by Kumar Singh, Research Director, Automation & Analytics, Supply Chain Management, SAPinsider
Common business sense dictates that no two organizations will be the same in terms of organizational structure, culture, current capabilities etc. so any strategic initiative that may work for one might not work for another. Yet, when we embark on strategic digital transformation initiatives, even before we look inside our processes and systems, we start asking what others are doing. While some generic best in class approaches can surely be adopted across companies and across industries, following the path of “one size fits all” for strategic initiatives, like developing an strategy around where and how to leverage AI capabilities in your business, is never a recommended path. In my perspective, every organization has a unique DNA. And hence any AI Strategy formulated needs to account for the unique challenges associated with THAT specific organization. In the subsequent sections, we will review a framework that can be used to evaluate if your organization has the “ingredients” for a successful AI strategy.
Ingredients to create a Successful AI Strategy
So what are some of the circumstances/ingredients that need to exist for a successful AI Strategy ? There are many school of thoughts on this but as per the framework I follow, they are the following :
- A strategic dilemma or trade-off must exist
- The nature of the problem is driven by uncertainity
- An AI algorithm will be able to reduce uncertainity enough to change the balance in the strategic dilemma.
A Real world example : Optimal Inventory placement
I plan to use example of an AI enabled supply chain solution built by an e-commerce company, a real world example, to illustrate the three ingredients mentioned above. The company in this example is Otto, a German e-commerce venture. Now let us review the three ingredients in this example, by walking through them one at a time. As you walk through them, you will see that these are sequential. Presence of one should prompt you to explore the next one.
Ingredient 1: A strategic Dilemma or trade-off must exist
Otto was experiencing delayed deliveries to the customers. Whenever there was a spike in delayed deliveries, it was also accompanied by a significant increase in returns. Impatient consumers of modern age would buy that item in the store and return the product from Otto whenever they received it. Even when Otto had sales, returns added to its costs. Otto obviously could not have stocked everything at every distribution center to avoid delayed deliveries. So their dilemma was –
What Inventory do we need to hold and where
Ingredient 2: The nature of the problem is driven by uncertainity
No matter how much sophistication you build in your algorithms, some level of uncertainity in demand is inherent in e-commerce demand. And every supply chain manager is aware of the impact of demand uncertainity on inventory. In Otto’s case as well, the uncertain nature of customer demands is what was leading to Inventory stock outs and delays. This step led to the exploration of the third ingredient- can this uncertainity be reduced ?
Ingredient 3: An AI algorithm will be able to reduce uncertainity enough to change the balance in the strategic dilemma
This ingredient is important because despite the promises being made, not all business propblems can be solved by AI and for many, the investment in developing an AI solution may not be worth the investment. Experimentation is the key and that is what Otto did. Leveraging a database of 3 billion past transactions and hundreds of other variables, Otto was able to create a prediction algorithm that can now predict with 90 percent accuracy what products it will sell within a month. This high level of prediction accuracy allows Otto to setup a new way of organizing logistics, Inventory, new warehouse locations, local shipping and customer delivery guarantees.
What does this mean for SAPinsiders
- The example and the ingredients mentioned here are high level, to indicate that a structured and more thoughtful approach is needed. It is more prudent to take few months to do an extensive exercise like this for “dilemmas” across your operations/organization, run pilots and finalize the AI capabilities you need to develop.
- Only when you have finalized a list of AI capabilities that you can decide what type of analytical org structure you want to develop, what types of roles you need and what should be the desired skill sets for those roles. There is an approach for that as well that we will cover in a separate article.
- Customize and Create….don’t just replicate !
: Ingredients framework adapted from Gans and Goldfarb 2018
Kumar Singh, Research Director, Automation & Analytics, can be reached at firstname.lastname@example.org