There is a concept that applying data science optimization to your business will automate processes, consolidate information faster, make employees lives easier, and allow them to go home earlier. All of that is true! It does leave an important part out, however. What often doesn’t get mentioned is that data science comprehensively considers so many nuances of your business, all at once, that understanding its optimized scenarios will require significant hours of training, as well as a mindset change within your planning team.
This can be hard to conceptualize for your entire supply chain, so let’s take a deeper look at what this looks like if you use data science for a revenue management approach. Here it’s important to integrate three pillars into every decision taking: supply chain, pricing, and financial indicators. Summarizing, we need to answer: Can we produce it? Will the market absorb this amount of volume at this price? Will we make more money doing it?
With this approach, operational research is a qualified technique to represent this problem in a mathematical form. Maximizing a financial indicator, chasing the best sales allocations, regionally and per channel, and fitting it into your factories’ bottle necks and logistics constraints has been a winning formula for big companies.
Explaining to the sales team that we will not make a discount campaign next month for our commodity brand because our manufacturing line X is already operating fully into 3 shifts can be challenging. In this same line of thinking, telling the industrial team that we will go with this more complicated mix of production plan because we are foreseeing relevant sales opportunities that will impact our bottom line will be equally complex.
Adding to this required change management, being able to explain each and every output the operational research model is delivering will require its own effort. Why are we saying the optimal strategy is B, if it is different than what we normally do with strategy A? To give the proper explanations for change will require knowing your supply chain end to end, a full data mapping of your business, and almost an over-use of analytics and intelligence functionalities to dynamically interpret different faces of the cube all at once.
Building a data science tool that does a good interpretation of your business complexities will be challenging (we’re done the hard work for you already with our scAIcloud® platform). Being able to explain “why” for each output this tool is delivering will be even more challenging. And making your entire team understand that we will now have different triggers for decision making will require a lot of energy and time spent on change management, from the leaders’ mindset to a possible tweak in your objectives and key results goals from each team.
Summing this up, the journey of applying data science to your business won´t be easy, but it has a big upside, highlighting significant amounts of profit increase opportunities, that makes this a journey worth taking.