Making Industry 4.0 Real for Small and Midsized Manufacturers

2/13/18 10:14 AM

Too often, well-intentioned discussions of how to prepare and operate toward the vision of Industry 4.0 become muddled in industry jargon. Rather than gaining concrete plans to move forward, you’re advised to harness Big Data, mix in some Artificial Intelligence, and leverage IoT, Cloud Computing, and Mobile Device Platforms. You’re left asking, “But what do we do to move forward, and what does progress mean?”

This post makes Industry 4.0 more tangible by describing, in the context of existing technologies, a specific case where a manufacturer reaps immediate benefits from Industry 4.0 capabilities and plotting ways to position for competitive differentiation over the next three to five years.

Making Big Data Specific Wins the Day

Industry 4.0 rests on your ability to capture and analyze Big Data – the vast amount of information generated from your factory machines, smart products, mobile devices, transactions, and more. Truthfully, however, what will separate your company from the pack is not only your ability to capture and analyze Big Data but also your ability to act on it via specific automated processes that give you a competitive advantage in targeted areas.

For example: Two midsized manufacturers with the same ISO certification are contracted to manufacture a new toy robot. The manufacturers produce the toy to the same specifications and source raw materials and components from the same suppliers. Nine months later, Manufacturer A’s robots were failing at a rate of just 0.04%, while Manufacturer B’s were failing at the abysmal rate of 5.20%. The toy company fired Manufacturer B.industry 4.0

How did Manufacturer A avoid in-field defects when Manufacturer B couldn’t? The answer lies in the ability to automatically collect, analyze, and act on relevant data … much faster than humans.

In this case, the key difference was Manufacturer A’s use of data provided by the National Weather Service and temperature sensors employed by a third-party logistics provider (3PL). Manufacturer A had modeled an automated process in which raw materials and components sensitive to extreme temperature fluctuations were identified, flagged, and tracked back to their source, through the delivery chain, and up to Manufacturer A’s warehouse. The tracking included dates and locations of raw materials and components, outdoor temperatures and humidity levels for those dates and locations, temperatures and humidity levels in the trucks and warehouses, and the length of time spent at each location.

A second process fed that data into an automated calculation to produce a “Temperature-Humidity Production Risk Factor” ─ not just for individual materials and components but also for assemblies that aggregated their risk factors. If any material, component, or assembly was calculated to have a risk factor over a certain level, an automated workflow in ERP instructed warehouse receiving personnel to store the associated raw materials and components in a temperature-controlled warehouse section for one full week before releasing them to the manufacturing shop floor. This ensured that factory machines weren’t cutting, shaping, and assembling materials and components that would shrink, expand, or lose their integrity after consumer purchase. Moreover, they lowered their defect rate over time by requesting that the 3PL only place their key components and materials on trucks at night, avoiding long hours of excessive heat and humidity exposure.

Manufacturer B didn’t have the means to incorporate weather, heat, and humidity data and calculations into its processes for receiving materials and components. As in-field failures started to rise, they investigated quality data by production run, checked machine integrity, machine settings, and even changed a supplier. They may have eventually found the answer, but with a competitor using targeted Big Data and having near-zero defects, there wasn’t enough time.

Manufacturer A’s Technology Exists Today

Today, advanced ERP systems like SAP Business One integrate seamlessly with database technology like SAP HANA, enabling all parts of a manufacturing company to come together in a single environment for modeling and performing Industry 4.0 actions. HANA collects data from sales, suppliers, logistics, manufacturing, finance, and outside sources and, using SAP’s integration framework and ‘in memory’ technology, manufacturers can use predictive analytics against this data for the processes they deem critical to success.

Are Business One and HANA the only options for this? No. However, manufacturers must consider where they are now and where they want to go. Business One is broad and deep in its capabilities for modeling both standardized and customized processes. As your company identifies the elements of Industry 4.0 where you can differentiate yourself, you’ll want as much modeling flexibility as possible. Also, the seamless integration with a high-powered database like HANA, purpose-built for the very tenets of Industry 4.0, may make it hard to beat Business One and HANA for years to come.

For more information, contact us today.

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