Artificial Intelligence - The Common Strategic Thread

All networks, social, business, integration and collaboration, news and extended supply chain networks are all related through a common thread, data and more specifically - how is the data used to drive decisions and execution strategies? The glue then that combines the data in a manner that can be used to sense and respond to the waves of disruption in the market.

I believe that the constant running through all aspects of the market place is the discontinuous disruption that is driven by consumer embrace of technology combined with the market reactions to the consumer demands. Marketplace partners must recognize this new reality and work to incorporate methods to obtain the data necessary to feed the decision making process. The value and the accuracy is based upon the amount and types of data that are incorporated and available to the process. This precept itself drives the need and the value of collaborative analysis and data collection across the partners.

This model is especially important to the retail marketplace where the market is continuously buffeted by changing demands from consumers that are themselves driven by changing technical capabilities. These reactions and impact from technology has been a fact of life for quite some time within the extended supply chain and the pressure of reaction to disruption combined with the demands of cost containment and reductions are driving collaborative partnerships in the extended supply chain. These partnerships along with the resulting data collaboration can be used as a model for the marketplace as a whole. This is a key reason for the importance placed on the supply chain by Amazon and others would benefit greatly by embracing this practice.

In addition to the importance of collaborative data, the marketplace partners must also take into account the level of accuracy changes that occur during the life cycle of the analysis. The accuracy of the decision goes through a measurable life cycle:

• Infancy starts with sensing the potential reactions to the demands. This is an unclear time where many options can be taken in reaction to the data and the analysis. This is the beginning of experimentation to test the options.
• Mid-term or decision childhood where the options are narrowed based on additional data analysis based on the results of experimentation from the infancy stage. This is the refinement of decisions and strategy based on the results of experimentation. This is marked by refinement and addition of experiments to support analysis refinement.
• Maturity or decision adulthood where the strategy is fully formed and executed based on the refinement resulting from the data analysis. This is also where the next disrupting concept begins to form and requires the continued analysis of results from the strategy to form the new concept and strategy.
• End of life of old age where the positive results of the strategy decline and the consumer and market are driving the new disruption. This is end of the strategy value and it is equally important to be able to sense the decline of the strategy as it is to identify the beginnings of a new disrupting factor.

You can see that the process I described above follows the PDCA, continuous improvement process, which is a standard practice of the supply chain. This provides a solid framework to sense and respond to the disrupting factors and has been incorporated across a wide range of marketplace practices. The disrupting factor that drives improvements to this process is the collaborative data collection and analysis that allows the process to sense potential changes earlier and then through AI analysis provides the guidance for experimentation and validation to formulate the change to react to the disruption.