How to Reduce Production Cost with Machine Health Monitoring System

Recently, there has been a miserable upward trend in manufacturing production costs. Across each industry, products are now becoming more complex as well as costly to produce. To start and stop machines always creates additional risk for malfunctions as well as failures and the longer a machine is down, the more cost the company loses while it’s not producing.

One of the really great results of the growth of IoT is the fact that machine data can be utilized to restrict the operational costs and influence of downtime, both planned as well as unplanned. This is also called predictive maintenance.

Manufacturers today can work faster and smarter by using machine health monitoring systems to reduce the production costs in the following ways:

1) Prioritizing Predictive Parts Replacement and Repairs

Previously, manufacturers would depend on reactive maintenance and other maintenance strategies. You can better imagine that servicing of machines when they broke down leads to a tremendous cost, in terms of unplanned downtime and the potential effect to other parts of the machine. This can increase costs significantly.

Over time, companies move away from reactive maintenance and go with implementing preventive maintenance methods. With a machine health monitoring system that is made up of machine sensors as well as IoT algorithms to get insights from machine health data – manufacturers can assume when a machine will require a specific part. They can then take the required measures to get it without experiencing additional costs for accelerating the process. Sensors on machines can easily track machine health indicators likewise vibration and power utilization and advanced IoT algorithms enable you to monitor real-time from any time, anywhere. Basically, you get a much better view of what is and not allowable in a machine’s current state, allowing you to prioritize repairs and maintain downtime to a minimum

What’s more, the machine health data from Hiotron’s Industry 4.0 Solutions is simple to export and share. Hence, it could be sent easily to off-site experts who could then assign technicians via tricky repair processes virtually.

2) Reducing Waste

Many industries are having manufacturing process through batch production. If a machine failure were happened during batch production, the manufacturing company would lose a whole batch of goods. With machine health data as well as insights, though, the manufacturer would know earlier which equipment’s or parts are in poor condition. They could easily replace the part before keeping in the batch, saving themselves from wasting a complete batch and product. Sub-optimal operation that is not recognized can result in wasteful production. Raw material, energy, labor costs, as well as machine time get wasted in such incidents. Predictive maintenance systems can reveal concerns that can result in waste before they occur.

3) Implement an Equipment Upgrade Plan

Many manufacturers still depends on calendar-based preventive maintenance methods, where equipment maintenance is fixed at regular intervals as per the time or usage. Scheduling this way, though, leaves storerooms filled up with excess parts and pieces, wasting important space and driving up costs.

Let’s say you’re operating a predictive maintenance model. In that you may still notice some unplanned downtime if equipment breakdown without warning signs. In this case, you require equipment upgrades in order to acquire better and more sensitive sensors to keep up to cut down unplanned downtime.

This means it’s time to concentrate on more tough parts. If your predictive or preventive maintenance recognizes parts, machines, or processes that keep failing under heavy stress, that means it’s time to upgrade to equipment that operates better under stress.

Using a data-based machine health monitoring system and meter-based PM schedule instead, manufacturers can understand exactly when to repair or replace specific parts. The perspective also provides a complete understanding of the health and life cycle of equipment. This, in turn, slows down the rate of parts utilization, removes the requirement for excess inventory and minimizes the chances of unplanned downtime. At the same time, costs are reserved throughout the process. By integrating your equipment upgrade plan with data from predictive maintenance, you’ll be capable to build parts, equipment as well as processes that fail less frequently under high speeds. You’ll go through less on maintenance and finish up with more productive and more effective machinery.

4) Monitor and Eliminating Unplanned Downtime 

As per the Wall Street Journal post, “Unplanned downtime costs industrial manufacturers near about $50 billion annually.” Utilizing predictive maintenance to restrict this cost is crucial in highly competitive manufacturing industries. Worse yet, 82% of manufacturers go into the concern at least once a year.

In as much that scheduled preventive maintenance can assure that machines operate smoothly most of the time, analyzing machines digitally gathers reams of data that, when monitored , will show patterns on any given machine. This type of pattern detection, depends on historical data, can assist to rectify a machine that is likely to notice an interruption and for which maintenance can be planned proactively.

With those insights, manufacturers can also scheduled shutdowns for machine repairs. Additionally, for assets that trigger downtime based on factors likewise temperature, vibration, sound, heat as well as light, you can place automatic alerts to inform operators when the deviation is assumed to occur. That way, you can take further actions to reduce those anomalies. Since downtime is completely unpredictable on the types of assets and the type of operations on the shop floor, designing your own data-based maintenance plan is essential. 

Reducing unplanned downtime, at least as it concerns to the functioning of the machines, is a large cost saving and will save delays to market that will also impact the bottom line.

The more you know about the health of your machines, the better you’ll be at resolving them rapidly, easily and more efficiently.