The Crystal Ball: Understanding Analytics in Today’s Supply Chain
Nick Ostdick - October 18, 2016
It’s the center of so many fairy tales: a character peers into a crystal ball to catch a glimpse of his or her future. While this usually has disastrous consequences for the character, the ability to look into the future and make accurate, sound predictions is a key driver for OEMs and other suppliers in today’s automotive supply chain. Advanced modeling, forecasting, and predictions based on previous demand and data provide planners and managers with a number of competitive advantages in creating precise demand planning strategies for efficient production programs.
But how do planners and managers cultivate these highly-detailed demand planning schemes? What methods are at their disposal to create modeling and forecasts whereby customer orders can be aligned with production facility capacity and programs to adequate part coverage and reliable delivery? In other words, what kind of crystal ball can planners look into in order to see their future?
Enter advanced analytics and its ability to provide planners with critical insights into the overall supply situation. Culled from intelligent planning solutions such as BOM management, Every Part Every Interval, and Plan for Every Part, advanced analytics is a platform of data mining that planners must leverage in order to gain a deeper understanding of where parts go, when, how, and the areas where efficiencies and ROI (return on investment) can be enhanced.
The trouble with analytics is that many planners and managers lack a fundamental understanding of how to define, determine, and deploy such strategies as value-added propositions for their company. Grasping these concepts will not only allow planners to leverage lean principles for greater productivity and profitability, but it will also better equip them to understand how today’s variant-rich supply stream operates in a global environment.
Understanding and Defining Analytics
Let’s make this easy: The sole purpose of analytics in today’s supply chain is to support and aid planners in complex decision making about where products go, when, and why. Advanced analytics allows companies to become more proactive in anticipating future supply scenarios and creating optimized responses to foster E2E (end-to-end) visibility across each touch point of the supply chain.
But of course nothing in today’s global supply chain is as simple as a unified definition and advanced analytics is no exception. While a fundamental, functioning definition aligns with what we just discussed, there are two branches of advanced analytics planners can deploy in order to achieve greater agility and transparency into their supply network. These two branches, predictive and prescriptive analytics, both consist of the methods, technology, and strategies that mine data, identify patterns from past performance, and predict and recommend future scenarios for optimal planning. However, the nuances of advanced analytics include:
Predictive analytics: This brand of analytics consists of extracting information or insights from data to predict trends and behavior patterns. Predictive analytics spans a number of platforms including regression, decision, clustering, and classification.
Prescriptive analytics: Based on the information and knowledge gained from predictive analytics, prescriptive analytics generates recommendations for the best course of action based on expected future scenarios.
Essentially, it’s useful to think of predictive and prescriptive analytics as two linked processes whereby one is dependent upon the other to help planners fully optimize demand planning. If predictive analytics is like a football coach watching game film to dissect how an opposing team functions, then prescriptive analytics is that same coach crafting plays for his own team to best combat the opposing team’s strategy. Predictive analytics offer support to users by helping them understand likely future scenarios; prescriptive analytics offers support with actionable recommendations.
Benefits of Advanced Analytics
As we discussed at the beginning of this entry, deploying advanced analytics has a number of potential benefits when it comes to ROI, increased efficiency, and enhanced productivity. Particularly in an age where Big Data and digital transformations are driving much of the data mining and analysis across the automotive industry, advanced analytics and its ability to quickly and accurately give planners a window into the current supply situation is rapidly becoming an essential tool to remain agile and competitive.
More specifically, incorporating advanced analytics into an existing supply and production platform means OEMs can experience:
Improved customer service. Because advanced analytics is built upon giving companies precise glimpses into the future based on past data, planners can allocate resources and production programs to facilities that are best equipped to handle any given order. This means best practices for inventory, part coverage, and container and transportation management, all of which result in greater levels of customer satisfaction as the right parts arrive at the right place at the right time.
Better inventory management. Overages? Shortages? Missing shipments? Advanced analytics provide OEMs the ability to overcome these hurdles by giving them a clear view of the holistic supply stream. Because advanced analytics relies on real-time data reporting and analysis, planners can adjust and modify orders, inventory levels, and container or yard movements based on the most up-to-date parameters for production.
Enhanced Asset Utilization. If a company’s ability to predict future production and supply needs is more detailed and efficient, it of course makes sense this same company will be able to better allocate resources and assets for improved profitability. Because advanced analytics functions as something of a lean principle where areas of waste or spent cost are identified and corrected, OEMs will see an increased ROI when it comes to how they allocate resources, where, and why - for example, everything from increased output at production facilities to more effective transportation and freight management.
As you can see, understanding advanced analytics and its crystal ball-like capacity for modeling, forecasting, and predicting is a critical driver for OEMs in maintaining a strong foothold in today’s global supply network. With more expansive production networks in new and emerging markets, the ability to more accurately dissect and predict supply chain trends and movements is essential for long-term success.