April 15, 2019 Streamlining Distributed Manufacturing Through Data Analytics

The shift to the right technology to manage complex automotive supply chains isn’t just beneficial, it’s critical.

April 12, 2019

By Pete Butler, founder and CEO, MS Companies

As the automotive industry supply chain continues to grow (currently responsible for 3% of the U.S. GDP), automakers and their suppliers are looking for automated solutions to address their challenges in risk identification and prevention. With the more than 20,000 moving parts that go into a vehicle and increasing numbers of distribution plants and locations, gauging these defects has only become more difficult and costly.

Effective use of big data paired with a scalable, interoperable platform can provide manufacturers and suppliers the ability to efficiently identify and contain defects and address quality issues in the supply chain. Unfortunately, most manufacturers and suppliers lack reliable technology that captures configurations of parts in deep granularity. In the face of this intensifying challenge, many continue to use unreliable point-based solutions, multiple ERPs, legacy systems or basic spreadsheets to drive massive operations, but these methods aren’t cutting it.

According to a study from IBM, automotive executives rank visibility as the most critical means to effectively containing costs and managing issues, but outdated systems often hamper end-to-end supply chain visibility, cause latency in critical decision-making and breed operational inefficiencies. What they need is a solution set that provides data-driven insights within an advanced hierarchy, enabling operational managers and executives to pinpoint issues across multiple distributed manufacturing locations in real-time.

Thirty-one percent of production/non-production processes and equipment already incorporate some level of embedded intelligence, and this number is only projected to increase. With the right data-driven solutions in place, manufacturers can reach synchronization, reduce costs and boost productivity. The benefits supply chain executives should expect from this technology include:

1. Real-time data and alerts throughout operational levels to identify and resolve issues. When manufacturers and suppliers manage separate operational databases, information becomes easily fragmented and impacts issue and resource management. OEMs can utilize real-time data analytics platforms in conjunction with their suppliers to aggregate and share data across individual platforms and combat the heavily siloed structure of traditional supply chains.
2. Reduced communication delays, costs and inefficiencies across distributed locations. The right platform can bridge the communication gap that exists in the automotive manufacturing industry by tying people, processes and systems together for heightened visibility and improved decision-making. A digital communication tool can connect the entire supply chain ecosystem with user-friendly interfaces that allow supervisors to create, automate and track progress of tasks and case assignments, and managers to access and configure data based on KPIs.
3. Immediate responsiveness and dramatic reduction of the defect exposure window to production. Through data input and analysis, OEMs and top-tier suppliers can leverage this innovative technology to bridge digital communication and drive containment back to the source to eliminate waste. As miscommunication from incorrect or incomplete data is often the culprit of supply chain inefficiencies, this type of platform offers significant value.
4. Identification of defect root causes and source resolution. Collecting data and allowing it to flow from the bottom up through the operational pyramid allows manufacturers to analyze and identify the origin of issues that are found hindering operations, dramatically reducing time expenditure for issue containment.
5. Course corrected variability in parts with supplier/manufacturer synchronization. Maximizing profitability in a supply chain lies in the ability to quickly identify and contain issues with minimal disruptions. A data analytics platform can centralize the data that allows OEMs and suppliers to track trends so they can predict and prevent issues from arising in the future.

As the automotive manufacturing supply chain becomes almost too massive to manage, industry stakeholders need to continuously evaluate the supplier network to identify areas for improvement. There are significant challenges to productivity that must be met with aggressive technology solutions to ensure continued growth, efficiency and global profitability. Now is the time to adopt the technologies that support business objectives in a complex ecosystem and drive competitive advantage.

About the Author
Pete Butler is chief executive officer and founder of MS Companies, a data-driven technology company providing manufacturers and suppliers with unparalleled efficiency and agility in both workforce and operational quality objectives. Pete is an accomplished entrepreneurial business executive with more than 25 years of experience strengthening and developing existing business models and state-of-the-art new business ventures

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