“By the end of 2020,” asserts the editorial staff at Material Handling & Logistics (MH&L), “one-third of all manufacturing supply chains will be using analytics-driven cognitive capabilities, thus increasing cost efficiency by 10% and service performance by 5%.” Most analysts agree the Digital Age demands the implementation of digital supply chains. What does that mean? To my mind, it means maximizing the use of data being generated across value chains to create the most efficient and effective supply chains possible. The MH&L staff notes, “Advanced analytics [can be used] to complement existing analysis, focusing more on identifying patterns and prerequisites for workflows and processes, such as preventative maintenance and customer sentiment to direct sales, [and] identifying customer preferences for more efficient product innovation.”
Advanced Supply Chain Analytics
Although the Digital Age is characterized by the vast amounts of data being generated, without analysis the data is of little use. Jeff Bodenstab, ToolsGroup Vice President of Marketing USA, reports, “Gartner has created a … five-stage maturity model for assessing the overall maturity level of your organization in using supply chain analytics.” According to Gartner, the five maturity stages are:
- “In Stage 1 the goal is to use data to measure a single metric within a particular function, focused on after-the-fact performance. Excel spreadsheets dominate, providing limited analytics.”
- “In Stage 2 the aim is to measure performance and provide data for decision making in supply chain functions. Companies bring in data from ERP and other systems. The organization operates in functional pockets, with little collaboration or knowledge sharing. Applications target improvements in functional silos, with supporting technology from Excel spreadsheets, reports and dashboards.”
- “Stage 3 improves decision making across the internal supply chain. Companies focus on data harmonization and good data governance so the analytics can leverage end-to-end process data. The supply chain data is aligned with areas like product development, sales, and finance. Applications focus on establishing visibility and performance measurement across processes — like using descriptive analytics to determine the cost to serve a customer across the chain. Advanced analytics emphasize predicting scenarios and prescribing actions across the entire supply chain — like simulating the impact of order variability on production plans.”
- “In Stage 4 the objective is to improve performance of a more extended supply chain of trading partners. Data comes from both internal sources and external trading partners to focus analytics at a network level. Technology concentrates on multi-enterprise capabilities to create outside-in visibility and measure performance across the extended chain. Analytics are faster and more dynamic, and exploit trading partner data — i.e., a CPG manufacturer creates forecasts for a new product launch, then adjusts replenishment plans based on retail partner downstream data.”
- “At Stage 5 the goal changes to measuring and improving performance across a trading partner network to satisfy customer demand while maintaining margin. Data comes from public and unstructured sources and the IoT. Complex applications focus on visibility, improved performance, and creating value across the network. Supporting technologies automate decision making and execution, factoring in complex trade-offs and overall business goals among trading partners — like setting optimal safety stock levels for networked suppliers. Analytics support new business models and demand shaping. In-memory computing manages the large data streams for rapid response.”
Note that Gartner doesn’t use the term “advanced analytics” until the third stage of maturity. My casual observation is that few companies remain in Stage 3 for very long because few, if any, companies rely solely on internal data. Lora Cecere (@lcecere), founder and CEO of Supply Chain Insights, looks at advanced analytics differently than Gartner. Rather than looking at advanced analytics through the lens of a maturity model, she discusses “five relevant dimensions” of advanced analytics. “First,” Cecere writes, “consider how to drive insights. Companies are data rich and insight poor. New forms of insights from predictive, to prescriptive, to cognitive analytics define new capabilities. Difference? While visualization helps business users to see the problem, there is no optimization. In predictive analytics, there is a clear objective function and a solve against a desired outcome. On prescriptive analytics, the solve yields exceptions and the insight engine gives recommendations on how to best manage them. Machine learning drives insights for prescriptive analytics. Cognitive sense, learn and then drive action.” It’s important to note there is no single, silver bullet analysis that meets all the needs of a business. What Cecere is arguing, however, is that companies need to embrace new cognitive technologies to ensure they can best leverage all available data in myriad ways.
The second dimension companies need to consider, according to Cecere, is “form and function.” She explains, “With new analytics, we can now improve workflows, collaboration, the use of sensors with streaming data, unstructured text mining, and the management of transactional data. Data can now move at the speed of business. While the traditional paradigm focused on batch and latent data, new forms of analytics redefine the Art of the Possible. Streaming data architectures are quite different from traditional analytic approaches.”
The third dimension of advanced analytics discussed by Cecere is “type of deployment.” She explains, “Analytics can be deployed in clouds, rivers or lakes. Traditional analytic approaches were more focused on reporting on applications like ERP, CRM, APS, WMS, and SRM. New approaches are not application specific. Instead, these analytic architectures sit between the traditional applications and workforce productivity.”
Cecere’s fourth dimension is the “database structure.” “New forms of analytics enable schema on read versus schema on write. The traditional use of use of relational database technology dictated schema on write forcing users to define hierarchies and relationships in deployment. The problem? Business changes. What is believed to be the right requirements in an early deployment might not be the answer. Schema on read enables a much more flexible approach for decision support and workflow. This approach makes many of the relational database structures questionable. While columnar store makes sense for transactional data that is used frequently, it is not a good choice for decision support for time phased data or streaming data architectures.”
Cecere’s final dimension is “defining the system of record.” She writes, “As analytic infrastructure evolves, less and less enterprise data will be written to ERP. More will be written to data lakes to enable schema on read approaches to enable new forms of process enablement. Transactional data, while important, will be combined with unstructured data more routinely in data lakes, clouds and streams. We must rewire our brains to not think so narrowly as the integration of transactional data into ERP as the end-to-end vision for supply chain management. Blockchain will become the System of Record for B2B. While there is much to test and evolve to enable multi-tier relationships, the technology is promising.”
The common ground between Gartner’s maturity model and Cecere’s analytics framework is the insistence organizations will only flourish if they implement cognitive advanced analytics. Analysts from Source One observe, “The increasing discussion of AI in the supply chain world has been brought on by the convergence of ambition and ability. Supply Chain Dive recently noted that years of speculation have finally given way to realistic and attainable business goals. Companies are getting enough computing power to make their content into coherent insights, even when the information is massive in scope and lacking structure.” In other words, cognitive technologies can help organizations deal with the complexity now inherent in most supply chains.
 Staff, “Let’s Get Digital: Supply Chains Are Going to Get Smarter in 2018,” Material Handling & Logistics, 27 December 2017.
 Jeff Bodenstab, “Gartner’s Five-Stage Maturity Model for Supply Chain Analytics,” ToolsGroup Blog, 29 August 2017.
 Lora Cecere, “A Framework for Supply Chain Leaders to Understand Supply Chain Analytics,” Supply Chain Shaman, 4 February 2018.
 Source One, “Analytics In The Supply Chain: An Idea Becoming A Reality,” The Strategic Sourcerer, 27 October 2017.