A Look at the Future of Demand Planning and Management

As companies across the ranges of business and industry have responded to the impacts of COVID-19, an unprecedented event that has had a substantial impact on every link in the supply chain as well as consumer behaviors, one thing has become abundantly clear. There is an indisputable need to transform the supply chain from end to end, and that means moving past the traditional demand planning and forecasting approach.

Solutions now need to incorporate a more integrated approach across the spectrum to maximize each company’s ability to respond with agility and resilience, focusing on flexible problem-solving in order to achieve success and peak performance. 

Traditional demand planning and management is not enough anymore

While traditional demand planning utilized historic performance as the primary model for forecasting, the impacts of the pandemic have made that method less than useful. Year-over-year and quarter-over-quarter data is no longer an effective indicator for use as the sole forecasting method. You need that valuable data, yes. However, simple data analysis just doesn't cut it anymore.

Traditional demand planning needs to be modified to have a place in the rapidly changing world of today, and that has left many businesses that rely on this method in the lurch with little idea of a path forward to guide them in critical decision-making. 

Thanks to tech advances in collaboration, data-gathering, analytics, and process management, forecast inputs have a more diverse range of sources. Coupling the variety of new sources with some of the historical statistical models can help to create a more complete picture. Current demand planning requires taking into consideration market intelligence, the newer and more advanced algorithms available thanks to AI and the digitization of processes, and more collaboration across multiple processes, (both internal and external), in order to to get a more accurate demand planning model.  

A bigger picture approach is needed

As the COVID-19 pandemic has demonstrated, companies must assess their internal processes in regards to forecasting, and also incorporate data and observations about the external forces that may have an impact on the market. External market drivers that go beyond seasonal swings have more of an impact than ever before thanks to the globalization and interconnectivity of the market and supply chains. A combination of data sources, both internal and external, is therefore critical in a modeling platform in order to determine what decisions must be made to maximize both service capabilities and profits. This, of course, does not imply that the usage of historical data should be completely abandoned. The utilization of year-over-year and quarter-over-quarter data still serves a purpose, as part of a more complete picture that includes market-based forecasting as the main driver in decision-making. 

In a brick and mortar retail store, for example, weather, seasonal travel, foot traffic, and other external data may serve to help understand what is driving sales on a particular day. Incorporating a broader range of market-based data, however, like social media trends, general health concerns, political discussions and more that can be mined from social media platforms allows for the utilization of sentiment data as a component in forecasting. For instance, there was a massive spike in sales of Ocean Spray Cranberry Juice thanks to a TikTok trend. Social media platforms can be great indicators of market shifts.

With public health data, social media data, and traditional external factors all taken together, an organization is equipped with a fuller and more expansive view on what product mix and volumes should be made available.   

Other things to consider

Other key market concerns that can be found within the supply chain, like competitor pricing, distribution or manufacturer incentives, and so on, are also critical data points to be taken into consideration. Utilizing every piece of available data, and expanding the range of sources of information allows for the most integrated revenue management solutions.

Taking all data points into a system that allows for advanced algorithms, such as AI or machine learning, will produce a forecasted picture that gives more specific predictions. Predictions about which demand drivers will have an impact on demand volumes and pricing that helps demand forecasters to make more specific decisions.   

The future of demand planning is in the details

At its core, the future for demand forecasting and planning is in the details and data. AI and machine learning, working within a platform that enables the collection and input of a vast array of data, and one that has the flexibility to recalibrate when needed will lead to the most accurate results. Companies that have access to the most complete information are in a better-informed position and will be able to make decisions quickly and efficiently.

Flexibility and responsiveness to the market will cut down on wastes, and allow for peak outcomes. In times of great uncertainty, the most information and the ability to clearly communicate it both within and without any organization leads to better profitability and confidence.