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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 – This chapter develops the case for a global Greenscape. It introduces the green global marketplace (Greenscape) to better understand the global green market.Design/methodology/approach – The chapter introduces current green market practices and adopts case study methodology to present three distinct green cases related to renewable energy, process technology and wastewater recycling and their international market activities. The chapter offers discussion on findings and incorporates the novel technique of discourse analysis using Leximancer 3.0.Findings – The case shows how the Greendex Report (2012) positions Brazil, India, China and Russia at the top of the markets for green product penetration. The developed nations of USA, France and Canada make up the bottom rankings. The chapter finds essential elements for creating the global Greenscape and marketing of green technologies.Research limitations/implications (if applicable) – Empirical research testing success pathways and destination opportunities is desirable.Practical implications (if applicable) – The ‘success and failure criteria’ identify how planning, patent and partnerships are essential for successful entry. Specific market research on G(reen) markets, market information, marketing functions for market entry and market diffusion for renewable products and process technologies such as supply chain elements, and how these interrelate with achieving sustainability goals is essential for successful entry.Originality/value of chapter – The chapter offers a novel and original approach to international green market penetration and offers analysis related to the new world BRIC countries that have been little explored.