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Forecasting & Demand Planning

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The Big Data Imperative in Forecasting & Demand Planning

Analyst Insight: "Big data" software and analysis will be the most important supply chain technology for forecasting and demand planning in the years to come. Through analysis of huge quantities of data it provides a competitive advantage by providing unparalleled insights. 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 absorb the information into their organizational decision making processes. - Nada R. Sanders, Professor of Supply Chain Management and Iacocca Chair, Lehigh University

A recent survey of 214 senior executives and managers finds the majority (78 percent) to believe "big data" a forecasting priority for the future. Sixty-eight percent expected to be making some degree of technological forecasting software investment in the coming year. However, eighty-three expressed substantial concerns regarding costs of the technology and the adoption decision for their individual needs, considering costs versus capabilities needed. This was especially true of smaller and medium-sized firms. Further, they expressed concern regarding adapting the technology to their organizational processes. Specifically:

" Data Versus Information. Big data enables mining of huge quantities of data. The trick is converting this massive data into usable information the organization can absorb and effectively use to make decisions. There are two critical elements for success. First is having a good forecasting process in place before technology adoption. Also, a rudimentary knowledge of forecasting continues to be essential. Technology overlaid on top of poor processes just solidifies poor performance.  Leading companies understand that they cannot just invest in these technologies without adequate organizational processes in place. Second is having an organizational structure that can absorb the information into their decision making processes. This requires creating a learning organization where processes are in place to absorb the new information.

Customer Service Driver.One of the primary reasons for investing in analytics software is to improve operational performance. This includes improving forecast accuracy, reducing demand variability, and improving supply chain visibility. However, another high-ranking reason is to improve customer service. In fact, a large percentage of companies today are placing greater importance on enhancing the customer experience, as a driver of growth, rather than merely cutting costs. Leading manufacturers are expanding their product offerings to include a service component - called "servitization" - which is adding additional supply chain complexities. Analytics helps identify key elements of the product-service bundle customers consider most important.

Dismantling Information Silos. Past investments in sophisticated technologies - such as CRM or supplier management software - have created information silos at many organizations. Using insights from big data, companies can better integrate information residing in disparate organizational areas, such as purchasing, operations and distribution. Effective performance will come from unifying these into a single data base.

Sales & Operations Planning.  Best-in-class companies are breaking down the silos with big data sharing across the organization, improved collaboration, and use of unifying metrics. Unifying data bases from CRM, supplier management, and operations enables better understanding of end-to-end processes. Further, incorporating pricing, inventory, and risk as decision factors within the S&OP and S&IOP (inventory) process provides greater understanding.

The Outlook

Technology and analytics rapidly continue to provide a competitive advantage to leading-edge firms. In 2013, companies will increasingly commit resources to new technologies, maintain technology upgrades, and hire analytical capabilities, such as decision scientists.  Leading-edge companies will also redesign internal processes - breaking down silos and improving collaboration - in order to utilize new information capabilities to their fullest extent. Expect these companies to ensure that organizational processes are in place to use this new information for improved decision making.

Keywords: servitization, Big Data, forecast accuracy, information silos, data vs. information, supply chain management IT, value chain IT, supply chain solutions

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