Relationship of Manufacturing Flexibility with Organizational Strategy

IT Capabilities, Strategic Flexibility and Organizational Resilience in SMEs Post-COVID-19: A Mediating and Moderating Role of Big Data Analytics Capabilities

This research provides a novel progression to the existing research about big data analytics capabilities (BDAC) by investigating and measuring its influence on organizational resilience and strategic flexibility. Toward that end, 400 different SMEs in Saudi Arabia were approached. Data were collected via questionnaire. Results confirm that the ability to handle big data analytics totally mediates the relationship between IT capabilities and strategic flexibility. Big data infrastructure flexibility has a negative effect on strategic flexibility. Big data personal expertise not only negatively affects the relationship between IT capabilities and strategic flexibility but also stimulates and reinforces the relationship between strategic flexibility and organizational resilience. The critical pathway developed and tested the trend to make the organization as an immune system able to make the best of the worst. This implies the urgent need for policymakers and managers to adopt and comprehend the concept of BDAC instead of IT capabilities to define oriented plans specifically formulated for stimulating strategic flexibility and organizational resilience. By adopting the proposed model, SMEs can interact more effectively internally and externally.

Prioritization of Environmental Uncertainty and Manufacturing Flexibility for Labor-Intensive Industry: A Case Study on Ready-Made Garment Industries in Bangladesh

Manufacturing flexibility is a widely accepted manufacturing strategy for mitigating the negative impacts of environmental uncertainty on firm performance and is also a required strategic attribute to acclimatize mass customization and agile manufacturing. Manufacturing flexibility has been adequately studied for technology-intensive industries but remains inadequately addressed for labor-intensive industries. In this study, a framework is proposed for sorting the relevant manufacturing flexibility types for the relevant environmental uncertainty types as an initial step towards implementing manufacturing flexibility in labor-intensive industries. This study considered the RMG (ready-made garment) industries in Bangladesh, which are mostly labor-intensive, as a case. Different types of manufacturing flexibility and environmental uncertainty were identified through a deductive approach from the existing literature and theory. Then, final sorting was conducted through a focus group discussion using the analytical hierarchy process (AHP) and decision making trial and evaluation laboratory (DEMATEL) techniques. This study revealed that demand, competitor, supplier, and technology uncertainty were ranked sequentially from first to fourth. This study also revealed that demand and competitor uncertainty would be the first-line focus, and supplier uncertainty the second-line focus, of decision makers. Similarly, new product, volume, workforce, and modification flexibility were ranked sequentially from first to fourth, and these would be considered first-line focuses by decision makers. This study also showed that all types of environmental uncertainties had an internal effect (one type affects another type within the types of environmental uncertainty). Internal effects among the different types of manufacturing flexibility were also identified. This study contributes to the theory of manufacturing flexibility for labor-intensive industries and will help decision makers gradually implement manufacturing flexibility based on their capacity and goal.

Lead time prediction for sheeter machine production in a paper conversion industry

Lead time is a critical performance measure in any manufacturing setting Key Performance Indicator (KPI). The same is true in the paper conversion industry, which has a significant degree of product variability. Due to the great variety of their products, all industries must be able to foresee and plan ahead in order to meet client demand. With contemporary research concentrating on machine learning and simulation techniques, businesses must implement a manufacturing execution system (MES) to track data. However, without such a framework, applying machine learning and simulation approaches becomes difficult. This study introduces a novel method for forecasting lead time (special to sheeter machines used in the paper conversion sector) by combining the time required to process the reel (sheeting time) with the human (setup) elements. The method used to calculate the sheeting time takes product parameters into account, allowing for product-specific lead time forecast. As a result, a very successful 'product-specific' lead time prediction approach for small scale enterprises has been developed that enables production planning without relying on current and data-intensive prediction methods such as machine learning and simulation.

The Trajectory of Two Decades of Global Journal of Flexible Systems Management and Flexibility Research: A Bibliometric Analysis

In the year 2020, the Global Journal of Flexible Systems Management (GJFSM) has celebrated its two decades of publication. This study is an attempt to commemorate the two decades of publication by presenting the overview of the GJFSM along with the trajectory of flexibility research in various journals. By using multiple bibliometric tools and indicators, the study finds that the GJFSM has grown over the years in terms of total publications and citations. The contributors are from across the globe, i.e., South America, North America, Asia, Europe, Africa, and the Pacific. The Journal publishes several flexibility areas, including information systems flexibility, financial flexibility, supply chain flexibility, technology management flexibility, marketing flexibility, organizational flexibility, strategic flexibility, and manufacturing flexibility. The GJFSM is cited by authors from various countries across the globe. It has been cited across different Scopus categorizations, including "Strategy and Management," "Business and International Management," "General Business, Management and Accounting," "Industrial and Manufacturing Engineering," and "Management Science and Operations Research." Keyword co-occurrences analysis helps to analyze the various groups of keywords cited together in the GJFSM. Co-citation analysis of references helps to identify crucial clusters of GJFSM, i.e., strategic flexibility, manufacturing flexibility, the conceptual framework of flexibility, supply chain flexibility, modeling flexibility, and application of TISM. Overall, GJFSM has seen an increase in both publications and citations, reflecting its increasing presence among the journals publishing flexibility research. The diversity of flexibility research and its contributions to research under-one-roof make this Journal unique. The paper concludes with the gap areas of flexibility research and the way forward.

Technology and Entrepreneurial Marketing Decisions During COVID-19

The prime concern of this study is to explore how technology influences entrepreneurial marketing decisions during the world pandemic (COVID-19). The study uses a sample of 127 SMEs from Bangladesh. Data were collected by sending out questionnaires electronically and by mail. Smart PLS (SEM) 3.0 was used to analyse the data following the quantitative method. The study reveals positive and significant relationships between entrepreneurial opportunity recognition, opportunity development and opportunity exploitation with their entrepreneurial marketing decisions. It also claims that entrepreneurial passion mediates the relationship between entrepreneurial opportunity recognition and opportunity development with their entrepreneurial marketing decisions. However, entrepreneurial passion does not mediate the relationship between entrepreneurial opportunity exploitation and entrepreneurial marketing decisions. The study offers researchers a broader and more wide-ranging view of the importance of artificial intelligence in small firms. Researchers, educators and practitioners will benefit from the findings. The analyses are more complex and varied than the methodologies used in most of the limited previous research.

Definition of input and output variables to assess operations strategy efficiency frontier

Purpose

To investigate the relationship between performance frontier and operations strategy. A two-level conceptual framework is proposed based on performance elements that act as output/input variables and delimit the scope of the frontier analysis.

Design/methodology/approach

The framework proposition is based on the fourth round of high-performance manufacturing survey data. A representative set of variables for assessing performance based on operations strategy constructs is defined through multivariate data analysis techniques. The main method used is the principal component analysis.

Findings

The proposed first-level conceptual framework formalizes the relationships between performance frontier analysis techniques and operations strategy, delimiting the scope and the structural definitions. The second-level conceptual framework defines the constructs of the input and output dimensions for frontier analysis studies.

Originality/value

The paper contribution is developed in the gap of market-led orientation to study operations strategy performance frontier since most related literature focuses on capabilities development with a main focus on the resource-based view (RBV) approach. A conceptual framework based on the competitive priorities is therefore proposed to represent the operations strategy in the view of the frontier techniques. The value lies in defining performance measures which are not a straightforward task as the growth of organization competitiveness and complexity require multiple performance measures. A deeper understanding of frontier estimation on the operations strategy context is also provided, contributing to positively influence firms to succeed in the current dynamic competitive environments.