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Understand the external environment and trends impacting customer preferences. Understanding the elements of customer segmentation and positioning for products such as backpacks and travel luggage. Understand the levers for turnaround management. Qualitative evaluation of an opportunity for investment – greenfield versus brownfield. Developing a marketing plan for growth.

Indian Luggage market has an oligopoly structure with three major brands – very important person (VIP) Industries, Samsonite and Safari Industries Ltd. (SIL), holding around 90% share of the organized market. The market had evolved slowly, with the players offering limited assortment and having limited manufacturing capacities in India. SIL, having been in business for nearly 25 years, had been registering a flat top line. In 2011, the top management shuffle at VIP Industries witnessed the exit of the then MD, Mr Sudhir Jatia. Mr Jatia decided to acquire the majority stake of 56.55% for Rs 29 crores for the struggling SIL. What followed in the subsequent years was the resurgence of SIL to a noteworthy position in the industry. From a merely 2% market share in 2011, SIL went on to hold nearly 16% market share in 2019. This growth in market share, along with CAGR of almost 15% for the luggage market overall, has boosted the revenue of SIL by nearly 10-folds from INR 620m in 2011 to INR 5.73bn in the year 2018. Several reasons had been identified behind the growth of this company, such as – Mr Jatia’s leadership style, focus on profitable stock keeping units, acquisition of other brands, operational efficiency and financial infusion. However, the larger question was that – Will SIL, which had been following a challenger strategy to date, be able to overcome the leaders in the industry? What strategies should it pursue now? And what obstacles can it expect on this anticipated journey of growth?

Undergraduate and post-graduate.

Teaching Notes are available for educators only.

CSS 11: Strategy.

Innovation is a prerequisite for economic and technological growth of any organization. Identifying enablers of innovation can help managers accelerate the process of economic growth. The purpose of this paper is to identify the prominent enablers of innovation in small and medium enterprises (SMEs) that can help overhauling organizations and benefit them economically.

A comprehensive literature review and expert’s opinions have been applied to identify enablers of innovation. A case of automotive component supplier is considered for conducting the research. A coalescence of Grey and DEMATEL methodologies has been incorporated to first rank the enablers of innovation based on their importance and then identify the causal relationship among these enablers by characterizing enablers into causal and effect groups.

A few important enablers, namely, entrepreneur traits, knowledge management, resources for innovation, and linkage capabilities, have been identified as prominent enablers for successful innovation in SMEs.

This paper identifies enablers of innovation in SMEs and the causal relationship between these enablers. The identified enablers and the causal relationship between these enablers will help managers of small organizations in selecting the enablers that need to be focused on, which, in turn, can drive other enablers of innovation, thus saving time and resources of the organizations.

This paper uses a novel Grey–DEMATEL methodology to identify the causal relationship among enablers and also contributes to the literature on innovation by identifying enablers of innovation in SMEs.

The purpose of this paper is to identify and prioritise supply chain finance (SCF) adoption enablers and develop a novel comprehensive framework to select supplier firms based on their SCF adoption capability.

The study deploys a three-phase method to identify and prioritise SCF adoption enablers, followed by developing a model to select suppliers according to their SCF adoption capability. An extensive literature review, followed by a Delphi approach-based expert interview, has been used to finalise the enablers. Using the Best Worst Method and the VIsekriterijumsko KOmpromisno Rangiranje technique, a supplier selection model has been developed in the context of a case company.

The financial health and technological advancement variables received the top priority, followed by collaborative efficiency, whereas the human resources and organisational variables received the slightest significance. A supplier selection framework has also been developed by using the adoption capability of these factors by the supplier partners. In this study’s model, Supplier 4 exhibited better SCF adoption capability and received the top priority.

Manufacturing supply chains in a developing country are the scope of the current study. Extensive future studies are required to derive a global consensus.

The proposed framework of this study can be used to select supplier firms based on their SCF adoption capability. Policymakers can emphasise the most critical enablers of SCF adoption to assist small supplier firms to be a part of the advanced global supply chains.

The current study established a novel comprehensive framework for supplier selection based on the Supply Chain Finance adoption capability of MSME supplier firms.

Technology and knowledge have become the buzzwords of the new millennium. Technological changes and demanding customers are creating a more knowledge intensive, turbulent, complex and uncertain environment. Organizations, which are able to continually build faster and cheaper new strategic assets than their competitors, create long-term competitive advantages. Thus, the growth of companies is directly associated with innovativeness and technological development, especially for small organizations that are more vulnerable to dynamic changes in market place. Organizations need a strategic framework that can help them to achieve the goal of technology development and competitiveness. The purpose of this paper is to develop such strategic framework for small organizations for their technology development and, hence, survival in marketplace.

Options field methodology, options profile methodology, analytic hierarchy process (AHP) and fuzzy set theory are utilized to generate various options and profiles to propose a conceptual framework for technology development.

The results from the study showed that “mixed approach,” “strategic simulation approach” and the “regulatory environment approach,” in this order, emerged as the top three important options for the strategic technological development of small manufacturing enterprises.

This result can provide an original and more accurate implementation pathway toward technological innovative development in emerging economies. The proposed framework can provide valuable guidelines and recommendations to practicing managers and analysts for policy development to promote innovative and technological developments.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

Sustainability trends have changed the modus operandi in businesses even as the market environment becomes more socially conscious. However, relatively little research has been conducted on integrating social sustainability aspects with a focus on corporate social responsibility (CSR) into the selection of suppliers in the service sector, particularly the banking industry. In this paper, this study aims to propose a CSR decision support methodology to evaluate and prioritize socially responsible suppliers.

A novel integrated decision support methodology composed of Shannon Entropy and TOmada de Decisão Interativa e Multicritério (TODIM) methods is introduced. The Shannon-Entropy approach is used to estimate CSR factor weights, and TODIM is used to rank the suppliers, with the process completed in a group decision setting.

A Nigerian bank was used as a case study to test and show the usefulness of the CSR-based decision framework in evaluating and selecting socially responsible suppliers. The results show the topmost ranked suppliers that are recommended for future negotiations by the case (bank). The study will enable banks to select socially responsible suppliers, which could accelerate the attainment of sustainability objectives, protect their reputations and improve competitiveness.

This study pioneers the application of a novel decision methodology based on Shannon Entropy and TODIM in selecting socially sustainable suppliers in the Banking sector of an African emerging economy-Nigeria.

The objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.

To accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.

In the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.

Employing the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.

This research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.

The study's goal was to identify the factors contributing to the practical completion of Kaizen events (KEs). The effect of the work-study man's characteristics, the supervisor's conduct and the autonomy of the Kaizen team are analysed in this study.

Data were collected from 249 respondents working in the manufacturing sector in India, mainly those who had been involved in Kaizen projects. Three-step procedures, namely, exploratory factor analysis, confirmatory factor analysis and partial least squares, have been applied to test the research hypotheses through structural equational modelling.

The exploratory factor analysis extracted in-role performance, creative performance and human aspect as latent variables explaining work-study man's performance (eigenvalue = 1). The study's findings indicate that the performance of work-study man (in-role, creative and human) and supervisors' conduct is directly related to the success of KEs. It was shown that supervisors might influence the outcomes of KEs only by moderating the human aspects. Additionally, the degree of autonomy of the Kaizen team was found having a significant positive relationship with the success of KEs.

The current study suggests that in-role and creative performance are prime assets of a work-study man. At the same time, the human aspect is a delicate issue that can affect the supervisor's behaviour. Therefore, the study implies that work-study men have the tactics and abilities to work with other co-workers to make a Kaizen project successful.

Although the significance of Kaizen projects has been widely emphasised, past research has failed to establish what factors contribute to the success of Kaizen efforts. Similarly, the supervisor's critical role has been highlighted several times. However, it is unclear how their conduct influences the relationship between work-study man's performance and the effectiveness of Kaizen projects. This study contributes significantly to organisational culture and human resource management by answering these questions.

The aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.

The Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.

The total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.

The findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.

This study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.