How to Boost Manufacturing Productivity Using ERP and IoT?

One of the primary factors leading to productivity loss is the forced downtime of equipment. The most frequent cause of such downtime is the malfunction or complete breakdown of manufacturing equipment.

For instance, mining and metallurgical industries face losses amounting to hundreds of thousands of dollars annually due to unplanned equipment downtime.

Utilizing ERP systems for planning, advanced planning strategies, IoT technologies, and relevant analytics significantly reduces potential equipment failures and optimizes downtime, thereby enhancing manufacturing efficiency.

Why are ERP and IoT necessary in maintenance planning? 

ERP systems are powerful tools that integrate various business processes and functions, including finance, inventory management, human resources, and technical maintenance.

The integration of predictive maintenance with ERP systems means combining the capabilities of predictive repair strategies and ERP functionalities to manage technical maintenance within an organization.

Utilizing ERP systems integrated with IoT allows for:

• Implementing the most advanced (predictive) repair strategies.
• Receiving reliable data about equipment and manufacturing processes in real-time.
• Planning and optimizing maintenance schedules based on the availability of necessary raw materials and supplies derived from the gathered data.
• Using instructions to mitigate errors and insufficient personnel qualification.
• Avoiding unforeseen production process downtime.
• Enhancing the productivity of the manufacturing system by reducing the time and amount of readjustments.

Improving Maintenance Planning Strategies 

At the heart of maintenance planning always lies a chosen strategy. The maintenance and technical service strategy is a general approach to ensuring maintenance and its support, aimed at minimizing unplanned equipment downtime. The following are the primary repair strategies classified:

• Emergency; Reactive maintenance is the simplest form and involves maintenance and repair of equipment only after it has broken down.
• Scheduled; In this case, planned maintenance tasks are performed according to a calendar-based or usage-based schedule. An example of scheduled maintenance is an oil change in a car engine after a certain mileage.
• Condition-based; This strategy uses sensors to periodically assess the condition of equipment. The scope and timing of repairs depend on the equipment’s condition monitoring results.
• Predictive. The most advanced strategy—predictive repairs—allows for performing tasks only when necessary, ensuring a high probability of uninterrupted operation. The predictive planning strategy results in saving time and financial resources on unnecessary maintenance works, significantly reducing the risks of malfunction and equipment failures. At the same time, such a strategy requires:
• Significant costs at the implementation stage.
• The use of technical diagnostics tools (IoT).
• Qualified diagnosticians and analysts to develop the predictive model.
• Storing and processing large volumes of statistical data, for instance, within an ERP system.

The Advantages of IoT

• IoT allows for real-time equipment monitoring. Condition monitoring systems based on IoT are more accurate compared to traditional methods. They enable continuous data collection and sophisticated analysis, thus identifying potential problems at early stages and making appropriate maintenance decisions before equipment failure occurs.
• Based on the obtained data, an optimal set of maintenance operations can be selected. Thus, the time spent on equipment repair can be optimized, unnecessary maintenance can be avoided, and the lifespan of the equipment can be extended. By predicting equipment failures in advance, condition monitoring systems based on IoT can help businesses reduce downtime, saving significant funds.
• An integrated ERP & IoT solution allows for reducing repair and spare parts costs. Condition monitoring systems based on IoT can help companies reduce maintenance expenses by planning for the selection of necessary spare parts, providing clear instructions, and saving labor time on repairs.

Who is the Predictive Strategy Suitable For? 

When determining the correct maintenance strategy for equipment, two main factors exist:

• The cost of equipment failure What impact does equipment failure have? What losses (including financial, reputational damage, safety threats, and impact on other processes) could it lead to?
• The ease of implementation and support of equipment monitoring The implementation and support of an integrated ERP & IoT solution based on a predictive strategy entail certain costs. If the cost of implementing such a system turns out to be higher than the cost of preventing failure, perhaps a simpler maintenance strategy can be chosen for this equipment.

Industries Most Likely Suitable for ERP & IoT:

• Factories and Manufacturing Failures and unplanned equipment downtimes at manufacturing facilities can be very costly for businesses and lead to a halt in the manufacturing process. Technologies such as infrared thermography, various sensors for vibration, humidity, pressure, and others are used to predict the need for equipment repair or replacement. According to a McKinsey report, predictive maintenance based on IoT helps reduce equipment maintenance costs at the plant by up to 40 percent.
• Oil and Gas Sector According to the Russian Ministry of Energy, in 2021, there were 10,088 ruptures registered on main pipelines in Russia, of which 5,880 cases were recorded on oil pipelines. A greater amount of oil spills occurs during its transportation through pipelines due to their wear or mechanical damage. Often, oil and gas companies’ equipment is located in places that are not easy to access. Instead of regular visits to check the condition of machines, technicians can use predictive maintenance to determine when repairs are genuinely needed. Repair process automation, timely analytics, and maintenance can significantly reduce accidents.
• Healthcare and Hospitals Predictive maintenance can be applied to a wide range of critical equipment and systems in hospitals. Incorrect calibration of such equipment and downtime can cost human lives. Medical institutions can significantly improve the quality of their service by using monitoring sensors for equipment. With a limited budget, frequent replacement of necessary medical equipment becomes an unbearable burden for them. Predictive maintenance can extend the life of expensive equipment.

About the author: Inna Sitnikova, a seasoned engineering manager with over 10 years of experience in SAP product implementation. Currently leading teams at EPAM Systems, Sitnikova specializes in enterprise asset management and customer service solutions, working across industries like manufacturing, telecommunications and logistics.