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This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Sustainability has become the topic of the era, and the world is moving toward a circular economy. As part of it, companies are also integrating sustainability in supply chain management. Therefore, this study conducts a technology-empowered systematic literature review to examine the state of the literature on the technologies in the sustainable supply chain. The review reveals that limited studies have been conducted in the area. There is still scope for the researchers to explore new innovative technologies, many clusters in author collaboration, and involve more sectors in the study. Future studies can enrich the field by employing other methodologies like meta-analysis review.

Purpose: Technological innovations and frameworks that provide a framework for unification have evolved to improve information exchange across organisational units and information security. These integration technologies share and communicate information using defined protocols and different data. Service-oriented architecture (SOA) is a significant emerging approach that enables modular design solution construction.

Methodology: These designs are beneficial when many apps operating on different architectures and networks need to connect. A well-defined strategy and company-specific guidelines are essential for ensuring the firm’s systematic adoption of such an architecture. The critical components of MASSOASCM ‘(Multi-Agent System Service Oriented Architecture Supply Chain Management’ are a multi-agent system (MAS), a service-oriented structure, and supplier management. The MASSOASCM model has been made, and a production unit has been made to show how it works.

Findings: It has been stated that it saves development costs, and inventory management, all of which are critical concerns in any company. Our goal is to create an inventory control approach that relies on MAS and SOA but also a simulation that demonstrates how it works and may enhance Supply Chain Management (SCM) productivity in a production plant.

Practical Implications: The SCM implementation comprises three different services: SCM, SOA, and MAS. These facilities are constructed, maintained, planned, and implemented individually before being brought together collectively using MAS and SOA techniques.