Optimal processes are the foundation of digital transformation.

Digitization happens around a process, and if the processes in place are not optimal, realizing the actual value of digitization of your supply chain will be challenging. What is the most effective strategy to implement digital is an extensive discussion topic. You will find many white papers and videos on the best approaches.

There is no standard cookie-cutter approach to digital transformation in the supply chain. That topic is also not the focus of this article. Still, the gist is that one of the most critical aspects of the digital transformation approach is that process transformation needs to happen before technology comes into play. And AI-enabled transformations are no different.

In supply chains, segmentation plays a crucial role in process transformation. To better understand that, we must understand what supply chain segmentation means. Chopra et al. define supply chain segmentation as follows:

Segmentation stratifies the end-to-end supply chain into logical groups based on the various characteristics of the product, customer, supplier, distribution channel, etc.

These groupings are used to optimize demand and supply planning and scheduling by providing differentiated services for each group. For example, it can be used to group customers with similar fulfillment needs and then develop different supply chains to meet them. As evident from the definition above, it leads to multiple supply chains within a supply chain, each optimized for a particular segment.  

And within these supply chains, you will redesign business processes aligned precisely for the segment the supply chain serves. Any digital implementations must occur only after these multiple supply chains have been implemented.

Examples of AI-enabled transformation after supply chain segmentation

Example 1: Food and beverages

I was reading a case study regarding a food and beverage company that completely digitized its supply chain in developing freestyle machines. 

Freestyle presents more than 100 different beverage options, with all of the company’s drink products available to the consumer from a single source at the point of sale. The machines are managed and replenished with raw materials in an entirely automated manner.

You can imagine the role machine learning algorithms are (or can) play in this dedicated supply chain within the supply chain. But the company would have segmented its supply chain to determine precisely what type of algorithms are needed, where, and how they interface with broader algorithms that help plan the end-to-end supply chain.

Only based on a segmentation exercise, the company would have realized that a segment of its customers needed a more personalized experience. The fulfillment processes would have been then designed exclusively for this segment and THEN Digital technology, including AI tools, were implemented to deliver a customized experience.  

Example 2: A pharmaceutical company

Another case study I recently read was about a leading pharma company that determined through segmentation that it could leverage efficient and responsive supply chains within the same supply chain for mail-order prescription fulfillment.

Then, for the segments and Supply Chains where rapid fulfillment and visibility were critical, it leveraged best-in-class digital technologies to provide visibility, interface, and planning. It even created separate warehousing processes for these different supply chains, where the pick, pack, and dispatch processes for the responsive supply chain were highly automated.

As you can imagine, before the technology plan came into play, the processes, process integration and flow for this specific supply chain segment would have been finalized. Only that will ensure that any AI algorithms (I see multiple opportunities to leverage AI algorithms in this scenario) implemented will yield value.

Example 3: A leading Fashion retailer

A leading fashion retailer segmented its different brands and developed separate supply chains for each. For the brand where the end customer was seeking more personalization and new varieties and was more tech-savvy, a complete digital experience was provided. On the marketing side, it allowed them to personalize certain aspects of the apparel, whereas, on the supply chain side, digital enablement allowed for rapid customization and fulfillment.

Now you can envision the machine learning algorithms that need to be leveraged in this context. But you can also imagine that several processes must be designed and re-designed for this segment. Unless they are in place, there is no way to think optimally about which algorithms will be the best fit.

Technology, like AI and ML algorithms, plays a crucial role in digital transformation, but leveraging technology on top of a robust, digitally aligned process is the key to successful change.