Uncertainty is a given in any business, especially during a pandemic with fast-changing consumer demands and supply chain challenges to consider. But with proper demand planning, you can reduce the impact of uncertainty in your business, ensuring resiliency even when you encounter curveballs.

Learn what makes demand planning responsive and how different methods and new technologies can help reduce the risks brought about by uncertainty.

What is Demand Planning?

Demand planning is a critical business function that will ensure your supply chain can meet demand and revenue targets. It is a process of aligning the inventory levels, the demand of a product at specific periods, and other factors. Having a good demand planning practice will increase the accuracy of revenue forecasts, improve customer satisfaction and ultimately, enhance profitability.

Ideally, demand planning should be a collaborative process so the team involved can cover all bases – natural calamities, socio-economic issues of the day, and other influential factors that can impact demand.

What Is Demand Forecasting?

Demand planning and demand forecasting are commonly interchanged terms, but it’s essential to clarify the difference between the two.

Demand forecasting is a process that tries to understand and predict customer demand over a specific period. It can be done in two ways: qualitative, which is based on expert insight from internal and external players, and quantitative, based on more definitive data such as historical sales figures.

An effective demand plan relies on the most accurate demand forecast possible because the former informs the strategies in the latter.

How demand forecasting and demand planning impact uncertainty

It is natural to seek certainty, especially where big investments are concerned. But movement in the supply chain and other external factors make this impossible. Instead of going after a perfect prediction, which is never going to happen, focusing your efforts on data-based demand forecasting and demand planning will mitigate the impact of uncertainty in two ways.

  • Rationalized inventory. It is standard to retain safety stock that will serve as a buffer between forecasted and actual demand levels. However, frequent excess and unnecessary inventory are costly. One must strike a balance between buffer and inventory overages. Accurate demand forecasting and intentional demand planning will minimize the risk of uncertainties while maximizing overall resources so that they are diverted to other aspects to maximize opportunities.
  • Visibility of Suppliers’ Activities. Demand planning and demand forecasting will compel you to take stock of your suppliers’ capabilities to source and deliver the stocks that you need. This is useful in long-term planning. A forward-looking plan gives you a glimpse of future projections from your suppliers’ side, which helps in handling uncertainties.

While there will always be discrepancies because of uncertain factors, data-based demand planning based on real-time forecast will help you identify where best to allocate your resources in the inventory and other parts of the supply chain.

Common Methods for Creating Demand Forecasts for Supply Chain Planning

As mentioned earlier, there are two types of forecasting methods to consider for demand forecasting for supply chain planning: quantitative and qualitative. Quantitative forecasting relies heavily on demand data while qualitative data is mostly insight-based.

Quantitative forecasting methods:

  • Trend Projection. With this method, it is assumed that factors determining a trend at a given period will continue to do so in the same manner in the future and around the same time frame.
  • Exponential Smoothing. For this method, older data is given less weight than new data. The “smoothing constants” dictate the importance given to the data. Exponential smoothing is typically used only for short-term forecasts.
  • Regression Analysis. This method establishes the relationship between one independent variable with another dependent variable. If values of the dependent variable can be estimated, it would be possible to forecast the value of the independent variable.

Qualitative forecasting methods:

  • Market Research. Market research is a resource-intensive method, but this is worth the investment if you are planning to expand or tap a new market. Methods include focus-group discussions, surveys, and polls.
  • Expert Opinion. Managers with extensive knowledge, consultants, and industry leaders can offer insight and information to the demand forecast.
  • Delphi Method. This method is similar to expert opinion, but questions are fielded in a panel setting to generate an interactive discussion with diverse insights.

Components of a successful demand forecasting strategy for supply chain management

One or more methods may be used for an accurate demand forecasting. What is important is that you reap the results of thorough demand forecasting in your supply chain. Here are some indicators worth noting:

  • Accuracy. Uncertainty is an accepted reality in the supply chain web, but forecasts should be close to the actual figures. Otherwise, it should be back to the drawing board for forecasting and planning.
  • Relevance. No matter the depth and breadth of your forecast, they are useless if your data is dated. Quick turnaround time is just as important as accuracy.
  • Costeffectiveness. Building forecast models, consulting with experts and setting up processes are costly and time-consuming. Costs for your demand forecasting should not drastically exceed the benefits it brings.
  • Credibility. Sources of data must be of unquestionable integrity. Tools used for collecting, cleaning, and analyzing data should be supported by the latest technologies and capable of delivering accurate and relevant insights.

Leveraging artificial intelligence and demand forecasting as a service to address forecast uncertainty

The foundation of solid demand planning is accurate demand forecasting. Although it is a tall order to forecast accuracy amidst uncertainty, some tools and services can bring you closer to that ideal. AI-powered tools provide the following benefits to your demand forecasting pursuits:

  • Reduce errors. Automation can reduce errors by as much as 50%, translating to better predictions.
  • Save time. Instead of painstakingly entering data manually with the risk of errors, AI tools for forecasting can organize data for you and even flag deviations outside your key performance indicators (KPIs).
  • Real-time insights. AI with machine learning can pull in data from both internal and external sources, giving you deeper insights to improve forecast accuracy.

Algo’s Demand Forecasting solution uses cutting-edge machine learning approaches to provide more accurate forecasts. Through Algo’s unified platform, demand forecasts can feed directly into the overall planning process and help generate optimized and more precise demand plans.

AI-powered tools in the hands of experienced demand forecasters will improve your supply chain management prospects despite the uncertainties along the way. With these tools, you will learn to embrace uncertainty knowing that you can make data-driven decisions confidently.

About the author


Combining human centered AI with deep domain expertise, Algo’s analytics enriched supply chain intelligence platform helps suppliers and retailers plan, collaborate, simulate and execute a more efficient supply chain.

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