Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs

Hard dimensions evaluation in sustainable supply chain management for environmentally adaptive and mitigated adverse eco‐effect environmental policies

In the oil and gas industry, adopting policies that can reduce the negative environmental effect is vital. Environmentally Sustainable Supply Chain Management (ESSCM) is an approach to carrying out Supply Chain Management (SCM) in an eco‐friendly manner and according to environmental requirements. There are different environmental policies that companies can apply based on their resource availability. Therefore, this study aims to evaluate the impact of hard dimensions on Environmentally Adaptive (EA) and Mitigated Adverse Eco‐Effect (MAE) policies in the oil and gas industry. To rank the data, Bayesian Best‐Worst Method (BWM) and Ordinal Priority Approach (OPA) have been applied. Cause‐and‐effect relationships are then calculated by employing the Decision‐Making Trial and Evaluation Laboratory (DEMATEL) technique. The results indicate that the ranking of the hard dimensions varies based on the companies' business policies and their new product/technology development projects. In other words, the findings of this research demonstrate that ‘innovation’ is the crucial dimension in companies that are focussed on developing eco‐friendly products while ‘technologies for cleaner production’ is the most important dimension in the companies attempting to reduce destructive consequences on the environment. In both types of the company policies, ‘lean manufacturing’, ‘total quality management’, and ‘institutional pressures’ are the key dimensions for a successful implementation of ESSCM while the least important dimensions include ‘supplier relationship management’, ‘green purchasing’, and ‘green logistics’. The findings of this research can assist the decision‐makers in the oil and gas sector in prioritising and identifying the interrelationship of the dimensions that significantly impact the ESSCM

International Strategic Alliances for Collaborative Product Innovation: An Agent-Based Scenario Analysis in Biopharmaceutical Industry

The lack of ability, knowledge, and resources at a national level leads companies to choose international strategic alliances (ISAs) as an appropriate solution to enter the global markets. In this regard, selecting appropriate partners is a fundamental step in reducing the risks and costs of these alliances. This article analyzes international partner selection scenarios using agent-based simulations in Iran's biopharmaceutical industry. In line with the goal of this research, a complete understanding of the failure and success of collaboration product innovation is presented. First, the critical success factors (KSFs) in international strategic alliances were identified through a literature review (LR) and screened via Fuzzy Delphi. Next, selected factors were weighted by fuzzy SWARA (Stepwise Weight Assessment Ratio Analysis) and considered as simulation inputs for international partner selection. The simulation results indicated the critical role of the knowledge-matching degree and complementary resources and the insignificant impact of the company's reputation, government support, and experience in the international alliance. Furthermore, we found that pharmaceutical and biotech companies should emphasize their partner's complementary resources and develop an evaluation system to measure the knowledge-matching degree of their foreign partners in ISA. Eventually, this paper provides practical and applied solutions for biopharmaceutical industry managers to analyze business partners. The article also adds to the debate on ISAs in biopharmaceutical industries