-sharing contract, almost all retail stores have computer systems and barcodes to track the sales of each meat product, so monitoring and verifying revenue is not a difficult task for the producer. A revenue-sharing contract not only helps the manufacturer and retailer achieve a "win-win" situation but also leads to a greater supply chain profit than a decentralized policy.

The disruption caused by natural, economic and political crises, piracy, and disturbance in information systems and human agents (low probability factors with high impact intensity) travel like a wave from the upstream of the supply chain, i.e. from the suppliers to its downstream. The ripple effect is the phenomenon of disruption in the supply chain with serious impacts on the performance of the supply chain. It can influence sales, on-time delivery, and profits. It has more serious consequences in reducing short-term performance, which leads to market share reduction. This disruption affects the entire supply chain. It destroys the capacity or inventory of the chain, disrupts its facilities, and causes material loss and production reduction in the next stages of the supply chain. Since disruptions are inevitable in supply chains, it is necessary to address this issue and evaluate the impact of disruption risk and its distribution on the supply chain. The purpose of the present study is to provide a simulation model for the behavior of disruption propagation in a three-stage supply chain. It tries to take into account both the vulnerability and recovery capabilities of the disrupted supplier and to determine a criterion for quantitatively predicting the ripple effect of the supplier disruption on the distributor. As the disruption in suppliers prolongs deliveries to customers, and any increase in lead time will decrease sales, the current study seeks to present the quantitative estimation of the ripple effect of supplier disruption on the distributor in terms of lead time and lost sales.
First, using a discrete-time Markov chain, a recovery and vulnerability model was proposed for long-term disrupted suppliers with three states of operational, semi-operational, and fully disrupted. The model was integrated with a Bayesian network to show the way the supplier disruption spreads to the manufacturer and distributor by dynamic Bayesian modeling. Then, in order to quantitatively show the ripple effect of the supplier disruption on the distributor, a criterion was established in terms of the lead time for the delivery of goods and lost sales based on the decision tree and the data of the dynamic Bayesian simulation model.
The model was presented through figures, definitions, and mathematical relations. Its capability was shown by an example based on the status of suppliers, manufacturers, and distributors in the production of a voltage-increasing electronic component. The obtained results showed that when a disturbance occurs in upstream of a supply chain, it spreads as a ripple to its downstream and affects its performance.
The proposed model by this study can quantitatively show the spread and impact of disruption along the supply chain. It can reveal the hidden risk paths and the role of each entity in the chain at the time of disruption. By quantitatively estimating the vulnerability caused by any disruption in the supply chain, the proposed model can help managers identify deviations or the risk of deviations in the chain in time. It can also help them with analyzing and performing control measures to restore the operation and process of the chain and prioritize possible policies and recovery. It is also useful for the selection of suppliers and inventory plans and making the right decisions to reduce the vulnerability of the supply chain sectors quickly with minimal cost.