Five basic maintenance automation mistakes to avoid

Article By : Bryan Christiansen

As businesses race to realize the competitive advantages predictive and prescriptive maintenance brings to their business, there are five common implementation errors to avoid.

Maintenance automation has lagged behind automation in other industries, confined largely to automated planning and scheduling tasks. Yet, the growth in IIoT and the maturity of cloud-based systems have created momentum for change, encouraging investment in real-time data capture, storage, and analysis using artificial intelligence and machine learning.  Yet, as businesses race to realize the competitive advantages predictive and prescriptive maintenance brings to their business, there are five common implementation errors to avoid.

Error # 1 – Not defining a clear goal

Implementing maintenance automation is not a simple task decoupled from the rest of the business. It requires a fundamental change in how the maintenance, operations, and stores department must work. It also has implications for the finance and human resources functions.

Creating a business case and intra-department alignment is vital in implementing such a major change, requiring the business to answer three important questions with wide input from the business functions.

  • What is the imperative to automate?
  • What specific improvements do we seek?
  • How will we measure the success of the implementation?

Your answers to these questions create the path to follow when scoping and executing the project, assisting in preventing scope creep or implementing solutions that solve an unstated need.

Error # 2 – Not optimizing maintenance processes before automation

Maintenance programs become untidy over time with new processes, equipment, and maintenance tasks layered over the old. Tasks slide out of phase with each other, and maintenance documentation may be superseded. Such program creep and slippage result in duplication and obsolescence, causing system inefficiency and ineffectiveness. Automating such inefficiency will only create a greater abstraction, making it harder to resolve.

Cleaning and optimizing the program is crucial in realizing the gains from automation, removing redundant tasks, reviewing frequencies, updating task hours, and realigning tasks that have drifted out of phase. Cleaning and re-baselining your maintenance program cement a point in time against which to measure the results from your automation efforts, identifying the true impacts on equipment availability and comparing benefits against your original business-case assumptions.

Error # 3 – Automating for its own sake

Once the automation project begins, it is common for project members to get caught up in the initiative by seeing automation as a panacea, justifying implementation outside the original project scope. Adding just one or two more sensors seems cheap if the network and computing systems exist and have capacity. Yet, the sensor cost is the tip of the iceberg, with the company bearing lifelong data capture, storage, and analysis costs for something adding little incremental value.

The expense of automation requires targeted use in a maintenance system to produce quantifiable safety, productivity, or quality benefits. Producing terabytes of data no one will use or wasting reliability resources to analyze data of dubious worth erodes value. An equipment criticality analysis before the implementation process helps to ensure the nice-to-have suggestions are filtered and discarded, avoiding scope creep.

Error # 4 – Automating at scale, rapidly

Poorly executed automation projects can slow or bring production to a halt, reducing revenue, increasing costs for an extended period, and creating losses that will take many years to recover. Designing a carefully considered and tightly controlled proof-of-concept allows networks, communication, and data analysis to be proven and debugged, with pre planned contingencies in place to maintain operations in the event of difficulty.

Upon completion of the proof-of-concept implementation, a formal review of costs, benefits, and encountered issues informs plan improvements for the next implementation stage. While a phased and incremental rollout on less-critical systems takes longer, it minimizes the risk of serious impact on production and irons out integration issues while learning what data to monitor and how to use it.

Error # 5 – Not planning for flexibility and scale

Businesses risk focusing on their immediate needs, ignoring their future growth potential and the effects of rapidly evolving technology. Worse still is using in-house personnel to make technology and component selections that risk leaving the company with a legacy system in a few years, requiring extensive investment and risking further production disruption.

Automation planning requires a strategic mindset and an understanding of the industry, considering equipment and technology obsolescence and how the business might scale in five to ten years. While maintenance, engineering, and reliability expertise may exist within your company, it is unlikely you’ll have the required depth and breadth of automation capability. Such expertise is not cheap, but it allows you to de-risk your project and ensure a technology selection cognisant of emerging trends to future-proof your investment.

Conclusion

Maintenance automation can be complex, but it offers a step-change in efficiency when correctly implemented. Any fundamental business change requires strategic planning, broad interdepartmental support, and a considered incremental rollout. Knowing why you’re making the journey, where you’re going, and having the discipline to make and stick to a plan goes a long way toward ensuring maintenance automation success.

This article was originally published on Embedded.

Bryan Christiansen is the founder and CEO of Limble CMMS. Limble is a modern, easy-to-use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.

 

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