Companies are drowning in data, but are low on insights. Exciting advances in analytics are opening up new opportunities for supply chain teams. The challenge is that it means charting a new path and defining new processes to take advantages of new capabilities. The path forward means charting a new direction. It is not an evolution. –Lora Cecere, Founder, Supply Chain Insights

The evolution of supply chain analytics is an exciting development and opportunity for business leaders. While traditional analytics approaches focus on the building of analytics as an extension of traditional applications—Advanced Planning (APS), Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Supplier Relationship Management (SRM), Transportation Management Solutions (TMS), and Warehouse Management (WMS) -- new analytic approaches are designed based on data flows and requirements. In this new world, data pools, and streams inform business teams. The significant advances are:

• Cognitive Computing. Within five years, decision support technologies like supply chain planning, revenue management and sourcing will be transformed through the use of cognitive computing. This new approach will allow teams to drive new business value through semantic reasoning. The challenge for most teams will be trusting new forms of analytics. Companies will have to force a divorce from Excel spreadsheets.
• Open Source Analytics. When e-commerce providers could not scale on relational database technologies, they evolved open source capabilities on Hadoop. Massive parallel processing enables schema on read and greater flexibility in the design of analytics. Instead of being stuck with inflexible hierarchies, schema on read enables the building of hierarchies and relationships from reading the data. This allows the evolution of capabilities.
• Machine Learning to Drive Insights on Master Data. Companies are plagued by disparate data. The view is that it is dirty data. The reality is that most silos within an organization need data with a different context. As a result, the traditional approach of hand-coding master data is expensive and outdated. The use of machine learning allows the read of the data when needed and the shaping of data to the business context. Machine learning will transform master data processes within two years.
• Streaming Data Architectures. Today’s business processes operate on batch processes and latent data. With the evolution of Internet of Things (IOT) and advanced sensors, new processes will evolve to transform the enterprise to make decisions at the speed of business. This includes the redefinition of replenishment from the outside-in, the redefinition of digital manufacturing to transform maintenance, and the building of new processes for service of heavy equipment, utilities and asset-intensive processes.
• Unstructured Data Mining. Within the organization 70 percent of data is unstructured. This data is essential for understanding customer sentiment, warranty and quality data. The mining of this data enables the use of unstructured data, which allows companies to better understand how customers view their products based on consumption patterns.

These changes are not an extension of existing processes and technologies. Instead, it requires testing and learning to understand the value and limitations of the technology and then designing and implementing new processes.

The Outlook

To take advantage of new analytics approaches, supply chain leaders need to learn from the past to unlearn to relearn to take new advantage of the opportunity. Companies with Analytics Centers of Excellence and business funding for testing and learning will drive business results the fastest. The benefits cannot be approached through the use of traditional project plans. Instead, it needs to be driven by small, scrappy teams with a test and learn mindset.