Editor’s Update: Interesting that an article in 2013 talking about “integrating social and enterprise process” is thought to be covering new ground. I am of course talking about today’s Forbes post by Rawn Shah titled “SAP Realizes Enterprise Social Processes.”
Back in 2008 I wrote an article on my government funded research in the late 90s titled “Optimization Modeling and the Modern Supply Chain,” in which I spoke about the relational synchronization between stakeholders under an agent-based model (http://wp.me/p4HrB-1m).
I was not alone in my assessment of the future landscape as reflected in the findings of a 2000 SIIA white paper titled “Strategic Backgrounder: Software as a Service.”
Besides the fact that their solutions were based on a linear, equation-based model, the main challenge for traditional enterprise application players such as SAP and Oracle in terms accepting this integrative transformation was directly tied to the structure of their revenue models, which involved a significant up-front investment of capital coupled with onerous monthly maintenance fees. It is only when this old model became untenable that they (well SAP anyway) could publicly embraced the new frontier of Enterprise Social Process to which Shah’s article refers.
It is also one of the reasons why one could make the assertion that SAP is no longer an ERP Company. (http://wp.me/p4HrB-3zL) The same however cannot be said for Oracle. But this is a story for another day.
Check out the link to access Shah’s article in its entirety. In the meantime, have a read (or re-read) of my 2008 post below. Your comments as always are welcome.
Have you been able to utilize simulation or optimization modeling to solve a demand-side supply chain problem? If so, what was it?
There have been numerous studies and reports on the various methods (i.e. Monte Carlo) used in determining supply chain optimization.
I would have to say that my preferred method has been to use the heuristic approach under an agent-based model in which the unique operating attributes of each stakeholder is understood separately before a collective outcome is identified and achieved.
It is this latter “twist” if you can call it that, that has enabled the optimization process to extend beyond the limitations of executional boundaries referred to in a May 2007 article that appeared in Supply Chain Digest titled Supply Chain Optimization versus Simulation. Specifically the author’s assertion that “Mathematical” optimality is not used, and is not required or likely even feasible. Optimization almost always takes at least some minutes to process (and in some cases hours), and hence isn’t generally usable in an execution environment.”
With partial funding from the Government of Canada’s Scientific Research and Experimental Development (SR&ED) Program, I developed a theory that I refer to as “strand commonality” in which disparate and seemingly unrelated data streams can be linked through the use of advanced algorithms to produce a “positive or beneficial” collective outcome.
It is really quite fascinating in that production models have consistently produced the correct results in terms of real-world applicability approximately 98.2% of the time.
It gets really interesting when you introduce multiple tier factors that include intangible elements such as the value of technician certification versus x number of years of practical experience.
The key starting point is to recognize that the term supply chain is a misnomer in that it implies a sequential order of events (in the spirit of your question, a non-deterministic set of algorithms which aligns with the equation-based modeling used by most software vendors).
In reality however we operate in a world in which synchronization between diverse (and now global) stakeholders have to exchange and quantify disparate data on a real-time basis, and therefore the term supply practice would be a more appropriate description.
Once again, when you recognize this elemental difference you will then take the first steps towards building an effective and meaningful optimization model.
I hope this helps. In the meantime I have included a link to the 2007 Supply Chain Digest article.