Demand forecasting in the new normal: Part 2

Demand forecasting in the new normal: Part 2 - ERP Systems

The last few years have been difficult. Many organizations that survived the pandemic made use of innovative methods that enabled them to quickly analyze and react to new information. The supply chain is one of the areas that was thrown into disarray by the effects of the COVID-19 pandemic as consumer markets were battered by surges, cancellations, and surprises. As discussed in the previous blog post, the demand planning process, whose success depends on effective collaboration between teams from various departments, was negatively affected by the pandemic.
Not only are the issues related to demand planning diverse, but the cross-functional teams involved in the process require efficient tools to be able to work effectively. Organizations realized that the tools they relied on were no longer sufficient to facilitate the increased complexity of the demand planning process. Consequently, organizations were forced to speed up the adoption of digital technologies, especially those offering advanced data analysis and reporting functionalities. Below are some of the approaches that organizations adopted to overcome the new challenges in demand planning.

Granular consumer data

To remain competitive, organizations needed to get closer to the market by collecting consumer data on a granular level. Consumers’ preferences have changed drastically and are expected to continue changing at a rate that has never been experienced before. Granular consumer data can help organizations detect early warning signs of potential changes and react swiftly to shifting demands. This data can be extracted from systems such as Point of Sale (POS). POS systems have evolved from being used just as cash registers and can now be used to manage sales, promotional offers, and customer experience.

Incorporate external data with demand forecasting

While the demand planning process normally focuses on internal operational data such as historical sales, some experts argue that using only internal data when forecasting demand is making an incorrect assumption that nothing other than internal business operations affect consumers’ choices. The demand planning process needs to closely monitor external factors which are not directly related to the organization’s operations, but which affect the organization’s sales. Economic indicators to take into consideration when demand planning include unemployment figures and disposable income levels in the areas in which the organization operates.

Enhanced Data Analytics with demand forecasting

There is a high likelihood that supply chains will remain volatile, which means consumer behaviors will continue shifting at a higher rate. Not only do organizations need to be better positioned to gather data on a granular level, but they also need to have systems to analyze the data and make use of interactive dashboards that can provide them with insights to respond quickly to new trends. Tools such as Zap BI can enable an organization to process data more efficiently. This capability can empower organizations to gain a competitive advantage by acting rapidly in areas where their competitors are slow.

While supplementing the demand planning process with the approaches discussed above has provided organizations with tools to weather the supply chain disruption storm, one may wonder if these methods are sufficient to deal with supply chain volatility that continues to increase exponentially.

It is important to note that organizations rely on forecasting algorithms, which have been used for decades, to predict future demand. Some experts believe that despite continuously supporting the demand planning process with additional tools, classical forecasting methods used in demand planning will likely struggle to perform adequately in the supply chains of the future.

One key factor is that time series analysis is at the core of most classical forecasting algorithms’ calculations and the analysis generally performs well when the data used is clean and data series are long and regular. Unfortunately, clean consistent data is not common in demand planning, as consumer demand is generally marred by lots of fluctuations.

Technologies such as Artificial intelligence and Machine Learning are the usual go-to techniques for dealing with business challenges that involve big complex data. The key question is if these techniques can bring solutions to demand planning challenges. A discussion looking into this will be covered in the next blog post – stay tuned.

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