Bjorn Andersson

The Critical Role of Maintenance and Repair Data for Manufacturers

Blog Post created by Bjorn Andersson Employee on Jan 8, 2019

Managing maintenance and repair processes by averages or set time schedules is not enough anymore.

By Bjorn Andersson, Senior Director, Global IoT Marketing at Hitachi Vantara


Lean, fast-paced production environments simply cannot tolerate equipment downtime. Thanks to the emergence of digitized factories, manufacturers now have access to increasing volumes of operating data that can be used to help optimize maintenance and repair tasks, minimizing unplanned downtime. However, many manufacturers also remain unsure how to gather, analyze, and apply these insights. Where do you start and what are the steps to advance?


Is your organization leveraging maintenance and repair data as a competitive advantage?

In today’s high-stakes manufacturing environment, where time and money are continuously being squeezed out of production, every company is focused on minimizing risk and maximizing predictability. Ensuring predictable factory outputs, meeting delivery deadlines consistently, avoiding staff overtime — all are critical to success in the very competitive world of manufacturing.


mfg circles.pngThose who manage production processes recognize that equipment downtime represents one of the single greatest areas of risk exposure for manufacturers. When machines break down and production lines unexpectedly grind to a halt, deadlines are missed and, too often, customers are lost — because globalization has made many alternative suppliers available at a moment’s notice.


Many manufacturers already use some data analytics to understand customer needs, improve logistics performance, and optimize other aspects of their operations. But few companies realize the full extent of how data can keep their equipment up and running. Those who collect, gather, and apply the available operations insights for that purpose will be better positioned to win in a crowded marketplace.


Maximizing Predictability Across the Equipment Lifecycle

Production machinery is the lifeblood of any manufacturing business, often representing the single largest capital expenditure on the balance sheet. With millions of dollars of capital tied up in these physical assets, companies need to ensure their continuous, optimal performance. It’s a simple, straightforward concept: Expensive equipment needs to perform as expected and generate predictable results.


Yet, despite their value, these machines are often managed using outdated methods based on historic averages, best guesses, and estimates. Production line managers look back at traditional operating patterns and downtime events in an attempt to understand future performance and schedule critical maintenance and repair tasks. They hedge their bets by overscheduling equipment inspection, maintenance, and repair — which can result in human resources and repair parts being wasted on unneeded work. Of course, any downtime, even when planned, can have a negative effect on production outputs and overall equipment efficiency rates.


retrofit legacy equipment.pngThere is a better way. Modern production equipment is sold with — or can easily be retrofitted with — an array with sensors and other smart technologies that gather real-time, continuous operating data. Thanks to the increasing digitalization of modern production facilities, manufacturers are rapidly developing competencies around gathering and analyzing this data to provide a fact-based perspective on equipment performance.


When blended with other data, this sensor-based information can reveal how efficiently equipment is running and predict impending failures. Forward-looking executives can rely on this data to schedule maintenance and repair tasks only when they are needed, enabling them to make the most of available resources and maximize the value of capital investments. Production schedules, employee schedules, and customer commitments can be made with a high degree of confidence that equipment will be operating continuously to support high-level business goals.

When a data-driven approach is applied consistently over the asset’s lifecycle, the benefits go beyond improved utilization. Doors are opened to innovation, as company resources can instead be reallocated to pursuing new or higher-value activities.


Return on Data: The Hitachi Way

There’s no question that the digitalization of production facilities is having a significant, and growing, financial impact for manufacturers. In fact, McKinsey estimates that the increasing adoption of the Industrial Internet of Things (IIoT) can add more than $11 trillion in value to the world economy by 2025.


Much has been made of factory automation, robotics, artificial intelligence, and other advanced technologies. Far less attention has been focused on the potential of simple sensors, data science and analytics, and other IIoT-enabled strategies for optimizing daily activities — like equipment maintenance and repair — that do not require a complete shift in operations but can have substantial impact on key profit indicators such as throughput, product quality, scheduling efficiency, and customer satisfaction. Big gains can be made without having to completely rethink the fundamentals of manufacturing.


Hitachi in Mfg - picture v2.png

Hitachi is a big manufacturer itself and has helped manufacturing customers around the world save millions of dollars by maximizing the life of their physical assets and minimizing unplanned downtime. For example, Hitachi helped a mining company gather and analyze data collected from remote sensors mounted on working equipment in the field. Using proven algorithms and deep learning techniques, the company was able to accurately predict the remaining useful life (RUL) for key equipment, allowing them to minimize new equipment investments while protecting uptime and production efficiency rates.


Hitachi also helped a metal manufacturing company understand the effects of specific operating conditions on equipment degradation over time. Not only can the manufacturer predict when equipment needs to be repaired or replaced, but it can also fine-tune daily operating parameters to reduce the effects of degradation. The solution included a high accuracy early-warning system that has been able to predict potential failure more than 12 days ahead of an equipment breakdown.


The Ultimate Payoff: Production Optimization

Often viewed as a “necessary evil” and not really appreciated, equipment maintenance and repair actually lie at the heart of the complex production and supply chain. The weak link in that chain is the machine that breaks unexpectedly, sending ripples through both supplier and customer organizations — eroding sales and profits, while adding significant costs.


By collecting, analyzing, and applying insights from operational IIoT data to maximize uptime and extend the useful life of their equipment, manufacturers can effectively guard against damage to their equipment, minimize their risk exposure, and avoid disrupting the end-to-end supply chain.


Ready to explore how data-driven maintenance and repair can impact your production outputs and costs?

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