Supply chain forecasting is the difference between data-driven decision-making and floundering in the dark—here’s how companies can ensure theirs is as good as can be in 2021.
Prior to the pandemic, companies had a bit of wiggle room in regards to their forecasting. Despite being important, a relatively stable market meant they didn’t have to be on the cutting edge. Instead, they could largely rely on historical trends to shape the management of their inventory. Then everything changed. Forecasting was made more critical than ever in an unpredictable 2020. With COVID-19 related disruptions persisting, it’s crucial that companies that were lagging behind give their forecasting procedures a much-needed update.
A Forbes article on improving forecasting explains,
If legacy forecasting models were out of date before, they’re practically obsolete now. In 2020, it became clear that the old models cannot keep up with the shifting demands of a highly interconnected global economy.
This article by Morai Logistics breaks down what companies should be doing to make sure their supply chain forecasting is as effective as possible.
No More Legacy Models
As you might’ve guessed given the above quote, legacy forecasting models have to go. They’re outdated and bear little relevance to consumer behaviour today. What’s more, they present a number of vulnerabilities that are difficult to combat without modernization. These include: data silos, inflexibility, blind spots, and more.
Utilizing New Data is Key
2020 was a game-changer. Consumer behaviour was as unpredictable as its ever been. As such, relying solely on data that came prior to, or even during, the year is going to lead to poor results. In order to overcome this, companies need to look outside of their own data. Not only that, but also to have as many points of data as possible.
As a McKinsey article from late last year emphasizes,
Because the market has changed so much from the prepandemic status quo, using a company’s historical data to forecast a trendline won’t work. Developing an accurate forecast requires building a new baseline based on the external data that affects a company’s business. This approach allows companies to build models by combining proprietary historical and pipeline volumes with hundreds of external data points…
Prepare for Multiple Outcomes
One of the many things that hurt supply chains last year was a lack of agility. Often faced with the need to pivot and change their operations, companies stalled rather than proceeded. Ensuring flexibility in the planning process goes a long way towards preparing supply chains for whatever’s to come. In order to do this, forecasting should be applied to a wide range of possibilities. In turn, companies can then come up with a game plan for a large variety of outcomes and have protocols in place to smoothly pivot as necessary.
Time for Intelligent Forecasting
Ultimately, companies have to make their forecasting as intelligent as possible. As supply chains get increasingly digitized, it only makes sense that their forecasting should too. This means forecasting should be a part of larger interconnected, fully integrated supply chain system. A system which consolidates all company data, presents it holistically, offers a real-time view of all segments of the supply chain, and utilizes the latest in artificial intelligence and machine learning to gain the most accurate insights possible. Forecasting, in the midst of this digital landscape, can then be lead by the best data available.
