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Maintenance Management in Times of Crisis: 3 Challenges


It´s no secret, that in many companies, when facing a financial crisis, one of the first sacrificed, is maintenance. It is a risk that “possibly” will materialize many months ahead, which is why it is an attractive option for adjusting budgets in the present. However, the risk assumed can have dire effects for extended crises; when any of these risks materialize, machine intervention is required and usually it has significant impacts on production plans with direct maintenance costs that could even reach, four times its equivalent preventive maintenance cost.


Additionally, in companies whose economic activity is intrinsically related to machinery, there is a high correlation between maintenance performance with multiple business variables such as: operational and logistical variable costs, productivity, accident rate, quality, environment, stock-outs and engagement. In other words, an inadequate assessment of the related risks of drastically lowering maintenance budgets can generate even greater damage than what is being mitigated.



But, where to start? What would be the initial list of challenges to discuss, at a strategic level that a maintenance manager should evaluate? Below are three proposals that alleviate maintenance management on the plant floor during periods of crisis:


1. Risk vs. Resources


The first challenge proposed refers to the relationship between how much resource are invested per machine vs. which is the risk assumed. If we had this information, we could validate if we are investing the resources where we really need them and we would better understand where we are taking the greatest risks. Now, it is common for operations to have information regarding where they are investing their resources (technicians' time, inventories and money), but it is not so frequent to have a framework for assessing the risk assumed.


To do this, a relatively simple way corresponds to assessing historically, what has been the fulfillment of plans vs. Reliability per machine (If you have RCMs, the result is much more accurate). Additionally, variables of machine criticality, inspection efficiency (generated inspection derivatives) and predictive maintenance (predictive derivatives generated according to the expected MTBF) can be taken into account. This can help determine the quality of the plans, the quality of the execution, the probability of failure associated with the execution carried out and its possible impact.


With these elements on the table, we could make a projection of expected maintenance reliability vs. Resources availability. However, it is not enough to understand the scope of company´s results. To do this, we must assess the correlation of this reliability with the output variables of the business and define goals that make sense with the company's needs.


2. Business goals vs. Maintenance goals

This brings us to the second challenge. What are the goals to achieve and how do they correlate with the business priorities? At this point, we must highlight teamwork of the entire organization, not only within the technical area ... we need to have an open discussion with finance, logistics, human resources, purchasing and sales, which is why, it is the most difficult challenge, for a maintenance manager to deal with. Finding a solution to the company requires thinking outside of the box and finding innovative ways to measure maintenance management focusing on the business needs.

At first, it is important to have a clear correlation between machine reliability and the main business variables. Once we understand it, we can define a "risk appetite" that we are willing to assume as a company.

One option is to define company goals packed specifically for maintenance, so that we are allowed some flexibility to accommodate unforeseen events, but which forces us to obtain company goals, where the area´s results, are inputs to diagnose and correct the course. For example, if one of my maintenance goals is maintenance cost + variable production cost, it allows me to have the flexibility to compensate for my deviations within the mix, according to unforeseen events, but I am obliged to comply with the sum of the two.


So that this compensation can be done correctly, the indicators of the area serve as an input to diagnose and optimize the correlation between the two financial variables. This is an important change in paradigms, since indicators such as machine reliability is a mean through which result achievement is obtained, focusing efforts on company goals directly.


This, in turn, eases the pressure of results by “silos” (where particular goals are prioritized over general ones) and promotes transparency of information and feedback, since a greater objective is pursue and requires constant communication between the areas.


3. Decision-Making agility at the shop floor

Once the goals have been defined and socialized, we must ensure mechanisms so that people focus on diagnosing and on capitalizing the main opportunities to achieve the company's objectives. We must ensure agility in decision making and transparency in information management. This implies, letting our people work. It seems easy, but it is very difficult.

Our job is to ensure that our maintenance managers clearly understand what are the goals to achieve, make their job easier and getting out of their way. For this, ideally, we should define and centrally generate, conceptual frameworks, dashboards, mathematical models or reports that facilitate, diagnosis and control for maintenance managers and leaders ... I repeat, THAT FACILITATES FOR THEM ... is not a tool for control and micro-management for us to review and press for result ... it is a tool for diagnosis, critical thinking and problem solving. Periodically, we offer guides, clarifications and support for the process to improve their decision-making capabilities.



Conclusions


In summary, our efforts as maintenance directors should focus on (1) generating a conceptual framework that supports the development of critical thinking and context understanding, (2) building clear standards of what is expected to be achieved at the business level and how our teams can openly contribute to the process and lastly, (3) facilitate and promote decision-making at the shop floor. If we positively favor complexity within our production processes, we generate more autonomous committed teams and allow innovation to flourish, finding solutions that we could never think of by ourselves.

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