Predicting demand in 2021 has become more difficult than ever. It is vital that retail and manufacturing professionals take a data-driven approach to demand planning. GSC provides predictive analytics for enterprise - including demand forecasting and supply chain risk management - that are tailored to your specific needs.
Here are some helpful tips on how you can use predictive analytics in your business.
Old forecasting methods no longer work
Retailers and manufacturers used to rely on using historical sales patterns to predict future demand. However, shifting consumer habits make that analysis obsolete.
One example of this changing trend is the switch to online shopping. This will continue to impact the sales of consumer goods companies, regardless if they operate a brick and mortar or online shop.
What is predictive analytics ?
Given the shifting context, retailers and manufacturers increasingly resort to advanced analytics to predict demand volume and timing. Predictive analytics uses a combination of historical sales, promotional data, post-pandemic indicators, and AI to predict inventory requirements .
The data will be fed into advanced analytics systems that are programmed with human knowledge. The AI can then analyze various factors in order to predict short- or mid-term sales trends.
Demand prediction requires new data sources to be accurate
Retailers and manufacturers that can harness the power of predictive analytics with post-pandemic consumer data will be in a much better position than those who rely on previous forecasting methods.
- E-Commerce browsing data and Google search trends Consumer activity indexes
- Credit Card spending data
- Holidays and Events calendar data, such as Valentines day
- Macroeconomic data, such as disposable income
- Covid news and government restrictions (For countries with restrictive measures in place)
Those data sources can be combined with previous sales and promotional data to understand real-time consumer trends. Data sources play a key role in determining demand, but they are not enough on their own. Data sources need to be processed using accurate and versatile AI models in order to give each feature the appropriate weighting.
Data Models
Describing the AI algorithms that can be used to forecast demand is beyond the scope of this article. A brief overview of some predictive models that can be applied with above data sources are, however, provided below.
In general, it is preferable to select Machine Learning models that can be trained on both recent and variety of historical data. These models should be able to accurately produce tactical forecasts, from few weeks to 3 months on the horizon.
- LSTM: can help to automatically retain important information and discard the rest
- Ensemble Models: will help you take advantage of many models at once
- Bayesian hierarchical models: will help choose among various ambiguous or conflicting data features
Conclusion
Securing sufficient sales while also avoiding the risk of overstocking or understocking is a difficult balance to strike. And in these unstable times, demand planning has never been more difficult. Fortunately for retailers and manufacturers alike, there are ways to predict demand more accurately through data-driven thinking.
GSC offers demand forecasting services that can help you predict demand for your consumer products or services. We use cutting-edge AI solutions to help supply chain professionals in you post-COVID inventory management and demand planning.
