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How Demand Forecasting Can Boost Business Efficiency

The art of predicting the future is nearly as old as civilization itself. Oracles, fortune tellers and soothsayers of all kinds have long claimed to be able to see the course of future events, and they’ve often found a receptive audience in humans who want desperately to know what’s just around the corner.

These days, of course, the world has more scientific and data-based methods available. The business community, in particular, has embraced what has come to be called Big Data: the practice of taking the mountains of information that businesses accumulate and putting it to work. One of the most widely-used applications of Big Data is the practice of demand forecasting, in which analysts attempt to understand what customers will want next by understanding what they’ve wanted in the past.

What kinds of methods do businesses use to perform these calculations, and what goals do they typically want to accomplish? Perhaps most importantly, how can they improve their accuracy and make them a smart use of resources?


 
Basics of Demand Forecasting

Businesses of all kinds use demand forecasting to estimate future demand for their goods and services. A basic primer on today’s demand forecasting methods begins with the difference between each major type of forecasting:

  • Short-Term Demand Forecasting: Focuses on customer demand within several months to a year and how best to prepare ordering and shipping practices for seasonal demand cycles and approaching campaigns.
  • Mid- to Long-Term Demand Forecasting: Focuses on strategic deployment of resources to meet anticipated customer demand in the next one to five years.
  • Active Demand Forecasting: A more intensive technique used to evaluate how aggressive plans for expansion and scaling will interact with consumer demand.
  • Macro Demand Forecasting: The “big picture” demand forecasting technique that looks ahead at broader market conditions to help plan a business’s overarching strategies.

Beyond that, there are two broad categories of methods that each type of forecasting can employ. Many businesses use both, combining techniques to achieve better results:

  • Qualitative Forecasting: Uses methods that focus on opinion and consensus, such as market research and the Delphi method, to arrive at a reasoned conclusion.
  • Quantitative Forecasting: Uses statistical techniques and computer modeling to crunch the hard numbers and create a data-intensive picture of future demand.

Businesses use all kinds of tools and technologies to implement these methods, from relatively simple research surveys to complex data modeling tools. That’s certainly a big investment—so what, exactly, do these methods achieve?

Why Demand Forecasting Is Important

Most businesses consider demand forecasting to be critical for achieving a better deployment of resources. The individual goals that each business hopes to achieve through demand forecasting are as diverse as individual business plans, but there are some common themes:

  • Optimizing Inventory: In a business climate where warehouse space and fulfillment capacity are perpetually at a premium, it’s essential to ensure that valuable space and logistics resources aren’t being wasted on inventory that does not create value.
  • Improving Cash Flow: By the same token, it’s important that businesses keep their cash flow situation agile and not over-allocate resources to projects that aren’t aligned with market conditions.
  • Serving Customers Better: Whether it’s disposable consumer goods or high-end B2B services, every business wants to give its customers what they want. Accurate demand forecasting helps to improve customer satisfaction by ensuring that supply matches demand as closely as possible.

Obviously, there are some excellent reasons to implement demand forecasting—assuming that it works. All too often, though, businesses experience frustration with inaccurate forecasting. Thus, the relevant question becomes: How can forecasting be implemented in a way that provides solid ROI and improves efficiency, rather than simply prognosticating?


 
How to Boost Efficiency Through Better Demand Forecasting

Nearly every large- or medium-sized business today uses some form of demand forecasting, and thanks to the advent of affordable cloud-based ERP software, it’s more easily available than it’s ever been. However, to improve the accuracy and efficacy of demand forecasting, it’s important to implement some best practices used by the top forecasters in business. Although there are many ways to do demand forecasting right, these key tips can help a business get more out of their investment in the practice:

  • Remember that demand is not monolithic and try implementing a disaggregated model. Break down demand by demographics, product lines, regional differences and any other statistical segments that matter. Again, the powerful reporting features available in many distribution software products can help establish what these segments are and how they can be separated.
  • Focus resources not just on forecasting demand itself, but in determining the variability of demand forecasts. A forecast with a five percent margin of error and one with a 20 percent margin of error can both be useful, but building a business plan around a high-variability forecast is much more risky. Uncertainty will always be present, so a smart business will take steps to identify just how much uncertainty is present and allocate resources more or less flexibly based on that understanding.
  • Use automated systems to monitor how a demand forecast is matching up to actual expressed demand. Collaborate with data experts to identify breakpoints at which divergence will require revision of demand models and then set up alerts at those points. Many modern manufacturing ERP systems allow for sophisticated real-time modeling of demand that allows this kind of rolling analysis to take place.
  • Diversify forecasting methods. No one approach can capture every element of a system as complex as market supply and demand, so try out a variety of models and evaluate their accuracy using the previously discussed principles such as real-time monitoring and variability forecasting.


 
Planning for the future is a key part of any business, and demand forecasting is now an indispensable tool for making that planning useful and effective. So, although tools, techniques and goals may vary, one thing is clear: Establishing a forecasting system and evaluating its efficacy should be high-priority goals for a business that’s trying to grow, compete and innovate.