Imagine trying to drive a car down the street using the rear view mirror as your only method of navigation. You can’t actually look forward to see where you’re going, what lies ahead, or how to successfully get where you need to be - instead, all you can do is look backwards at where you’ve already been and make your best guess as to how to keep moving forward without causing a horrible wreck.
Sounds incredibly difficult, right? Yet this is the situation many OEMs and automotive manufacturers find themselves in when striving to create accurate forecasting and planning based solely on descriptive analytic models - data that merely paints a portrait as to the current state and productivity of the supply chain - rather than predictive analytic models, which have in recent years been a value-added proposition for OEMs in fostering efficient demand planning for future production programs.
Predictive analytics, with its reliance primarily on Big Data, essentially provides OEMs with a windshield for enhanced end-to-end visibility in order to better see how agile, transparent, and responsive their value chain is, and what steps needs to be taken in order to modify or alter production and supply practices to create an optimized future supply stream.
Leveraging Big Data and other information analysis strategies such as data mining, pattern recognition, and business analytics tools have changed and will continue to change the ways OEMs plan for the short and mid-term future by allowing greater insight into strengths and weaknesses of their supply networks. And as you’ll see, predictive analytics is no longer a peripheral trend, but rather a critical driver in overhauling how today’s OEMs must conduct their global supply management.
Descriptive vs. Predictive Models
As we eluded to moments ago, predictive analytics is quickly picking up where descriptive analytics has left off in terms of providing OEMs with greater power and control over their supply streams. This is in part because descriptive analytics simply provides a view of the current state of play based solely on past performance or supply chain situations, which doesn’t account for unforeseen fluctuations in production based on customer demand, order volume, and bottlenecks or disruptions.
Descriptive analytics, which a majority of OEMs have utilized for the last decade or so, do provide some short-term demand planning advantages assuming production conditions remain consistent and there’s stability across a company’s supply stream. Of course, in today’s global, connected supply and manufacturing where facilities and hubs operate throughout the world in various social and economic landscapes, a stable supply stream cannot always be counted on.
Predictive analytics, on the other hand, with the ability to simulate complex ‘what-if’ scenarios to test the agility and responsiveness of a company’s supply architecture, are changing the landscape of a given OEM’s supply strategy from a reactive model to a proactive one. Rather than simply playing catch-up to modifications or fluctuations in the constraints of planned production programs, supply chain planners and managers can leverage the reporting predictive analytics provides to anticipate potential issues in production cycles and game plan strategies to prepare for them.
Commonly referred to as ‘insight-driven enterprise,’ this enhanced level of visibility is a core driver in promoting lean principles in manufacturing where inventory is reduced, shortages or overages are averted, and customer orders are filled on-time without undue strain on a manufacturer’s resources, both financial and logistical.
Why Predictive Analytics?
It’s true some planners and managers might view predictive analytics as yet another data analysis model and strategy to incorporate into an already crowded, existing platform of planning strategy. But the burden of implementing a predictive analytic model is only temporary, and the competitive advantages to be gained from leveraging this planning model far outweigh the discomfort.
Let’s first remember predictive analytics and its power to help planners anticipate future demand planning strategies is based on Big Data, which provides the most accurate, detailed, and up-to-the-moment view of a company’s global supply situation. This in turn allows OEMs to better leverage other production strategies such as BOM management, Plan for Every Part, and Every Part Every Interval to better allocate and control production cycles and create production plans best aligned with the capabilities of certain facilities given their available resources.
Secondly, let’s also remember the automotive supply network is one with a certain amount of volatility and complexity built into it - as is the case with any variant rich industries in which success depends on a global value chain comprised of various partners and variables subject to the nuances of the regions in which they operate. With this in mind, imagine if an OEM could access real-time ordering and sales data and the associated costs by region and product type at any given time. Then, based on this and other external data, this OEM’s planners could create and execute a strategy of dynamic, responsive production schedules, inventory management, and transportation and yard management to best meet customer and market demands.
This is where predictive analytics comes into play and provides planners the insight and visibility to foster supply streams like the one in this example capable of enhanced levels of responsiveness.
Predictive Analytics Going Forward
A recent study by Deloitte and the Material Handling Institute of nearly 400 supply chain professionals from across a range of industries indicated less than 25 percent of planners have incorporated predictive analytics to date. While only 24 percent of companies that responded indicated they currently deploy such strategies, that number is expected to climb - more than double - in the next five years, making predictive analytics one of the most pervasive logistical strategies currently in play. It’s clear given the benefits of predictive analytics and its position as an emerging system that companies who leverage this way of thinking will prime themselves for success in the near and long-term future.
