DHL Supply Chain – Excellence. Simply delivered.

2015 was a year of mixed and very modest growth in

the automotive 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.

Automotive OEMs and suppliers alike are turning to new data analytics tools to reduce risk, boost performance and manage volatility

SUPPLY CHAIN INSIGHTS

“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 automotive 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 automotive 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.”

THE PREDICTIVE SUPPLY CHAIN: NEW ENGINE FOR GROWTH IN AUTOMOTIVE

“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. Automotive

Practice.

DHL Supply Chain – Excellence. Simply delivered.

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.

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.”

Two examples

Risk takes many forms in the automotive 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 Automotive 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.