Enabling S&OP through Advanced Analytics

Transformation is coming to your supply chain and according to most industry research, most CEOs and executives believe their supply chain will be substantially or unrecognizably transformed by 2020. That is less than two years from now. There is no doubt that most companies could benefit from a quicker and smarter supply chain that automates the routine, fully engages around event-based opportunities and actively mitigates the risk of disruptions. However, how do we get to this transformed state from where we are today?

At the Informs Business Analytics Conference in Las Vegas last year, I spoke about the potential to enable S&OP / IBP through advanced analytics. I firmly believe that one viable path to more automated and intelligent supply chain operations is through moving up the analytics maturity ladder. There should be no mystery to analytics. Analytics is simply the discipline that applies logic and mathematics to data to provide insights for making better decisions. Gartner has defined five levels of analytics that have become the industry standard:

  1. Descriptive – Determine what happened through reports, key performance indicators, etc.
  2. Diagnostic – Figure out why it happened through root cause analysis, the application of 6-Sigma type tools, etc.
  3. Predictive – Determine what will happen from forecasts, what-if scenarios, simulations, etc.
  4. Prescriptive – Decide what should be done through algorithmic optimization. Optimization programs could provide advice to support human decisions or could be allowed to automatically take action.
  5. Cognitive – Derive new insights or recommended actions through artificial intelligence and machine learning.

Difficulty (and maturity) increases as you move from Descriptive to more advanced levels of analytics but so does the value gained. While it is easy to visualize that higher levels of analytics (Prescriptive, Cognitive) will enable automated and smart supply chain operations; the question still stands, “how do we get there from here?”

Most companies have a mid-term, tactical planning process (SIOP, S&OP, IBP, etc.) that attempts to look into the future to balance supply and demand and meet financial objectives. It seems logical then to build on this planning process through the addition of advanced analytics. In fact, in a 2017 study by Ernst & Young and Forbes Insights, Data & Analytics: High Stakes, High Rewards, 57% of respondents said that they leverage analytics in their sales & operations planning processes. As supply chains become more connected and collaborative advanced analytics will be more common in planning and synchronizing the supply chain.

Below are a few examples of how advanced analytics could be used to enhance your tactical planning process and move your company towards a more automated and intelligent supply chain operation.

1) Analysis through Supply Chain Network Simulation

Simulating the supply chain can often provide unique insights. From a display of the full, end-to-end supply chain or just a segment, you can derive critical information needed to conduct robust analysis. A network simulator can actively display demand projections, facility and lane capacities, and other user-defined information for a selected time horizon. Additionally, network simulations can visually highlight areas to quickly draw the planner’s attention to opportunities and risks, and provide drill down capabilities for root cause analysis.

2) Analysis through Segmentation

Numerical and visual segmentation is a powerful application of diagnostic analytics that can provide powerful insights into the operation of your supply chain. For example, a visual segmentation by product group, revenue and margin, can help determine which product groups represent your top/bottom volume or margin producers. This type of analysis provides insight for product rationalization, price adjustment, and sales and marketing initiatives.

3) Analysis through Heat Maps

Heat map displays is another form of diagnostic analytics that enable rich analysis of complex data. At a glance, you are able to discern areas of concern based on color, size or position of a specific grouping of information. For example, a heat map displaying total revenue (represented by the size of the box) and forecast accuracy (represented by the color of the box) by sales region will quickly identify which sales regions that might warrant additional analysis such as those with high volume and low forecast accuracy. The next logical step would be to conduct a root cause analysis to determine exactly what product groups and specific products are causing the high forecast error situation.

4) Analysis through What-If Scenarios

One of the most powerful advance analytic techniques is the use of multiple side-by-side “what-if” scenarios to determine an appropriate response to a planned or unplanned supply chain event. To be effective, these scenarios must be easy to set up, intuitive and simple to run and modify.

For example, a “what-if” scenario could be set up to look at an upcoming promotional event to analyze various price / lift combinations and their effects on promotion profitability. Each scenario to be compared would include a baseline forecast, a projected promotional lift, critical resource capacities, and the corresponding revenue and margin produced.

A number of insights are quickly visible by displaying multiple scenarios side-by-side that contain both financial and volumetric information. One would expect an increase in revenue with a corresponding increase in promotional volume. However, is there enough manufacturing and distribution capacity to handle the increase in volume? Can we obtain enough raw materials to support the promotion? Which promotion scenario produces the best margin? Any number of questions can be answered using multiple “what-if” scenarios depending on the information displayed.

5) Analysis through Algorithmic Optimization

Algorithmic optimization enables some of the most advanced examples of predictive and prescriptive analytics today. Systems provide recommended actions and, if desired, the system can be enabled to automate the optimal decision. Examples of algorithmic optimization used in the supply chain include:

  • Inventory Optimization
  • Advanced Production & Scheduling
  • Supply Optimization
  • Transportation Load and Route Optimization
  • Optimized Selection and Application of Demand Planning Algorithms

These are just five examples of how advanced analytics can be used to enable a more intelligent planning process and transform your supply chain in to a more automated and intelligent operation.

What analytical tools is your company using to help transform your supply chain? In what other areas of the supply chain can advanced analytical tools be used to enhance capabilities?

Related Content:

White Paper: Three Checklists to Build a Successful Supply Chain Analytics Foundation

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