How can smart supply chains reduce unplanned downtime?

Smart supply chain concepts like Supply Chain 4.0 are set to redefine manufacturing and reduce unplanned downtime. They will bring in advanced automation, real-time data analytics, and AI systems, marking a new era in the industry.

This article offers an in-depth look at the major impact of smart supply chains. It highlights how these chains can effectively eliminate unplanned downtime. As well as, supporting the growing use of predictive analytics, IoT sensors, and interconnected systems.

Manufacturers may learn a lot about their machines' status and efficiency in real time with the use of these technologies. With this kind of planning ahead of time, you can perform proactive maintenance strategies that prevent unexpected machine downtime and keep costly disruptions to a minimum. In addition to this, it offers the chance of reaping greater benefits throughout the supply chain.

Unplanned downtime in manufacturing

Most manufacturers would agree that machine downtime continues to have a massive influence on their business. Of course, some downtime is inevitable to allow for essential maintenance. However, unplanned downtime will have a negative impact on production, poses a threat to profitability, and has the potential to result in the loss of business and reputation.

Machine downtime is more common in sectors where complex machinery is used, such as the pharmaceutical, aerospace, and automobile industries. Some estimate that, on average, factories are losing 25 production hours a month to unplanned or unscheduled downtime¹. This equates to more than a full working day and the loss of almost two weeks a year.

The cost of unscheduled downtime

The cost of machine downtime is increasing at a rapid pace. This is mostly due to spiraling inflation, production lines running at full capacity, and stressed supply chains. A 2023 survey conducted by ABB reveals that the cost of an hour's downtime in a car plant now exceeds $2 million. This marks a major rise from the previous value of $1.3 million recorded in 2019-20.

In the oil and gas sector, the hourly cost of machine downtime has more than doubled to $500,000².

It is possible that even these estimates are conservative. This is because the true cost of machine downtime is difficult to predict. For instance, the wages of the maintenance team, the cost of new parts, or the premium for expedited shipping. As well as, the wages of employees who are unable to work, and possible penalties for breach of contract.

Despite the negative impact on manufacturing, 2/3 companies still rely on run-to-fail or time-based maintenance strategies. In other words, maintenance on machinery is strictly on an as-needed or schedule-based basis. This is true irrespective of how urgent the need for repair actually is³.

In the long run, neither of these scenarios are sustainable. Manufacturers need to transition from an unplanned to a planned approach, anticipating downtime well in advance.

Predictive maintenance

Scheduled machine downtime allows manufacturers to run thorough equipment inspections. They can replace worn-out components and perform preventive measures, such as cleaning, lubrication, calibration and software updates. It also provides time to assess and manage spare parts inventory and train engineering teams. As a result, this means they can keep up with the most recent maintenance procedures and technologies.

Furthermore, planned downtime offers a perfect chance to incorporate new technologies or optimise existing processes. This can lead to an improved overall equipment efficiency (OEE), increased product quality and lower energy use.

A predictive maintenance approach is a fundamental principle of Industry 4.0. Industry 4.0 was first introduced in Germany. It aims to transform manufacturing by integrating advanced industrial digital technologies (IDTs), the internet of things (IoT), artificial intelligence (AI), and automation. It marks a shift towards smart, interconnected factories where machines, processes and systems communicate and work together seamlessly.

To effectively deploy a predictive maintenance strategy, it is crucial to embed sensors at every level. In addition, processes and systems must be integrated, and widespread automation should be used.  These measures enable the collection of valuable data that serves as the foundation for predictive maintenance.

Recent technological advances in computing and sensor technology, coupled with powerful data analytics and AI, have opened new possibilities for manufacturers. They can now gain unprecedented real-time insights into machinery health and performance. However, the potential for these breakthroughs goes beyond enhanced maintenance.

Beyond predictive maintenance

Supply Chain 4.0 extends the principles and benefits of Industry 4.0 on the factory floor across the entire value chain. It aims to create intelligent and responsive networks by focusing on real-time data, visibility and connectivity between suppliers, manufacturers, distributors and customers.

Demand forecasting

Demand forecasting is one example. Historically, anticipating future demand for products or services has been based on sales data, statistical models, and market patterns.

However, this approach lacks agility and struggles to adapt to sudden shifts in consumer demand. Therefore, leading to overstocking or understocking. Overstocking ties up capital and storage costs, while a lack of stock can result in unhappy customers and lost sales.

Advanced analytics and AI algorithms are used in a smart supply chain model to analyse vast datasets. These algorithms take into account customer behaviour, social media, and market trends. By identifying patterns, they are able to provide more accurate predictions.

Real-time visibility into market dynamics enables manufacturers to make immediate adjustments to demand forecasts as conditions change. By plugging this information back into the shop floor, machine capacity and availability can be better managed. It is important to always consider planned maintenance outages in this process.

Inventory management

Another area that benefits from Supply Chain 4.0 is inventory management. Industry 4.0 allows for the reordering of spare parts for machines before a planned outage. Similarly, Supply Chain 4.0 utilises AI and machine learning to constantly check stock levels. It then generates reorder requests automatically when inventory reaches a predefined threshold.

Automation is a game-changer when it comes to routine reordering tasks. By eliminating the need for manual intervention, it significantly reduces the risks of errors. As a result, this means that stock can be replenished at the right time, without any hiccups, reducing unplanned downtime.

And for those who have suffered the pain of a lost or misplaced order, smart supply chains extend to route optimisation and order tracking. Smart supply chains allow for dynamic adjustments to keep routes optimum even when variables change frequently.

Logistics managers can now monitor shipments minute by minute through real-time tracking using IoT devices, GPS, and advanced RFID tags. This allows for continuous optimisation of transport routes based on current conditions. As well as ensuring timely delivery of goods to the correct recipient, every time to reduce unplanned downtime.

Delivering Supply Chain 4.0

Industry 4.0 has paved the way for smart factories and predictive maintenance regimes. Smart supply chains are set to redefine manufacturing by using advanced automation, real-time data analytics, and AI systems.

When there is machine breakdown, smart supply chains will help resolve issues quickly and restore operations to reduce unplanned downtime. The interconnected nature of a digital supply chain, coupled with enhanced visibility and transparency, allows for the rapid detection of faulty parts or systems. As a result, a streamlined process is triggered for sourcing replacement parts or equipment. This ultimately helps to reduce downtime in manufacturing and keep assembly lines moving smoothly.

Delivering Supply Chain 4.0 involves more than an investment in the latest AI and sensor technology. To ensure the successful adoption of smart supply chains, it is crucial to focus on upskilling, collaboration, and strategic partnerships. This includes working closely with global suppliers like EU Automation, logistics partners, and technology providers.

Although it is a major commitment, the benefits of such an ecosystem go beyond just managing machine breakdowns to reduce unplanned downtime. It provides manufacturers with a way to thrive in the face of increased uncertainty and ongoing supply chain disruption.