The aid of technology has changed supply chains in countless ways, with one of the most prominent drivers of change in recent years being predictive maintenance.
As supply chains get more complex, they require greater oversight and care. As such, with machines and robots and a variety of complex technology comes numerous benefits yet also potential points of weakness. If any of those key components in a supply chain malfunction or breakdown, they can bring it to a complete halt.
An article from GTI Predictive explains,
The cost of failing to manage supply chain systems effectively can be enormous . . . Taking all possible variables into account is critical to maintain steady prices and stay profitable . . . In 2018, supply chain interruptions occurred at a record rate of nearly 30%, costing billions in delays and wasted product.
This is where predictive maintenance comes in. It makes sure machines and robots in a supply chain are well maintained.
This week’s article by Morai Logistics explains just what predictive maintenance is and how it’s transforming supply chains.
What is Predictive Maintenance?
Achieving predictive maintenance comes by having a mindset and the technology to help fulfill it. That mindset involves emphasis on ensuring that machines and robots in a supply chain are actively maintained. Additionally, on the technological front, this approach is accommodated by IoT devices such as sensors that collect data and monitor conditions.
In turn, machine learning is the final component of predictive maintenance. It allows computers to analyze the data collected and makes predictions based off it. With that in mind, just what are the benefits this brings?
Proactive Supply Chains
Predictive maintenance means a foundational shift to how supply chains operate. They don’t have to be as responsive to what happens to them. Instead, predictive maintenance transforms a supply chain into being proactive. For example, it tracks machine health, resulting in machines that are in better condition. That is to say, a supply chain doesn’t have to wait for something to go wrong with a machine to provide it upkeep. Consequently, supply chains can tackle potential problems before they can even become problems.
With sensors continually attached to the machinery and robotics in a supply chain, data is regularly being collected. Above all, what this results in is a smarter, more efficient supply chain. In addition, as we’ve mentioned before, machine learning and artificial intelligence more generally does not perform well without a sufficiently large pool of data. By having big data to pull from, computers, via machine learning, can make more precise models and accurate forecasts. Therefore, predictive forecasting doesn’t only ensure better kept machines and robots but more knowledge to work with in general.
By regularly scheduling maintenance on machines and robots as a result of predictive maintenance, the chances of either of them having issues becomes minimal. This is critical, as it means supply chains have a higher likelihood of running smoothly without disruptions. That in turn means more cost-reduction and improved earnings.
Supply Chain Dive explains this in their article on predictive maintenance,
The most common goal of predictive maintenance is improved uptime, with 51% of respondents in a PwC survey last year saying this was their organization’s reason for adopting the technology.