Data Driven Quality Management Systems for improving Supply Chain Management Performance

Call for papers for: The TQM Journal

Data Driven Quality Management Systems for improving Supply Chain Management Performance
__________________________________________________________________
Special issue call for papers from The TQM Journal

Guest Editor
Surajit Bag Ph.D.
Research Associate, Department of Transport and Supply Chain Management, University of Johannesburg, South Africa

Peter Kilbourn D. Com
Senior Deputy Head and Senior Lecturer, Department of Transport and Supply Chain Management, University of Johannesburg, South Africa

Noleen Pisa Ph.D
Head of Department and Senior Lecturer, Department of Transport and Supply Chain Management, University of Johannesburg, South Africa

Submission Deadline for Papers: 23rd December 2020


Introduction

In this era of fourth industrial revolution basic technologies such as internet and advanced technologies such as big data, cloud computing, cyber physical systems and industrial internet of things – present not just an opportunity, but a necessity for businesses to adapt to a new supply chain operations reality (Frank et al., 2019; Gupta et al., 2019). 
The application of such basic and advanced information and communication technologies in the domain of supply chain management is gaining popularity after executives realized the massive benefits of big data and analytics (Bag 2017; 2020a,b). Big data and analytics related capabilities can be categorized as data generation capabilities, data integration and management capabilities, advanced analytics capabilities, data visualization capabilities and data-driven culture (Arunachalam et al., 2018). Big data and analytics capabilities in supply chain management are a relatively new research area (Akter et al., 2016; Gunasekaran et al., 2017; Wamba et al., 2017) and there is lack of theoretically grounded studies on the application of big data and analytics capabilities in quality management function. Six critical quality related management systems are Japanese Total Quality Control, Total Quality Management, Deming's system of profound knowledge, Business Process Reengineering, Lean Thinking and Six Sigma (Chiarini, 2011). Integrating digital technologies in managing such traditional quality management systems i.e. data driven quality management systems is the future of quality (Gunasekaran et al., 2019). The core concept of digital quality management is about aligning the quality management practices with the emerging capabilities of digital technologies; to help drive organizations toward supply chain sustainability. Data driven quality management represents an opportunity to utilize those digital technologies to realign quality functions with broader organizational strategy. Developing an effective digital quality strategy will enable organizations to address long-standing quality problems. The concept of data driven quality management systems presents an opportunity for organizations to review the root causes of current barriers to quality success, and engage in strategic planning to explore how new digital technologies and the advantages they deliver such as improved data transparency and high quality data-driven insights can be leveraged to achieve a culture of  excellence. 
Organizations face various challenges in data driven quality management systems (Foidl and Felderer, 2015; Shin et al., 2018). These continuing challenges create questions about the current status in the journey of quality and progress that have happened in the conventional quality management systems especially during the new product design, development, manufacturing, packaging, storage and dispatch stages of supply chain in this digital era.

Aims


This special issue intends to explore the significance of human factors in data driven quality management systems. Literature indicates that organization culture plays a critical role in implementation of world class quality management practices. Leadership skills, collaboration with suppliers and customers can improve quality and reduce the cost of poor quality (Hyun Park et al., 2017; Gunasekaran et al., 2019). Clearly, more focus is required on the human aspects of quality management systems in this digital era. Future researchers are invited to identify the challenges arising from human factors in data driven quality management systems and adoption of short and long term strategies to overcome such challenges.

The following illustrative questions will guide submissions for this special issue:
•    What are the drivers, enablers, barriers, and challenges of data driven quality management systems for process/resource optimization in sustainable supply chain management?
•    How can organization leaders help to develop and implement synergies among employee empowerment and data driven quality management systems?
•    What is the role of basic and advanced technologies related applications in data driven quality management systems for improving sustainable supply chain management performance?    

Proposed Special Issue Outcome


In view of these broad questions, the potential topics for the special issue include (but are not limited to):
•    Drivers, enablers, barriers, and challenges of data driven quality management systems for process/resource optimization in a supply chain
•    Capturing the impact of collaborative relationships in data driven quality management systems
•    Leadership styles, organization culture, information technology and big data infrastructure in achieving economical sustainability
•    Human resource strategies for involvement of employees within organizations for digital quality management systems
•    Role of institutional pressures in designing data driven quality management business models in a multinational company.
•    Role of basic and advanced technologies in data driven quality management systems for improving sustainable supply chain management performance
•    Develop new organization theories or extend existing theories in the field of digital quality management.

We welcome original research work based on theoretical, conceptual, empirical, and review papers, from scholars, industrial researchers, corporate people, governmental officers and NGOs.
Submissions should comply with the journal author guidelines here.

Submissions should be made through ScholarOne Manuscripts, the online submission and peer review system. 

References

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R. and Childe, S. J. (2016), “How to improve firm performance using big data analytics capability and business strategy alignment?”, International Journal of Production Economics, Vol. 182, pp. 113-131.
Arunachalam, D., Kumar, N., and Kawalek, J. P. (2018), “Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice”, Transportation Research Part E: Logistics and Transportation Review, Vol.114, pp. 416-436. 
Bag, S., Wood, L. C., Mangla, S. K. and Luthra, S. (2020a), “Procurement 4.0 and its implications on business process performance in a circular economy”, Resources, Conservation and Recycling, Vol. 152 No. January, 104502.
Bag, S., Wood, L. C., Xu, L., Dhamija, P. and Kayikci, Y. (2020b), “Big data analytics as an operational excellence approach to enhance sustainable supply chain performance”, Resources, Conservation and Recycling, Vol. 153 No. February, 104559. 
Bag, S. (2017), “Big Data and Predictive Analysis is Key to Superior Supply Chain Performance: A South African Experience”, International Journal of Information Systems and Supply Chain Management, Vol. 10 No. 2, pp. 66-84.
Chiarini, A. (2011), “Japanese total quality control, TQM, Deming's system of profound knowledge, BPR, Lean and Six Sigma: Comparison and discussion”, International Journal of Lean Six Sigma, Vol. 2 No. 4, pp. 332-355.
Foidl, H. and Felderer, M. (2015), “Research challenges of industry 4.0 for quality management”, In International Conference on Enterprise Resource Planning Systems, pp. 121-137. Springer, Cham.
Frank, A. G., Dalenogare, L. S. and Ayala, N. F. (2019), “Industry 4.0 technologies: Implementation patterns in manufacturing companies”, International Journal of Production Economics, Vol. 210 No. April, pp. 15-26.
Gunasekaran, A., Subramanian, N. and Ngai, W. T. E. (2019), “Quality management in the 21st century enterprises: Research pathway towards Industry 4.0”, Vol.207 No. January, pp. 125-129
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B. and Akter, S. (2017), “Big data and predictive analytics for supply chain and organizational performance”, Journal of Business Research, Vol. 70, pp. 308-317.
Gupta, S., Drave, V. A., Bag, S. and Luo, Z. (2019), “Leveraging smart supply chain and information system agility for supply chain flexibility”, Information Systems Frontiers, Vol. 21 No. 3, pp. 547-564.
Hyun Park, S., Seon Shin, W., Hyun Park, Y. and Lee, Y. (2017), “Building a new culture for quality management in the era of the Fourth Industrial Revolution”, Total Quality Management & Business Excellence, Vol. 28 No. (9-10), pp. 934-945.
Shin, W. S., Dahlgaard, J. J., Dahlgaard-Park, S. M. and Kim, M. G. (2018), “A Quality Scorecard for the era of Industry 4.0”, Total Quality Management & Business Excellence, Vol. 29 No. (9-10), pp. 959-976.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R. and Childe, S. J. (2017), “Big data analytics and firm performance: Effects of dynamic capabilities”, Journal of Business Research, Vol. 70, pp. 356-365.