Living Processes

As digital takes over our lives both at home and in office, it opens new opportunities in process improvement and optimization. While the intent is to reduce manual intervention, and automate the decision-making capabilities, there is an imminent need to respond to the customer demands. In the experiential world, the customers have more options to choose from and so the enterprises need to be agile enough to respond accordingly.

The intelligent or the living processes learn on the go, call out exceptions and change behavior based on the customer trends / feedback and changes in guidelines. Now this may seem aspirational, however the current technology landscape is well on its way to solving some of the complex customer situations.

Take the example of a banking process where depending on the customer profile and credit history, a loan is approved. A typical bank would design the process for a Sunny day scenario while the exceptions are handled on a case to case basis. However, if the daily operations touch a point where the exceptions are a bigger percentage than the vanilla scenario, then the process needs to learn and adapt to address the situation. There are two possibilities here

A)     Exceptions are high and are caused by an unforeseen cause

B)     The exceptions are adhoc and do not follow a trend

Traditional problem solving techniques help in the first scenario when addressing the root cause can help eliminate the exceptions.

If the exceptions are adhoc, the bank manager comes up with a remediation mechanism or “band-aid” fix to solve these as they arise. This leads to other issues related to data integrity, compliance, transparency and people morale. This is not a sustainable mechanism and needs to be addressed on priority. The need is to define an agile and “self-healing” process which learns on the go, looks for hidden trends and translates multiple transactions. These are studied to identify the guidelines or policies that need to be modified to reduce the exceptions and thus evolve the process.

AI and Neural network based algorithms are helping businesses address such situations. Based on customer profile and segmentation, contextual inferred decisions feed neural networks real time to help in faster credit processing or evaluating transactions (as in an anti-money laundering process). Feeds from social media, internet transactions feed into the live profiling of customers and continuously tweak processes for a dynamic analysis.

In the world of supply chain, next best actions are recommended based on contextual business rules which are updated and refined through inputs from IoT, Point of purchase feeds, weather forecasts per region etc… Thus demand forecast is done through a universe of living processes which continuously self-heal and refine themselves and recommend next steps such as last mile printing, partner collaboration to service a particular stocking unit etc… The response to each scenario as it surfaces is a well considered decision facilitated by complex algorithms which optimize the cost to serve and customer satisfaction in the supply chain. Thus the processes are living and continuously adapting to balance the multiple objectives in the supply chain.

Living processes as the word goes by need to be nourished and watched closely for the best returns to the enterprise. The algorithms which drive key decisions must be tuned and optimized for value across the end to end process. The process culture and mindset of the organization is a key in this transformation.