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Auto-Mobility OEMs and suppliers alike are turning to new data analytics tools to reduce risk, boost performance and manage volatility.

2015 was a year of mixed and very modest growth in the auto-mobility industry. According to research firm IHS, mature car markets fared decently, but many of the world’s emerging markets saw poor sales performance. As a result, the sector realized just 1.5 percent in sales growth worldwide – the slowest pace since 2010.

While analysts expect 2016 to be better, growth will be far from robust. IHS expects global sales to expand at around 2.7 percent, with some 89.8 million units being sold.

In the face of this modest outlook, automakers and their suppliers are redoubling their efforts to streamline their operations – to reduce inventories at all levels, predict demand more accurately and anticipate risk more effectively.

To accomplish these goals – and do so on more than just a modest, incremental scale – automakers and their suppliers are turning to predictive analytics, modeling, simulation and optimization applications across their business, most particularly in the supply chain.

“Predictive analytics is developing into a powerful tool, allowing for an enormous boost in forecasting efficiency as well as operations and performance,” says Craig Giffi, Head of Deloitte’s U.S. Auto-Mobility Practice.

The challenge, Giffi says, lies in automakers’ ability to make sense of giant quantities of readily available knowledge and experience data. But the new analytics applications, combined with human expertise and intelligence, are helping original equipment manufacturers (OEMs) and suppliers turn the global supply chain into a powerful weapon for driving growth and managing risk.

From Reacting to Sensing

Just how does this emerging science of predictive analytics help transform the auto-mobility supply chain? “Advanced supply chain analytics represents an operational shift away from management models built on responding to data,” explains Siddharth Patil, Head of the Analytics and Information Management Practice for the Production Segment at Deloitte US. Instead, the technology enables manufacturers “to continually sense and respond as the industry changes around them.” Moreover, advanced supply chain analytics can help automakers analyze increasingly larger sets of data – allowing them to identify patterns and correlations that they may not have discovered in the past.

“In essence,” Patil says, “advanced supply chain analytics is providing opportunities for the global auto-mobility industry to move from historical point-in-time snapshots to real-time data access. This pushes analysis and visibility out to stakeholders within an organization and across the supply chain.”

This last point is critical if automakers and their suppliers are to realize the full potential of the predictive supply chain. According to Patil, these technologies, properly applied, help move organizations beyond just sharing data among internal cross-functional teams, to greater coordination and shared understanding of the data flows across value chain partners. “Individual silos within the supply chain, suppliers, procurement, operations, sales, the customer and consumer will be torn down” he asserts. “Instead a single, broader supply chain will emerge – one that is better connected and more prepared to sense, react, and proactively manage supply chain risk.”

Three Examples

Risk takes many forms in the auto-mobility supply chain, and managing it is a moving target. Predictive analytics can help, as these examples illustrate.

  • Supply chain control tower with real-time risk surveillance. It used to be sufficient for an auto supply chain to have good visibility of inventory, production, supply and transport flows across the supply chain. No longer. According to Michael Martin, VP Strategic Development, Global Auto-Mobility at DHL, leading companies are now marrying visibility with global risk surveillance applications to generate a risk-weighted supply chain. Analytics calculate the potential impact of those risks and suggest alternatives. Tapping these analytics, automakers and their suppliers can minimize or avoid costly supply chain disruptions.
  • Quality and supply assurance. Because OEMs rely on multiple tiers of suppliers for component and technology innovation, as well as an increasing percentage of sub-assemblies, they face escalating exposure in regards to quality and supply assurance. “Supply chain risk issues will flow deeper into the sub-tiers of the supply chain,” asserts Patel, “where … quality and excess inventory charges will continue to be the most significant drivers of supply chain risk and financial losses.” Companies can use visualization, modeling and analytics to improve their understanding of multi-tiered supply base risks, and thereby spot potential quality problems or avoid excess inventory issues before they take a toll.
  • Consumer data mining. When consumers go online to research and configure their vehicles, OEMs can mine this information to identify new emerging trends in areas such as options including color preferences. They can then analyze these inputs, and use this information to forecast demand with more granularity and accuracy. This translates into reduced inventory, less obsolescence and waste.

One Sure Thing

The use of advanced analytics in the supply chain will increase by orders of magnitude over the next few years – that much is certain. These analytics, together with human knowledge and understanding, will fuel the emergence of the predictive supply chain. In so doing, they will improve the industry’s ability to weather, and even capitalize on, the one factor that never changes in this industry – volatility.

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