Role of Analytics in Supply Chain Planning
In the responses for SAPinsider research, Supply Chain Planning in The Cloud, advanced analytics emerged as a key technical requirement as well as a key technology . Lack of relevant and timely data and insights was identified as a major pain point with current planning solutions portfolio as well .
Analytics plays a key role across the end-to-end supply chain planning process and can do so in non-technology aspects like collaboration and building trust. Network-wide initiatives like Collaborative Planning & Forecasting (CPFR) where every player in the supply chain leverages data to forge collaboration and trust is an example. Obviously, analytics forms a critical component of the core planning processes themselves. In that context, a key aspect that many SAPinsiders highlighted is that advanced analytics capability should not be viewed necessarily as a standalone technology capability. Any form of analytics, including advanced analytics methodologies, are essentially extensions of other capabilities, like supply chain planning technologies to create enablers. While even simple KPIs and metrics can be characterized as analytics, advanced planning incorporates almost all the three key forms of analytics- descriptive, prescriptive, and predictive. Devoid of analytics capabilities, these tools will not exist.
Building stepwise analytics capabilities
If you executed your supply chain visibility data strategy correctly, and integrate that effectively with your quest to build a single-source-of-truth, there are some advanced analytics methodologies that you can leverage on that data. An example of stepwise journey is shown in figure 1. Some more specific examples are reinforcement learning algorithms for inventory management and deep learning for logistics planning. But if you are not firmly established in the game, the best strategy is to start with elementary analytics methodologies, like descriptive and diagnostic analytics. These foundational analytics approaches are reactive but still important. It is critical that you master these before moving on to more advanced methods. Also, even when you have matured to more advanced methods, a true analytics portfolio leverages all these types of analytics in tandem. Predictive and prescriptive analytics methodologies can be labeled more advanced forms of analytics and are more proactive. As compared to descriptive and diagnostic methods, which help answers questions like "What happened", these mthods can help answer questions like "What can happen" or "What can or should be done".
This stepwise approach helps you build a gradual culture of analytics, redefine processes based on learning, and help establish a foundation for advanced approaches, like prescriptive and predictive analytics and eventually AI- and ML-based algorithms. Figure 1 below illustrates this stepwise journey.
Figure 1: Stepwise analytics maturity ladder
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