Like every year, LuxLicht, a big electric utility company in Germany, is holding its annual board meeting. It’s been a tough year for LuxLicht so far, and everyone feels the pressure of a more dynamic, complex, and data-driven environment. Every board member faces challenges in their specific area of responsibility and addresses them during the course of the meeting.
Alex, the CFO of the company, starts the meeting with an overview of hard facts regarding the financial situation. He points out that costs for aging infrastructure, grid expansion, and system integration for renewables are still rising heavily. The limited budget provided for CAPEX/OPEX is not enough to cover all crucial projects equally. Alex insists on searching for new solutions that can help reduce existing costs and therefore use the budget more efficiently.
COO Peter confirms the need for efficient cost optimization in the transformation process towards green energy and integrating decentralized renewables and their fluctuating electricity output into the current grid infrastructure. Further, he points out that one of his major responsibilities is to meet current and future energy demand. Peter emphasizes his doubts regarding the status quo of the business transformation and his ability to provide a robust, reliable energy infrastructure with decentralized assets with uncertain electricity output.
After describing the different aspects of the asset landscape, including the drive towards cost reduction, Bob, the CAM of LuxLicht, wants to discuss the last appointment he had with his maintenance staff where they evaluated overdue maintenance projects. Together, they reviewed a spreadsheet that contains preventive maintenance plans for a list of assets. This is the primary way LuxLicht has handled asset maintenance over the years. Nevertheless, Bob must acknowledge that this method leads to a high risk of under- or over-maintaining the assets. Having a methodology capable of individually reviewing assets and proposing tailor-made maintenance strategies would save a lot of money.
John, the CEO, is delayed in joining the meeting and apologizes to his colleagues. There was an important emergency case that had to be solved first. A local grid collapsed a couple of hours ago, and a team of employees was sent there to look at the issue. They found a bird’s nest on top of the transmission lines that caused the problem. One of the older workers climbed a ladder to remove the bird’s nest but fell and broke his leg. Luckily, the colleague is in good health and is now recovering from the accident in the hospital. John admits he has underestimated the current risk level of his employees and wants to achieve a better and safer work environment to limit further incidents.
Sussan, the CHRO, feels sorry for the injured employee and hopes that he will recover soon. Nonetheless, she reminds the board members that her department has been urging attention to the “war for talent” quite extensively. Changing demographics and the rapidly aging workforce coupled with a decline in available talent means incidents like this may happen more often in the foreseeable future. Sussan also recommends ensuring that knowledge transfer between older and younger employees works smoothly and the company takes steps to close the gap of skilled employees with an effective talent management initiative.
At the end of the annual board meeting, everyone agrees that these challenges must be addressed as soon as possible. Just one question remains: How can LuxLicht overcome all these challenges effectively?
Data standardization and harmonization
Companies are gathering an overwhelming amount of information in multiple asset classifications leading to the problem of a complex company structure. With the ongoing shift towards greater integration of decentralized renewables and other M&A integration projects, there is a constant need for one, consistent, common enterprise asset management (EAM) data model structure. Even if everyone is using the same asset management tools, there are always different definitions and nuances that make integration an insurmountable obstacle.
Therefore, the number one priority is to clean up the existing data model. This could be achieved with a customized machine learning solution that analyzes various patterns across different data models and consolidates everything into one model. Then, it becomes possible to decide which processes and current and future products are crucial to the portfolio. Finally, with trustworthy data across the network, companies can leverage the potential of their entire asset landscape by implementing a holistic EAM suite that effectively utilizes the accompanied cloud solutions. This will enable managers in the electric utilities segment with a much faster and more efficient decision-making process.
Collaboration and management of distributed assets
As the world becomes more dynamic, complex, and data-driven, the energy industry must react to changing influencing factors and framework conditions through different digitization processes. Essentially, all asset-intensive industries face disruptive challenges from changing business models and higher cost pressures.
With the constant rise of prosumers in the energy business, the competition in this segment is even stronger than ever. Nevertheless, new revenue streams can be created by implementing novel business models such as maintaining micro-grids and renewable energy sources for prosumers, especially those deployed by retail households and small industries. Revenue growth from new business models will help maximize returns while facing limited budget resources (CAPEX/OPEX).
A holistic asset network offers the opportunity to connect and collaborate with different stakeholders in the entire ecosystem. By collaborating with various manufacturers, operators, customers, public utilities, and service suppliers across the network, utilities have all the necessary asset information, like specific asset conditions and asset degeneration. Therefore, crucial information about different assets along the energy supply chain can be incorporated into future investment decisions and support management along the cost-optimization process.
The evolution of different maintenance routines over the past several years is also reshaping the energy sector, with the main objective to ensure asset uptime and increase profitability by avoiding unplanned and unnecessary maintenance in the energy supply chain. Decision making around maintenance strategies for an organization’s asset base can be instinctive, driven by intuition rather than hardcoded facts. Therefore, the risk of under- or over-maintenance plays a significant part in the process.
Well-established strategies, such as reactive and preventive maintenance, are already broadly used in energy markets. Hence, more advanced methods like predictive maintenance, where time-based condition monitoring provides many insights into asset behavior, can help companies achieve their objectives beyond cost optimization and further increase agility and optimal resource allocation. Predictive maintenance does maintenance only when it’s needed. It uses data science- and rules-based approaches to reduce unplanned failures and the overall number of maintenance actions.
An efficient asset and performance management strategy enables enterprises to adopt a risk-based approach to asset management, enables better decision making for maintenance planning, and reduces the probability of asset failure. Due to the segmentation of assets based on risk, criticality, impact, and environmental factors, it is possible to determine the best maintenance strategies at the lowest cost and risk to improve reliability. With tools like reliability-centered maintenance (RCM) and failure modes and effects analysis (FMEA), an appropriate asset strategy can help identify specific assets that need a change in maintenance strategy. With all this information at hand, it is feasible to calculate risk levels and determine alternative maintenance strategies. Strategies can include condition-based or predictive maintenance, making the most out of the existing asset landscape.
Organizational change management
Since most technical approaches are already feasible and mature, the implementation process of these projects often fails due to the lack of effective organizational change management (OCM). Many ERP implementations have discovered the hard way that people affect the success of an implementation. If the organization is not ready for the transformation, the project will miss timelines and come in over budget.
Organizations regularly follow a commitment curve on every ERP implementation. OCM educates the end users and leadership about the changes that are coming their way, using best practices to teach and encourage the organization about how to understand and accept the changes. Only after all individuals accept the changes can the organization say it has reached full adoption. OCM works at the individual levels through communication, involvement, training, support, reinforcement, and more to build consensus throughout the organization.
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