As a CEO who focuses on developing software purpose-built for food and beverage distributors and wholesalers, increasing data leverage is a topic that I frequently discuss with both users and potential users. Given our application focus areas—which include warehouse management, ERP and direct store delivery—we manage a wealth of insightful data within our customer’s environments. The key is to elevate that data into information which is actionable as part of an intuitive workflow within the application UI.
To be actionable, analytics must be designed to monitor specific processes in order to sense and respond to variances in demand, supply or capacity. When the variance is predicted to violate performance thresholds, prescribed actions are taken to avoid projected problems. Here are examples of what you can do with actionable analytics:
- Predict optimal stock levels for perishables to prevent spoilage and out-of-stocks for your van sales
- Predict if the warehouse will achieve today’s shipments based on shipment/warehouse type
- Predict space utilization issues by looking at the incoming vs outgoing items
- Predict if sales trends will move outside of target growth projections.
Alex Ring, VP Product Management of TPM Retail & FS, and I presented actionable analytics at our recent AFS 2016 User Conference. An important point that we discussed, regarding all potential technology, is that you should ensure that the cost of the problem you are solving justifies the investment. In other words, how expensive is the problem that you are trying to solve? Expense in this sense can have many dimensions, but the application of technology should be metered by the expected return on investment. You can start by answering:
- What is the opportunity payback of fixing the problem?
- How expensive is the solution to your problem?
- How do you access your smart data?
Once you have answered these questions, you need to process the data. The challenge with noisy data is it requires you to sift through and find the nuggets that will deliver value. You will want to look for data that is interpretable, relevant and novel. Focus on data that is tied to processes and is easily accessible. I recommend using your business process workflows as the filter on the value of your data.
Next, you need to apply intelligence. There are three types of analytics you can apply to your data:
- Descriptive analytics: Descriptive statistics that summarize the data.
- Predictive analytics: A statistical model that uses existing data to predict data that we don’t have.
- Prescriptive analytics: A deeper analysis that based on the analytical results can offer advice on how to proceed based on possible actions. This level can also lead to scoring as well as gamification, where that makes sense, as part of the future model.
Prescriptive analytics by definition are actionable, enabling us to solve problems before they occur, and can also provide the closed-loop feedback we’ve always wanted in terms of whether the prescribed action actually generated the expected results. We’ll revisit analytics in a future column.