This approach to planning supply chains through collaborative planning, forecasting and replenishment (CPFR) is highly dependent on collaborative applications and platforms that support analytics and advanced approaches to creating dashboards and balanced scored cards of sup[ply chain performance and value created (Huberman, Wilkinson, 2010).
The reliance on collaborative applications and platforms that are also capable of streamlining complex manufacturing processes, dropping costs per unit costs from each unit produced due to greater efficiencies being achieved with intelligence, is also an area of best practices beginning to emerge from collaborative systems in manufacturing (Rosenzweig, 2009). These systems have the ability to align information and knowledge to specific steps in a lean manufacturing process, further accelerating a company to its objectives of producing higher quality, lower cost products (Rosenzweig, 2009). This integration of lean manufacturing concepts and knowledge management is most often found in highly complex, specialized manufacturing operations. A prime example of this is in the area of production scheduling and production system planning and process definition. The Toyota Production System (TPS), perhaps one of the best-known systems in the automotive industry, is specifically designed to support highly integrative knowledge management workflows and development processes to onboard new suppliers as quickly and as accurately as possible (Dyer, Nobeoka, 2000). The Toyota Production System is also known for being able to covert expertise and intelligence into a lasting competitive advantage over time, turning supply chain expertise and insight into a formidable process advantage which is translated into profitability and growth over the long-term for many of the company's product liens globally (Dyer, Nobeoka, 2000).
Why IT and Business Strategies Must Have the Same Vocabulary
The reality is that any significant change within an organization is extremely difficult, and that resistance to chance has impeded and stopped more IT and company-wide strategies than any other single factor (Harley, 2011). To break down these barriers or resistance areas to change, what's needed is a strong focus on shared results and share outcomes all measured using a common set of analytics and metrics (LaValle, Lesser, Shockley, Hopkins, Kruschwitz, 2011). Once...
For resistance to change to be overcome the results of collaborative workflow results must be openly published, showing both the improvements and limitations of a given program (Huberman, Wilkinson, 2010). Only by doing this will a company be able to realize the full potential of collaborative platforms and systems over time.
Conclusion
The development of collaborative platforms and applications to streamline supply chain and complex manufacturing processes is predicated on how effectively An enterprise can manage and overcome resistance to change while also re-engineering wide areas of their production processes. Collaborative systems all share a commons et of attributes, with the need for greater analytics, data intelligence and integration being an unmet need the market needs that vendors must respond to and fulfill.
References
Cross, R., Gray, P., Cunningham, S., Showers, M., & Thomas, R.. (2010). The Collaborative Organization: How to Make Employee Networks Really Work. MIT Sloan Management Review, 52(1), 83-90.
Jeffrey H. Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.
James Harley. (2011). Collaboration and the use of online collaborative toolsets in the project management environment. International Journal of Managing Projects in Business, 4(2), 345-354.
Huberman, B., & Wilkinson, D.. (2010). Fluctuating Efforts and Interdependencies in Collaborative Work. Group Decision and Negotiation, 19(2), 169-191.
Huner, K., Otto, B., & Osterle, H.. (2011). Collaborative management of business metadata. International Journal of Information Management, 31(4), 366.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M., & Kruschwitz, N.. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52(2), 21-32.
Ramesh, A., DK Banwet, & R. Shankar. (2010). Modeling the barriers of supply chain collaboration. Journal of Modeling in Management, 5(2), 176-193.
Rosenzweig, E..…
