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Demand planning KPIs are a lot like the information shown on your car's dashboard or your GPS. Speed, miles per hour over the speed limit, range to empty, average miles per gallon, and more are often displayed to help you manage the trip. Just as the KPIs on an automobile dashboard allow a driver to adjust and plan for stops, service, and other necessities, KPIs on a demand planner assist in the manufacturing journey.
A demand planning KPI is a signal or alert of a status of a particular metric. Planners and managers have access to the most relevant information driving the demand and supply system by displaying them prominently on a team member's dashboard. Because they are data-based and real-time, demand planning KPIs allow decision-makers to act with confidence to keep the forecast on track.
There are different types and depths of KPIs that are useful depending on the team member's position reviewing them. But there are standards as well, and most of them have mathematical calculations for their formula. While KPIs will vary from industry to industry, there are some basics that everyone should know when forecasting demand and supply. These include:
This is a measure of the accuracy of the forecast against performance. Different expectations will be in place for the accuracy of established products versus new product offerings. But the better the accuracy, the greater the profitability.
Also known as bias, mean forecast error helps planners understand bias within the forecasting model. The key is to look for errors that tend to trend in one direction. Once identified, the model can be tweaked, eliminating the bias and improving forecast quality.
Like all dynamic systems, many variables are normally distributed. Tracking signals help planners know when an error to forecast is not normally distributed. It may be an outlier or an indicator of a sudden change in demand.
Some demand changes can be detected early. If the demand shifts too much, out of stock or overstocking can occur. Adjustments can be made to the supply chain by monitoring these early signals to bring the forecast back in line.
The Pareto Principle states that 20% of all causes impact 80% of outcomes. This is a rough but reliable rule and not strictly a mathematical one. It is, however, an excellent way to manage inputs and outputs to the benefit of the whole. Applied to demand and supply planning, Pareto Analysis can help determine things such as ABC rankings in ABC analysis in times of disruption. Or it may be helpful in forecasting which products to increase capacity for and which to phase out.
Almost all companies track forecast to actual sales. But when it is included in the demand and supply planning system and aided by advanced software, forecast vs. actual sales can help managers act ahead of events and make better decisions when errors do occur.
All these KPIs have strict formulas and mathematical equations behind them. And they can be calculated to produce a forecast. However, just as a forecast can be in error, manual planning can be faulty due to human error. This may be due to the overwhelming volume of data, the time lag in historical data, or unforeseen variables that humans can't detect. The answer to accurate and relevant KPIs is using robust demand and supply software by Plex DemandCaster.
With its agile supply chain planning software, Plex DemandCaster offers real-time data across the variables your company needs to track. Calculations are built-in and are powered with advanced analytics such as ABC analysis, demand sensing, "what-if" scenarios for managing disruptions, and other features that help produce the most accurate forecast available. These KPIs are easily visualized on dashboards that help managers make quick decisions based on information without manual calculations and data entry.