Our client is one of the largest electric utilities in the world. The maintenance of its electric lines as well as the towers, sub-stations, and other critical infrastructure that make up the operator’s network presents a significant challenge costing billions each year and engaging many thousands of skilled employees.
To manage this maintenance activity, our client has put in place a number of policies that guide teams in when to repair, renew, or replace assets in the field. Regular preventative maintenance can help extend the lifetime value of an asset by years, significantly reducing the required CAPEX; however, this preventative maintenance comes at a cost, too, and so optimizing the maintenance policies is a key concern for efficiently deploying the client’s OPEX budget.
Our client is challenged to balance the investment in the maintenance of the existing network against the costs of replacing assets in the network as part of an asset investment plan. They seek to optimize their maintenance policies to reduce OPEX expenditures, extend the life of their assets, and make the best use of their resources – financial and human – engaged in the maintenance of the transmission network.
We created a Simulation Digital Twin that modeled the client’s asset base and maintenance policies. The digital twin was realistic and included not only all of the assets and policies but also the human resources available to complete maintenance of the asset base and the financial, human, geographic, logistical, and environmental constraints on the maintenance of the company’s assets.
The client could test assumptions and run unlimited simulations of different maintenance policies in order to determine which would be optimal under the particular constraints of their business.
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