
Unlike a well-oiled machine, unplanned downtime in industrial manufacturing has costly repercussions. From missed customer deadlines to production delays to financial losses, issues with equipment downtime in industrial manufacturing run the gamut with significant impact.
The average manufacturer faces 800 hours of equipment downtime per year — more than 15 hours per week. Unplanned downtime costs industrial manufacturers as much as $50 billion a year.
Equipment maintenance approaches have evolved from reactive measures to preventative ones. Reactive measures meant frequent disruptions and high repair costs, while the move to preventative measures minimized unexpected downtime and breakdowns. Preventative measures include scheduled inspections and part replacements. The process has evolved even more today with predictive and prescriptive maintenance, which leverage advancements in AI, loT, and real-time data analytics. These tools can help organizations predict potential failures and prepare for the next steps in their maintenance schedule.
In light of today’s hyper-competitive global market, the stakes for manufacturing operations are higher than ever before. Downtime risks include revenue loss, increased operational costs, customer dissatisfaction, safety risks, and compromised quality.
Below, we address the limitations and challenges of traditional approaches to equipment management:
High Downtime
The unplanned downtime of reactive maintenance causes missed deadlines and costly production delays, contributing to lost revenue or sales opportunities and tighter profit margins.
Lack of Transparency
Without a clear, complete picture of real-time data, managers have difficulty identifying potential equipment issues early, and they make decisions based on dated or inaccurate information.
Incomplete Record-Keeping
Unlike Gomocha’s digital platform, siloed systems and paper-based documentation hinder data accuracy and accessibility for managers and various teams across an organization.
Higher Maintenance Costs
Inefficiencies and operational costs compound when organizations over-maintain or miss essential maintenance intervals. Companies may waste products or materials. Maintenance costs can also involve labor and customer service risks.
Digital transformation in equipment management is necessary for effective and efficient modern industrial manufacturing. Centralized data is pivotal in today’s industrial manufacturing to ensure timely, high-quality service, enhance predictive capabilities, boost efficiency, and streamline compliance and reporting. The Gomocha Field Service Platform keeps everything on track, supporting real-time task oversight through a centralized view of tasks and workflows.
Here are the key components of uptime optimization:
Service History Tracking
Service history tracking is vital for managers to spot any issues or trends based on the equipment’s data history. They can also gain a deeper understanding of the effectiveness of repairs and identify the root causes of problems. Equipped with this ability, managers can implement proactive measures. With Gomocha, technicians have access to real-time data and customer history, so they can offer tailored advice and upsell opportunities.
Real-Time Monitoring Capabilities
Instead of mere snapshots of data, manufacturers can track equipment performance continuously. Critical metrics alert teams to potential issues before they lead to downtime. The Gomocha Planboard supports real-time task oversight, improving efficiency and reducing delays.
Data-Driven Decision Making
Leveraging data helps organizations make the most informed decisions on resource maintenance, allocation, and optimization. With Database Manager’s centralized asset intelligence efficiency in Gomocha, teams have clear oversight of proactive maintenance and smarter resource allocation. All their equipment information is in one location and always up to date, helping to avoid conflicting records or guessing games.
Organizations need a well-organized strategy for optimizing uptime in modern industrial equipment management. Steps include digitizing asset management and choosing the right tools.
Companies also need to change management considerations, including leadership commitment, stakeholder involvement, and any policy and procedure shifts. Finally, organizations should consider team adoption approaches: training and skill development, continuous support, and incentives.
Managers can measure the success of these changes through factors like improved asset reliability, greater uptime, and operational and financial metrics. Success can also include better-informed decision-making, higher employee productivity and satisfaction, and customer satisfaction.
Keep production running and cut downtime costs. Manufacturing uptime delivers tangible results in today’s fast-paced, ultra-competitive market. By counting on advanced digital programs like Gomocha, industrial manufacturers can avoid costly downtime.