This dynamic is more than an experience effect or network effect, as it is multiplicative across the many members of the supplier network, in effect creating an entirely new platform for sharing knowledge and information. The reliance on analytics for creating the necessary integration links and platforms for decision making also dominate this phase of maturity in any supply chain network (Wang, Huang, Dismukes, 2004).
With the findings that what gets measured dictates the culture of an organization as much as how information is shared across the network, the development of an effective DDSN framework can begin. The initial steps of creating a common set of key performance indicators (KPIs) and metrics first needs to be completed, and the integration points across each area also defined. Third, there is the need for defining the idealized state of metrics performance followed by a best practices benchmark for comparable supply chain networks. With all of these factors in place, a supply chain can be effectively measured and compared relative to peers, so that a relative level of performance maturity can be assessed (Schlegel, Murray, 2010). With these factors in place a DDSN will over time transform itself into a knowledge sharing network provided that the key goals and objectives of the supplier chain itself continue to be accomplished (Dyer, Nobeoka, 2000). The ability to generate knowledge over time based on supply chain performance can then be achieved.
References
Barrett, J.. (2007, November). Demand-Driven is an Operational Strategy. Industrial Management, 49(6), 14-19,5.
Link: http://www.allbusiness.com/company-activities-management/operations-supply/8923983-1.html
Based on the research completed at Gartner on the performance of DDSNs relative to traditional supply chains, this analysis illustrates the value of using metrics to streamline the S&OP process while increasing knowledge sharing across a supply network. It also shows how using a demand-based signal (a forecast) the behavior and focus of a...
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.
Link: http://mit.dspace.org/bitstream/handle/1721.1/1441/147b.pdf?sequence=1
Study shows the value of having a highly coordinated and synchronized supply chain that is capable of transforming knowledge into a long-term competitive advantage. This study covers over a year of research and illustrates how cross supplier collaboration -- pretty much forbidden in the U.S. auto industry -- has helped reduce costs and increase new product introduction speed in the Japanese auto industry.
Schlegel, G., & Murray, P.. (2010). Next Generation of S&OP: Scenario Planning with Predictive Analytics & Digital Modeling. The Journal of Business Forecasting, 29(3), 20-23,28-30.
Link: http://shertrack.com/PDF/JBF-Fall%202010%20-%20Nxt%20Gen%20SOP.pdf
Defines the use of metrics and KPIs to better manage the Sales & Operations Planning (S&OP) process, one of the most complex on the sell-side. This is also an overview of how metrics and KPIs make an organization more aligned to real-
time supply chain goals as well.
Ge Wang, Samuel H. Huang, & John P. Dismukes. (2004). Product-driven supply chain selection using integrated mulit-criteria decision-making methodology. International Journal of Production Economics, 91(1), 1-15.
Link: http://bit.ly/qhHVXb
Defines a series of frameworks for integrating multi-attribute product and pricing data into supply chain performance and measures of profitability, using a series of longitudinal studies across several industries. This study also looked at how product-driven supply chains are more effective when analytics and KPIs are used for managing them to financial performance and increasing supplier coordination and synchronization.
