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The stakes are high in the industry segment. Machines and installations have to increase performance, with increasing functionality and complexity.  To achieve maximum yield, end users want to keep downtime to a minimum and expect immediate responses to malfunctions. All of this without having to keep an extensive range of spare parts in stock, because that is too much 'dead capital'. And end users hardly employ technical people nowadays, because with the current quality level of machines and installations they had too little to do.

So if things do go wrong, the technical distributor must immediately take action, to deliver the right replacement parts or the right expert to solve the problem. The modern value-add distributor keeps track of technical developments and knows about the possible phasing out of components, so that he always has the required mechanical and electrical supply ready. All this including advice on how to successfully replace and integrate components.

Ideally, OEMs and end users want to eliminate these risks altogether and prevent malfunctions - no matter how small the chance they occur. Maintenance should take care of that, organized as efficiently as possible. Corrective maintenance is basically out of the question, because too expensive because of the downtime that will still occur. Preventive maintenance seems to be an attractive alternative, but it is also not optimal. Because sometimes it is too late and in other cases perhaps too early: then, as a precaution, parts are being replaced that functioned well and could have lasted for a long time. Situation whereby the maintenance interval is set incorrectly based on  too general information or too little knowledge.

The crux lies in determining maintenance intervals. Nowadays it can be much smarter with the help of big data. Think of condition monitoring, whereby an upcoming malfunction can be distracted from certain signals - such as increasing vibrations or increasing energy consumption. On the basis of that information it can be predicted what the optimal moment for maintenance is. Thus predictive maintenance helps to efficiently minimize downtime. Technical value-add distributors have the knowledge and technology in house to organize predictive maintenance properly.

All of this contributes to the reduction of the total cost of ownership. Enough reason for OEMs and end-users to maintain close relationships with their technical value-add distributor.

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