How To Design Your Supply Chain
Leveraging The Power of Analytics-Based Segmentation
It’s safe to say that the expectations of today’s consumers are vastly different from the expectations of consumers just a few years ago. Part of that is driven by the post-Amazon world, and another part of it is driven by the emergence of the next generation of consumers – Millennials. They’re coming of age in the post-Amazon world, where they want their orders faster – they want personalized products and services, and they want a variety of products to choose from. Not to mention, they expect the products they seek to be available when they’re ready to place an order.
Knowing all this, it begs the question: is your supply chain up to the challenge?
Today’s Supply Chains
The fact of the matter is that today’s supply chains are designed to handle high volumes of orders and execute with efficiency. Most businesses have swiftly adapted their supply chains to provide a one size fits all solution where volume driven business can meet the demand of today’s consumers.
Although, it’s not all good. Supply chain models are growing redundant and repetitive, and this is negatively impacting customer satisfaction.
This is precisely where analytics-based segmentation comes into play. This type of segmentation can help to optimize operations for businesses with high volume orders. This approach makes it possible for businesses to tailor their supply chain in order to fulfil unique demand profiles while overcoming service and cost tradeoffs. How do they do it?
They group everything into segments and then define new supply chains that specifically cater to these segments. Once new segments are defined, they’re aligned to a business strategy based on their associated risk. For each profile, businesses define operational performance requirements for responsiveness, efficiency, and everything in between.
Essentially, these operational strategies make it easier to define the push-pull boundary of the various supply chains a business might implement. Ultimately this allows them to meet multiple segments on their own accord, virtually eliminating the one size fits all mentality.
For example, a more responsive segment would demand a pull strategy with delayed customization, while a low-price strategy would demand more of a push strategy with increased inventory and stocking of products.
Today, an ML-based clustering algorithm can evaluate numerous customer, market, and product attributes to develop a far more accurate set of segments that a business can use to build their supply chains around. Technology is here to stay, and if it can make it easier to implement an analytics-based segmentation approach to supply chain management, then every business should be taking advantage of it.
Segmentation can help to identify appropriate business segments and their associated value drivers, ultimately improving supply chain efficiency and driving higher customer satisfaction.