As discussed in previous entries, today’s manufacturing insiders and analysts define advanced analytics as as the ability to glimpse into the future to cut the complexities of the global supply chain is key to unlocking the value of this reporting model. Operating in two distinct categories — predictive (risk management, planning) and prescriptive analytics (transportation routes, inventory management) — advanced analytics removes much of the guesswork that was at one time associated with making informed decisions about demand planning and production programs.
With this definition, it’s easy to see why manufacturing companies need to deploy such a philosophy to leverage lean manufacturing and supply principles. Yet, many myths about advanced analytics and the value thereof still persists. With this in mind, let’s examine 5 myths about advanced analytics and truths behind these myths.
False. In fact, advanced analytics can go as far as to help manufacturing companies automate production processes, but also decision-making tasks in the planning stages as well. While some decisions in today’s integrated supply chain still rely on human intervention, more and more decisions can be completed more accurately and with more efficiency via an automation platform. For example, the need to manage inventory levels and replenish certain component parts or manage the movement of containers in a yard are prime candidates for automation through the application of advanced analytics. In addition, job allocation, management of raw materials or resources, and job scheduling on the production floor are contexts in which advanced analytics gives planners more power and control over production programs. The monitoring, data gathering, reporting, and forecasting capabilities with advanced analytics make it more viable to allow for robotic platforms to execute these tasks with greater efficacy compared with human intervention.
Not only does automated decision-making streamline processes and increase productivity, it can also significantly reduce costs and increase ROI (return on investment), which is critical for manufacturers to remain competitive in a growing marketplace.
Not quite. Perhaps the most compelling advantage of advanced analytics is that they improve your organization's bottom line. According to recent Gartner surveys, companies with a more mature use of advanced analytics also often report lower costs and higher revenues. This is because advanced analytics directly improve demand capacity management by allowing real-time insights into the demand/capacity curve. Additionally, predictive insights provided by advanced analytics enable more accurate and consistent forecasting across the organization, generally leading to more effective sales and S&OP processes.
Actually, advanced analytics is a critical foundation for manufacturing companies to leverage real-time insight and reporting. Advanced analytics essentially gives manufacturing companies an “in-the-moment” window into their current supply situation in order to make adjustments or modifications for mid and long-term success. This real-time capability is critical in both the planning and production stages, but it’s also a key driver for companies in reducing instances of inventory overages or shortages, transportation or routing breakdowns, and other costly disruptions along the value chain. Avoiding these pitfalls should be a top priority for manufacturing companies in leveraging a lean, streamlined production philosophy where waste in the supply network is significantly reduced and best practices are optimized for the greatest possible business outcomes.
If we think of advanced analytics as one method manufacturing companies can conduct in-depth reviews of their supply stream efficacy and strength, then it makes advanced analytics is a core driver in helping companies gather and sort complex data sets. Today’s global supply network relies heavily on the accuracy and efficacy of detailed reporting based on advanced metrics and criteria. Without these optimized reporting capabilities, manufacturing companies can find it difficult to create the accurate modeling and forecasting necessary to ensure more effective demand capacity planning and production programs. Through greater production control across a wide supply stream and enhanced capabilities to gather, sort, and collaborate on complex data sets, advanced analytics allows planners and managers to generate more accurate, detailed reports that foster more effective, precise modeling in future planning. The ability to better predict where you’re headed based on where you’ve been offers companies enhanced value in allocating the right resources and materials for given production programs.
Myth busted. A supply chain backed by advanced analytics is characterized by simplified integration processes that remove bottlenecks and eliminate duplicative data modeling. Furthermore, advanced analytics mean that real-time or near real-time data availability becomes the rule, rather than the exception. The natural result: increased speed to market. As the supply chain "learns" how to predict and respond to problems more quickly, modification and delivery times shorten. Meanwhile, advanced analytics enable supply chain managers to identify issues and replicate successful deployments across different parts of the organization. Increased speed to market is especially important given the industry's move toward additive manufacturing. Advanced analytics are necessary to truly scale any additive manufacturing enterprise because they offer the foundation for proactive processes that shorten lead time and resolve problems faster — thus accelerating successful product launches.