Is it possible to see into the future? Can organizations predict events before they happen? Statistical forecasting isn’t a fortune teller, but it can make accurate predictions about a company’s supply chain over time. The result? Organizations can reduce risk and identify problems before they get worse. Following is a quick guide to statistical forecasting, and how it can revolutionize the way companies do business.
About statistical forecasting
Statistical forecasting is a way to predict the future based on data from the past. By analyzing previous trends in customer behavior, sales, stock control patterns, and workflows, statistical forecasting software anticipates the future of a company over a period of time. These programs collate data from various sources to make assumptions about future sales and revenue, customer engagement, and inventory. In short, statistical forecasting is a crucial component of any supply chain management strategy.
People use this type of forecasting in almost every industry; this method can predict GDP, foretell market movements and housing crashes, and even forecast sports results. In a supply chain management context, it delivers real, actionable results that help businesses grow.
Say that a small footwear company wants to anticipate sales growth during the holiday season. Statistical forecasting software provides the company with real-time insights based on previous holiday sales, customer trends, and anticipated demand for their products. The company can finetune their marketing spend and optimize inventory control based on their statistical forecasts.
“When a company uses statistical sales forecasting techniques, it uses its historical sales or demand data to try to predict future sales,” says the Houston Chronicle. “Because of the complex mathematical formulas used to create the forecast, most companies rely on advanced software to accomplish this task.”
Choosing the right software
For statistical forecasting to work, software must be able to accumulate information from various sources, for example:
- Sales records
- Customer relationship management systems
- Enterprise resource planning systems
- Inventory management systems
Once the software has all of this information, it can create statistical correlations and make predictions about the future.
Not all statistical forecasting software is the same, however. Organizations should choose a program that aligns with their user base requirements. The user interface should be intuitive and have a positive initial reaction from end users. It must integrate with the prior mentioned data sources, provide online end-user support and help, and use various forecasting methods that facilitate demand planning.
The best forecasting and planning software uses mathematical models and algorithms to make supply chain predictions. However, users must know how to interpret this data for effective results. This is where the best of the best shine; they provide end users with an easy to understand interface and, where applicable, prescriptive analytics making recommendations based on business rules.
Organizations have created more data in the previous two years than in the entire history of mankind. Most of this data is useless for supply chain management. The best statistical forecasting programs, however, source the best data and use key metrics to make predictions.
Look for software that combines the two main statistical forecasting methods: time series forecasting and model-based forecasting. Using both of these methods expedites demand planning, resource allocation, inventory management, production planning, and more.
Conventional data analysis is a thing of the past. The most successful organizations use statistical forecasts to predict the future based on historical events. The forecasting programs provide organizations that want to increase sales and optimize their supply chain with a viable long-term solution.