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Aftermarket activities, including spare parts management, used to be an afterthought for manufacturing companies. This is no longer the case as a considerable amount of business’s revenue comes from the services and products that are provided after the
sale of the original products; hence, the aftermarket.
Moreover, customer expectations regarding support for their purchased products or services have grown significantly as a result of servitization.
In today’s competitive market, customers demand fast delivery, quick and accurate responses to their inquiries as well as “real-time alerts when
technicians are on their way to the job site/residence or that technicians have full access to the repair information and parts that they need to complete the job.”
Most companies within the manufacturing industry rely on some sort of system to manage and track their assets, keep a record of their financials along with their manufacturing, sales, and human resources. Two software systems that are typically employed for such purposes are called enterprise asset management (EAM) system and enterprise resource planning (ERP) system. While EAM is used for handling the management and tracking of assets, ERP is often preferred for recording financials, manufacturing, sales, and human resources operations. Another trend for keeping up with the staggering customer expectations is to integrate these two systems to have a bigger and clearer picture when it comes to asset management. The idea behind it is if businesses can see and track the availability, the whereabouts, cost, and duration of spare parts activities, all in one system, they can reach better decisions in a shorter amount of time. Thus, making customers happier with faster and better service.
The emergence of new technologies such as industrial IoT, machine learning, and AI has also played an essential role in making the integration of these two systems possible. The combination of EAM and ERP systems used to be a complicated task but with the availability of data that is provided by such technologies these two systems are easier to combine in a way that is useful for businesses.
Therefore, it’s possible to say that we will keep hearing about the big data, machine learning and AI in the future; in general and in the context of providing the manufacturing companies with a more streamlined system to manage and analyze their assets.