Why the Traditional Approach to Demand Planning Won’t Work Post-COVID

By Greg Silverman, Aug 17, 2020

If there's one thing that's certain right now, it's that society is collectively living through unprecedented circumstances. With months of social distancing behind us, there's a light at the end of the tunnel as states plan to strategically reopen businesses and entice more economic activity.

With the new beginning of business openings on the horizon, it's easy for organizations to want to fall back into old patterns, especially regarding demand planning. However, decision-makers must realize that the demand they always perceived as a given before the COVID-19 pandemic has likely been reduced. Consumer behavior has already shifted multiple times of late, with eCommerce sales 40% higher than they were pre-pandemic, according to Digital Commerce 360. Shopping priorities are changing; consumers are spending more on entertainment and household supplies while the travel and hospitality industry struggles to adapt.

Even as society works toward living in a post-COVID-19 world, demand across the board is likely to remain inconsistent, but not unpredictable. To meet the purchasing needs of the current and future market, businesses must forego the traditional approach to demand planning and incorporate augmented analytics to accurately forecast demand.

What is traditional demand planning?

As discussed in our previous post, "Demand planning vs. demand forecasting: What's the difference? ," demand planning has traditionally been a straightforward process. Businesses use historical data on purchasing behaviors and sales to articulate a practical target for other teams to help accomplish. This provides a clear and linear path to success, especially when aided by algorithms and machine learning that further streamline processes.

As accurate as the traditional approach to demand planning has evolved to be, it still has one glaring flaw: It assumes demand is a constant. Furthermore, this past method of predicting sales is limited in its inputs. The Institute of Business Forecasting & Planning explained that most businesses that utilize static demand planning employ a single hierarchy and only strive to find a single model to best fit their needs.

Using simple, unchanging variables may help businesses extrapolate their data to predict demand, but during uncertain times these constants no longer exist. Once the system is shocked by an event such as the COVID-19 pandemic, organizations need demand forecasting models that are flexible enough to be tweaked over time to capture the ever-changing variables of market conditions and consumer behavior that affect demand.

The reality of a post-COVID-19 business landscape is that it will not be like the past. Organizations must accept that now is the new normal and the time to make changes to their demand planning models to adapt to current and future market conditions. Without action, businesses risk losing shares in a contracting market as they miss opportunities to conquest new customers and capitalize on new trends. Only by utilizing the latest technologies are businesses able to adapt to new customer demand.

Incorporating augmented analytics into demand planning

Augmented analytics is the culmination of automated technologies such as machine learning and natural language processing. With this power, this form of analytics enhances a business's data stores, sharing capabilities, and business intelligence tools. Unlike its similarly named counterpart (artificial intelligence), augmented analytics still relies heavily on human intervention. Rather than taking the place of decision making, this technology enables team members to make better, faster strategic choices.

Augmented analytics is already gaining traction in the business world. Gartner predicted that this year, it will be the dominant driver of new purchases of analytics, business intelligence, and a slew of other machine learning and analytics platforms. Augmented analytics is composed of both Mode 1 capabilities to explore data for insights and Mode 2 functionality to blend static predictive analytics with the ability to run scenarios. This combination has disrupted traditional BI and ushered in a new way to query and narrate data.

This blending of modes is what Gartner recommends businesses aim to achieve, and considers a single platform capable of this seamless, best-of-breed experience to be the "holy grail." Demand planning in the modern era requires this combination of modes to both explore historic data for insights in the market and run scenarios to determine how current consumers will respond to their actions.

Augmented analytics allows business decision-makers across departments to obtain insights from current data to generate strategic insights in near-time. With this information, organizations are able to tailor their marketing plans and budgets to meet their goals even if demand differs. Whether it be acquiring new customers or lowering costs in response to decreased demand, augmented analytics helps businesses forecast human behavior to make these strategic decisions.

Prescriptive analytics makes this possible

Luckily, you don't have to embark on a Monty Python-esque quest to find the holy grail Gartner described, as it already exists. Prescriptive analytics, delivered through a software platform to recreate how a consumer market behaves in a simulated environment, helps businesses forecast demand now and in a post-COVID-19 world. One platform has the power of Mode 1 and Mode 2 described by Gartner to provide a roadmap to businesses needing to achieve their revenue marks. In times of uncertainty, this ability to analyze data and run what-if scenarios in near-time is what helps businesses determine where to invest and how to acquire new shares under different market conditions.

In simulation, prescriptive analytics is the foundation of the system, and augmented analytics is the bridge between business decision-makers and analytics teams. Advanced machine learning and artificial intelligence provides fast, accurate insights for business users, and the prescription provides clear instruction for teams to take action.

Together, augmented and prescriptive analytics not only provide an accurate picture of consumer demand but a recommendation for how to achieve peak performance even when consumer spending shifts. Concentric has the platform that helps businesses determine if their demand post-COVID will be enough to survive along with recommendations for the strategic actions they should take to ensure consumers will choose their product. Contact us today to learn more.

 

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