No modern business can survive without a proper inventory forecasting system. When businesses don’t plan their stock according to well-researched predictions, they risk lowering customer satisfaction levels, reducing profit margins, and miscalculating production. During the COVID-19 crisis, for example, a failure of proper demand forecasting led to hospitals in Michigan being forced to turn away patients due to labor shortages, while in Massachusetts nursing homes, one out of five beds remained empty because of insufficient staff. These problems could have been avoided had the hospitals had better demand forecasting systems.
As these examples demonstrate, demand forecasting done poorly results in wasted products, lost profits, and even damage to the company’s reputation. So how do you avoid these problems?
Here are three of the most common demand forecasting mistakes and how to avoid them.
Building the Forecast Around the Target
In 2020, former Disney chairman Jeffrey Katzenberg launched a new video-streaming platform called Quibi. Short for Quick Bites, Quibi was meant to serve as a short-form Netflix alternative catered to mobile users. However, in only seven months, the platform was forced to shut down, returning only a measly $350 million in revenue against the $1.65 billion investment.
Many wrong decisions contributed to the failure of Quibi, but perhaps the most egregious was the company’s insistence on building its demand forecast around ideal sales, rather than reality. This led Quibi to overestimate how much it needed to spend on production.
To avoid a Quibi situation, base your forecasts on both qualitative and quantitative research. If past sales records are not available, study the market. Conduct research with your target audience and analyze the factors that contribute to your competitors’ success.
Not Adapting to Market Trends
In 2021, film camera company Kodak filed for bankruptcy. Though Kodak was one of the biggest names in the camera industry over the decades prior, the rise of digital cameras significantly reduced its competitive edge. Stubbornly, Kodak clung to its film cameras, insistent that its history of success would be replicated despite the evolving market. Ultimately, Kodak’s failure to adapt rendered it obsolete.
Although analyzing the past is usually a reliable way of predicting future performance, it shouldn’t be the only piece of data your company studies. Instead of saying “sales were good last January, so they should be good this January,” take note of the factors that contributed to sales success. Then, see how these factors match up to present conditions. Perhaps if Kodak acknowledged the emerging competition and adjusted development and production to meet the new demand for digital cameras, it would now be known as a major digital camera brand.
Not Understanding Drivers of Demand
For the longest time, Coca-Cola was the top dog in the soft-drink market. That is, until its biggest competitor, Pepsi, launched a smart new marketing campaign: the Pepsi Challenge. In this series of tests, participants were tasked to take sips of two unlabeled soft drinks, one coke, and one Pepsi. The participants were then asked to pick the drink they liked better. By a large margin, Pepsi won.
Coke, intimidated by the campaign, fought back immediately. Deciding that the product was the problem, the company painstakingly designed a new drink and measured its effectiveness with consumers Pepsi Challenge style: through blind testing. However, when this new product, dubbed The New Coke, was launched, it was met with severe consumer backlash. The New Coke was so unpopular that Coca-Cola was forced to bring back the original less than three months later.
Coca-Cola’s issue was that it didn’t look deeply enough into the factors driving demand for Pepsi. It turned out that the blind taste tests only paid attention to sips, and that consumers felt differently about the drinks when they drank them for longer than just a sip. The problem was never the product. Pepsi simply created artificial levels of demand through a misleading marketing strategy. Had Coca-Cola paid better attention to why people bought its drinks, it wouldn’t have committed such a huge and costly blunder. Therefore, to keep demand planning accurate, pay close attention to the reasons consumers buy your products.
How to Improve Demand Forecasting
One way to improve demand forecasting is to obtain the assistance of an expert data professional who can guide you in understanding the information hidden in big data, such as past performance and market trends. This knowledge and deeper understanding will then enable you to create actionable insights for optimizing production, decreasing costs, and increasing profits.
A more versatile and long-term solution is to utilize demand forecasting software. TransImpact’s Demand Forecasting Software uses 250+ algorithms to create sales forecasts for as far as 60 months into the future. Additionally, it connects your business with effective enterprise resource planning and business intelligence tools. Customizable reporting tools allow you to translate data into a language that’s easy to understand.