In recent history Multinational Corporations MNCs from all industries have established their own production systems based on Toyotas unique success story Nevertheless industrial companies are still facing issues when it comes to the quantification of the influence of single system elements on the companys overall performance The following article presents an approach which allows decision makers to better determine site priorities define more impactful improvement measures and further leverage ones own production system by using the steadily increasing data available in manufacturing
Historic development of contemporary production systems Although the Toyota Production System (TPS) was developed as a specific production model for Toyota’s unique circumstances in the 1960s, such as lack of natural resources, lifetime employment practices, enterprise unions, need for an increased variety of products in smaller quantities (Sugimori et al. 1977, Lee and Jo 2007), TPS and its derivatives have clearly become the dominant production system of the 21st century, evidenced by its superior performance versus global competition (Cusumano 1988, Krafcik 1988, Womack et al. 1990). TPS has enabled Toyota to outperform its competitors in quality, reliability, production, cost, and growth, thus becoming the most successful car manufacturer worldwide (with annual sales of over 10m vehicles).
Over the last decades, the Toyota Production System has been widely studied by practitioners and researchers, and several companies from different industries have tried to adopt TPS within their own environment, resulting in very different success rates of implementation (Spear and Bowen 1999, Hino 2006, Morgan and Liker 2006).
One of the reasons attributed to this variance in the adoption rate is that most of the companies have focused on the tools and tactics of TPS without focalising on the basic set of operating principles. However, it is in fact the people who bring the system to life by working, communicating, resolving issues, and growing together, that companies should put emphasis on.
Only a relatively low number of companies have adapted the basic principles of TPS to their advantages, by focusing on some of the core TPS principles (Liker 2004). In this regard, many scholars coincide in necessary pre-conditions and constraints related to the transferability of TPS.
This academic group stresses that the successful implementation of TPS is dependent upon various organisational factors at recipient sites, such as long-term management strategies, labour-management cooperation, employee and union involvement, open communication and flat hierarchies, and investments in training (Haber et al. 1990, White et al. 1999, Lee and Jo 2007). Various case studies have proven that the adoption of lean involves a complex evolutionary process of organisational learning and interpretation and have demonstrated that both external and internal factors combine to form a complex causal chain, influencing the adoption and generating a certain pattern of path-dependence in the evolutionary trajectory of a particular production model (Maritan and Brush 2003, Jensen and Szulankski 2004, Collins and Schmenner 2007, Lee and Jo 2007).
Many production systems are specific to individual manufacturers, having evolved over time—by means of the continuous selection, interpretation, assimilation, and transmutation of the principles and operational elements of TPS. Nevertheless, in order to deal with changing business circumstances, nearly all companies fail when it comes to quantifying the influence of single system principles and elements (including manufacturing methods, work organisation, human resource management, and supply chain management) on the company’s overall performance.
In our understanding, sustainable performance improvements can only be attained if the company is able to determine what the strongest levers within their own production system are, in order to better determine site priorities and for target setting, define more impactful improvement measures and assign resources accordingly, and in the long run further enhance one’s own production system. For this purpose, the first step is to quantify the impact and influence of single production system principles and elements on the performance of the various sites within the manufacturing network. Once a profound knowledge of the coherences within the system has been achieved, the company can accurately tackle low performing sites and areas by setting a clear focus on principles and defining improvement initiatives that will assuredly lead to a significant performance increase.
This need arises, on the one hand, from the fact that a large number of MNCs have strategically used the rapidly increasing globalisation of the past two decades to grow internationally through acquisitions, mergers, and green-field establishments in foreign markets. Today, as economic conditions tighten and global competition toughens, many MNCs find themselves struggling with a dispersed, heterogeneous and low-performing manufacturing network. To improve operational capabilities in all sites of the network and, hence, increase the competitiveness of the MNC as a whole, the latest trend sees MNCs going from plant-specific improvement projects to multi-plant improvement programmes (Netland 2012, Netland and Aspelund 2014).
In regards to the content of such multi-plant improvement programmes, MNCs generally make use of proven production philosophies including, for example, Total Quality Management, Just-in-Time, Lean Thinking, Continuous Improvement, Six Sigma, Business Process Re-engineering, and World Class Manufacturing (Deming 1986, Schonberger 1986, Hammer and Champy 1995, Womack and Jones 1996, Zangwill and Kantor 1998, Schroeder et al. 2008, Monden 2010). In general, programmes that are based on one or a combination of these philosophies retain the same purpose under different names: They focus on making the most out of the existing resources and capabilities of a plant and share a common goal of improving the productivity of manufacturing operations (Repenning and Sterman 2002, Netland and Aspelund 2014). The question that arises at this point is whether the improvement measures and initiatives included in such multi-plant improvement programmes are based and/or aligned with the principles and elements of the underlying productions systems. Given the fact that these so-called meta-routines (Feldman and Pentland, 2003) and strategic organisational practices (Kostova, 1999) are vehicles for how organisations update what they do, the core challenge is to update and share knowledge within the network of sites – most often standardised in what has been called best practices (Voss 1995, Netland and Aspelund 2014) and may have been perpetuated in the MNCs production system.
On the other hand, the need for a thorough understanding of one’s own production system is reinforced by the ever increasing significance of (manufacturing) data in terms of its growing volumes, variety, and velocity (the speed with which it is being created and processed) (DBIS 2013). While data analytics has certainly been a part of MNCs agendas over the last decade, it is the scale and scope of change that big data is bringing that has attracted so much attention. As many novel phenomena it is oftentimes over-sold because of hype or misunderstanding. Nevertheless, there are several tangible case studies to-date that give evidence of the power of big data to generate value and competitive advantage. Among others, there are some first-hand examples our Institute is currently performing with various manufacturers in Europe within the scope of an industry project financed by the Swiss Commission for Technology and Innovation (CTI).
Big data analytics is defined as a “collection of data and technology that accesses, integrates, and reports all available data by filtering, correlating, and reporting insights not attainable with past data technologies” (APICS 2012). Its applications have been strong in financial services, insurance, retailing and healthcare sectors, while in manufacturing such examples remain comparatively small in number to-date. Companies such as Rolls Royce and Ford, which have reported to derive success from big data in predicting engine failures prior to their occurrence and in managing supplier risk (Goodwin 2013) further support the assumption that big data will undoubtedly become a key competitive factor for manufacturing companies of all sizes and sectors.
Data analytics in manufacturing has the potential to enable more sophisticated data-driven decision making and new ways to organise, learn, and innovate (Yiu 2012, Kiron 2013). Its effect may be evidenced in strengthening customer relationships, managing operations risk, improving operational efficiency, enhancing product quality, increasing service delivery or whatever the key business drivers may be (Kiron 2013). Lately, organisations are experiencing much more voluminous and unstructured data environments. In turn, this real-time information from sensors and RFID tags facilitates better asset and business process monitoring, greater end-to-end supply chain visibility, improved manufacturing and industrial automation, and an overall increased operations efficiency and effectiveness (Zelbst et al. 2011, Davenport et al. 2012, Wilkins 2013). For instance, big data enabled-automation and manufacturing allows real-time detection and diagnosis of production issues, and thus reduces significantly downtime risks and costs.
Based on its high operational and strategic potential, big data analytics has recently become the focus of a variety of scholars and practitioners. Some researchers suggest that big data is “the next big thing in innovation” (Gobble 2013), “the fourth paradigm of science” (Strawn 2012), or “the next frontier for innovation, competition, and productivity” (Manyika et al. 2011). As a result of this development, businesses and organizations are confronted with an increasing number and complexity of challenges related to big data, and many managers are still struggling to understand the major concepts and implementation possibilities, consequently failing to capture business value from big data.
The Institute of Technology Management at the University of St.Gallen, Switzerland, is among the leading research institutions in the field of Operational Excellence (OPEX) in the pharmaceutical industry and has been conducting research as well as industry projects in this field for more than 11 years. Today, our OPEX benchmarking database consists of 317 manufacturing sites from 124 different pharmaceutical companies, thus representing the largest independent OPEX benchmarking database in the pharmaceutical industry – worldwide. This strong quantitative benchmarking database allows us to support company-specific analyses with external data and therefore enhance the relevance and quality of project outcomes.
Last year, a globally leading pharmaceutical company approached our Institute for the conception of a Data-Informed Improvement Management system (DIIMS) based on their production system, which should bring the actual performance of the different production sites into the equation. Through a better understanding of the implications of their production system principles on site performance, and moving toward a data-based prioritisation of improvement measures, the pharma company hoped to significantly accelerate the realisation of a high performing manufacturing network. In order to ensure reliable and robust conclusions from the analyses, we quickly recognised that we would need to facilitate a performance comparison of the company’s internal manufacturing network with external sites included in our OPEX benchmarking database. At the same time, we should also allow for a comparison between the implementation level of the company’s production system principles in each manufacturing site and our so-called enabler (defined as methods and tools leading to better performance whose levels indicate the undertaken efforts for implementing OPEX) within the OPEX benchmarking database. By this, we would build the foundation for a semi-automated benchmarking of company-specific performance metrics and enabler with our continuously growing St.Gallen OPEX database. Consequently, we would enable more sophisticated data-driven decision making and new ways to define site’s priorities and for target setting. In the long term, the goal would be to further enhance the underlying metrics system and continuously improve the production system principles to accelerate performance upgrades of the manufacturing network and strengthen people’s involvement. For this purpose, we jointly defined the following project steps:
• Matching of the company’s production system principles to the St.Gallen OPEX enabler
• Matching of the company’s performance metrics to the St.Gallen OPEX metrics
• Understanding the implications of the company’s production system by quantitatively analysing the impact of single principles on the overall site performance
• Participation of various company-owned sites at the St.Gallen OPEX benchmarking in order to check the validity of DIIMS
• Start on-going benchmarking as long-term collaboration.
The described project is still in progress, and we are currently in the phase of enhancing the company’s metrics system by adding further relevant performance metrics in order to ensure a holistic calculation of the overall site performance. After having successfully matched the company’s production system principles to the OPEX enabler and having understood the impact of single principles on the overall St.Gallen OPEX performance, the next activities will focus on matching the different company-specific performance metrics. Once this step is finalised and the validity of DIIMS has been checked by comparing the project results with benchmarking data from several company-owned sites, our client will henceforth be able to semi-automatically assess the performance of their manufacturing network against a resilient database currently comprising 317 sites and with a confident outlook for stable growth.
The main advantage of matching the company’s internal performance metrics and enabler to the St.Gallen OPEX database is overcoming the need of a regular participation at external benchmarks by relying on one’s own metrics system, which is semi-automatically updated one way or another. This potential will even tend to increase over the near future with the ongoing technological developments toward big data enabled-automation and manufacturing, finally resulting in real-time comparisons of automatically generated data from machines, products, etc. However, the status quo (in our projects and described in literature) proves that there is still a long journey to go in order to reach that ultimate goal, and that profound knowledge about the implications of single production system principles is key for an impactful implementation of such data-informed improvement management systems.
01. APICS The Association for Operations Management (2012). APICS 2012 Big Data Insights and Innovations: Discovering emerging data practices in supply chain and operations management.
02. Collins, R. S.; Schmenner, R. W. (2007). Understanding persistently variable performance in plants. International Journal of Operations & Production Management, Vol. 27, p. 254.
03. Cusumano, M. (1988). Manufacturing innovation: lessons from the Japanese auto industry. MIT Sloan Management Review, 30(1): 29-39.4
04. Davenport, T. H.; Barth, P.; Bean, R. (2012). How “Big Data” Is Different. MIT Sloan Management Review, 54(1): 43-46.
05. Deming, W. E. (1986). Out of the Crisis: Quality, Productivity and Competitive Position, Cambridge University Press, Cambridge.
06. Department for Business- Innovation and Skills (2013). Seizing the data opportunity: A strategy for UK data capability.
07. Feldman, M. S.; Pentland, B. T. (2003). Reconceptualizing organizational routines as a source of flexibility and change. Administrative Science Quarterly, Vol. 48, pp. 94-118.
08. Goodwin, G. (2013). Takeaways from the MIT/Accenture Big Data in Manufacturing Conference.
09. Hammer, M.; Champy, J. (1995). Reengineering the Corporation: A Manifesto for Business Revolution. Nicolas Brealey, London.
10. Harber, D; Samson, D.; Dohal, A.; Wirth, A. (1990). Just-In-Time: the issue of implementation. International Journal of Operations & Production Management, 10(1): 21-30.
11. Hino, S. (2006). Inside the Mind of Toyota: Management Principles for Enduring Growth. Productivity Press, New York, NY.
12. Jensen, R.; Szulanski, G. (2004). Stickiness and the adaption of organizational practices in cross-border knowledge transfers. Journal of International Business Studies, Vol. 35, p. 508.
13. Kiron, D. (2013). Organizational Alignment is Key to Big Data Success. MIT Sloan Management Review, 54(3): 1-6.
14. Kostova, T. (1999). Transnational transfer of strategic organizational practices: a contextual perspective. The Academy of Management Review, Vol. 24, pp. 308-324.
15. Krafcik, J. (1988). Triumph of the lean production system. MIT Sloan Management Review, 30(1): 41-52.
16. Lee, B. H.; Jo, H. J. (2007). The mutation of the Toyota production system: adapting the TPS at Hyundai Motor Company. International Journal of Production Research, Vol. 45, pp.3665-3679.
17. Liker, J. K. (2004). The Toyota Way: 14 management principles from the world’s greatest manufacturer. McGraw-Hill, New York, NY.
18. Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.; Roxburgh, C.; Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute.
19. Maritan, C. A.; Brush, T. H. (2003). Heterogeneity and transferring practices implementing flow manufacturing in multiple plants. Strategic Management Journal, Vol. 24, pp. 945-959.
20. Monden, Y. (2010). Toyota Production System: An Integrated Approach to Just-In-Time. Productivity Press, New York, NY.
21. Morgan, J. M.; Liker, J. K. (2006). The Toyota Product Development System: Integrating People, Process, and Technology. Productivity Press, New York, NY.
22. Netland, T. H. (2012). Exploring the phenomenon of company-specific production systems: one-best-way or own-best-way? International Journal of Production Research, 51(4): 1084-1097.
23. Netland, T. H.; Aspelund, A. (2014). Multi-plant improvement programmes: a literature review and research agenda. International Journal of Operations & Production Management, 34(3): 390-418.
24. Repenning, N. P.; Sterman, J. D. (2002). Capability traps and self-confirming attribution errors in the dynamics of process improvement. Administrative Science Quarterly, Vol. 47, pp. 265-295.
25. Schonberger, R. J. (1986). World Class Manufacturing: The Lessons of Simplicity Applied. ASQC Quality Press, Milwaukee, WL.
26. Schroeder, R. G.; Linderman, K.; Liedtke, C.; Choo, A. S. (2008). Six Sigma: definition and underlying theory. Journal of Operations Management, Vol. 26, pp. 536-554.
27. Spear, S.; Bowen, H. K. (1999). Decoding the DNA of the Toyota Production System. Harvard Business Review, 77(5): 96-106
28. Strawn, G. O. (2012). Scientific Research: How Many Paradigms? EDUCAUSE Review, 47(3): 26.
29. Sugimori, Y.; Kusunoki, K.; Cho, F.; Uchikawa, S. (1977). Toyota Production System and Kanban system: materialisation of just-in-time and respect-for-human-system. International Journal of Production Research, 15(6): 553-564.
30. Voss, C. A. (1995). Alternative paradigms for manufacturing strategy. International Journal of Operations & Production Management, Vol. 15, pp. 5-16.
31. White, R.; Pearson, J.; Wilson, J. (1999). JIT manufacturing: a survey of implementation in small and large US manufacturers. Management Science, 45(1): 1-15.
32. Wilkins, J. (2013). Big data and its impact on manufacturing. Retrieved 19 March, 2016, from http://www.dpaonthenet.net/article/65238/Big-data-and-its-impact-on-manufacturing.aspx.
33. Womack, J. P.; Jones, D. T.; Roos, D. (1990). The Machine that Changed the World. Rawson Associates, New York, NY.
34. Womack, J. P.; Jones, D. T. (1996). Lean Thinking: Banish Waste and Create Wealth in Your Corporation. The Free Press, New York, NY.
35. Yiu, C. (2012). The Big Data Opportunity: Making Government faster, smarter and more personal. Policy Exchange. London: 36.
36. Zangwill, W. L.; Kantor, P. B. (1998). Toward a theory of continuous improvement and the learning curve. Management Science, Vol. 44, pp. 910-920.
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