Academic journal article The Journal of Business Forecasting

Using Big Data to Enhance Demand-Driven Forecasting and Planning

Academic journal article The Journal of Business Forecasting

Using Big Data to Enhance Demand-Driven Forecasting and Planning

Article excerpt


Big data is a popular term used to describe the exponential growth, availability, and use of information, both structured and unstructured. Much has been written on the big data trend and how it can serve as the basis for innovation, differentiation, and growth. Companies using real information to sense demand signals and respond quickly to changes in demand can confidently cut inventory, reduce working capital requirements, and free up cash.

Big data, what is referred to as the vastquantity of both structured and unstructured information that is now available as a result of the Internet, can be manipulated in ways never before possible, and is becoming the backbone of corporate performance and economic growth. Big data is the oil of the information economy that needs to be treated as an economic asset. If not, companies are condemned to confirm the old witticism that a skeptic knows the price of everything and the value of nothing. Yet the value of big data is not well understood.

Globally, companies are starting to realize that no matter what industry they are in, one of their most precious assets is their data. If harnessed correctly, the data can unleash new forms of economic value. However, putting a price tag on data is essential. Otherwise, the information will be undervalued, and the potential for further developing and monetizing big data may not be fully realized. Companies are also finding that big data doesn't necessarily translate into easy success. Furthermore, there is no real means for companies to calculate the true value of their data. To further complicate this situation, according to The Wall Street Journal's technology reporter Shira Ovide, roughly 44% of information technology professionals surveyed recently said that they had worked on big data initiatives that were eventually scrapped or put on hold. As a result, the value of information captured today is increasingly put to use for reporting purposes (descriptive analytics), rather than the primary purposes for which it was collected (predictive analytics). With big data, information is more potent, and it can be applied to areas unconnected with what it initially represents.

Furthermore, supply chain executives identified data and analytics as two of their top four most important supply chain challenges, according to interviews conducted by Lora Cecere, the editor of the online website Supply Chain Insights LLC. Two out of the top four challenges that supply chain executives identified are access to data and actionable analytics. (See Figure 1) Gaining insights and creating actionable analytics from huge quantities of data will require technology and high performance analytics (to take advantage of the parallel and grid processing power, and in-store memory). The challenge for companies will be staying ahead of the technology in a cost-effective manner, and developing organizational processes to effectively utilize the huge amounts of data and consume the information into their organizational decision making processes.

During interviews (75 in total) with supply chain executives, it was found that their top focus area is improving their demand forecasting and planning process. (See Figure 2) Lora Cecere and Charles Chase conducted interviews in 2012. Other recent su rveys fou nd senior executives and managers believe big data to be a forecasting priority for the future. Those same executives plan to make an investment in a new demand forecasting and planning solution in the near future. However, many supply chain executives have substantial concerns regarding the costs of the technology, and the requirements essential to make an informed adoption decision for their individual needs while considering costs versus capabilities. They also have concerns regarding change management requirements for adoption of new technology within their organizational processes. To be more specific, supply chain executives will have to overcome the challenges associated with big data. …

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