The Path to building Analytics capabilities
Understanding the linkage through an example: Developing supply chain analytics capabilities for a manufacturing company
Let us walk through an example to help us understand how analytics strategies should be ideally formulated. Following the journey that starts with a business strategy and goes all the way to defining required analytical capabilities, will provide you with insights that you can use to determine if any of the fancy advanced analytics tools that you are being sold are useful and will help propel your company’s strategy.
Our hypothetical company
Let us assume that we are in charge of formulating strategy for a company that manufactures LCD TVs and displays. We have already leveraged some of the levers like low cost country sourcing and manufacturing etc. but so have all our competitors. We are now brainstorming to develop a strategy path to differentiate our brand in a crowded marketplace and have formulated certain business strategies.
One of our business strategies is to offer a product that has the same quality as the competition but is cheaper than the competition. To summarize it in a sentence- “Offer lower prices with high quality”.
Supply chain strategy
Now leveraging the business strategy defined above, we define a supply chain strategy. In the illustration below, the supply chain strategy has been layered right below business strategy since it needs to support business strategy.
At a high level, formulating supply chain strategy is not very difficult-to be an efficient manufacturing and distribution organization, you need to develop a supply chain that minimizes cost/waste, across all sub domains of supply chain.
Defining components of supply chain strategy
But then, there are more granular aspects to supply chain strategy. Based on MIT Center of Logistics and Transportation’s proposed Strategy operations continuum, a supply chain strategy has three high level components, that add some structure and pathway to the supply chain strategy. Let us review and define those for this specific example:
Principles: General objectives for the supply chain
Imperatives: Specific objectives for the supply chain
Choices: Specific decisions made to support specific objectives
What these mean will become clear from examples in the illustration below, when we split the high level supply chain strategy mentioned above into these three supply chain strategy components.
Principles: Minimize manufacturing cost
Imperative: Increase capacity utilization-which means you produce more leveraging the same assets.
Choices: You can achieve the imperative above-increase capacity utilization by leveraging few things. For simplicity sake, we say we can increase utilization by reducing manufacturing cycle time.
For each principle, there can be multiple imperatives and choices but for the sake of simplicity, we will use one example of imperatives and choices.
Formulating operating practices
Now that you have a granular supply chain strategy defined, you can hypothesize that each of the supply chain policies/choices impact your operating practices. For example, to reduce fleet size, you need to optimize your routes and transportation assets. Again, each choice can have many operating practices, but to keep the example simple, we will use just one practice for each of supply chain strategy choices.
Determining analytics enablers
Now that you know what operating practices you need, we can now focus on defining what type of analytics is needed.
Force fitting cookie cutter solutions without understanding how it relates to an organization’s overall objectives will lead to initiatives that “also ran” for few years and then eventually faded away.
So if you want to optimize manufacturing flows, you want to leverage a simulation tool, at the very basic, to simulate your manufacturing flows and run simulation scenarios to determine what the optimal path will be. Then eventually, in the long term you want to have a capability called digital twin-a smart factory scenario where you can allow a digital “twin” of your manufacturing processes run and optimize manufacturing.
Trying to leverage analytics just because everyone is trying to do it will never give you any competitive edge. To use analytics as a true competitive differentiator, you need to think of it as an enabler, not a strategy in itself. When linked properly with strategic objectives, you will be able to leverage it as a true differentiator.