Heavy equipment manufacturers today are no strangers to using advanced technologies to get a competitive edge. Predictive maintenance is one of the advanced technological applications that offers tenfold ROI resulting in a 30% to 40% savings.
As more and more heavy equipment manufacturers adopt the predictive maintenance model you must be first familiar with relevant challenges. This blog will explore some of those challenges and determine the best ways to address them. So, let's get started!
Challenge 1: Lack of the Right Data
Sourcing the right data and using it to its full potential is much easier said than done. Not all available will give you the desired outcome making it important to map these outcomes and weed out any missing data.
Using solutions with analytical abilities can help easily identify any missing data and process them into comprehensible bits of information. This information will then help Heavy Equipment Manufacturers identify and predict performance issues for making informed decisions.
Challenge 2: Lack of Understanding of Outcomes for Business Impact
You should always make a point to evaluate and understand an asset's performance within the context of business outcomes. Manufacturers in Illinois must account for essential metrics such as machine throughput, uptime, history of critical failures, defect rate, and operating life.
You must leverage the potential of essential predictive models that address metrics more relevant to your business. This will help model the identified behavior and create an algorithm to monitor and predict the future behavior of the asset. This further helps access essential insights for better decision-making and enhanced customer experience with better revenue generation.
Challenge 3: Lack of Necessary Domain and Technical Skills
Achieving success with predictive analytics requires effective collaboration between domain experts and data scientists. While data scientists can focus their efforts on creating robust algorithms, domain experts can lend their expertise to creating the right algorithm. Unfortunately, this is not the reality in most cases resulting in failed preventive maintenance.
This is where heavy equipment manufacturers need to take a different approach by creating a synergy for effective preventive maintenance. You must create a team with both data scientists and domain experts to create the ideal solution based on your business's specific needs. The right combination of domain skills and technical expertise are key ingredients for a preventive maintenance success story.
Challenge 4: Ineffective Translation of Business Issues to Technical Issues
Predicting the failure of equipment is all about translating business problems into technical problems. This calls for a technical solution that addresses the issue and meets business needs accordingly. Once you determine the technical solution it becomes much easier to fit it into the context of business issues such as the budget.
One cannot find the right solution without enhanced business acumen and technical expertise. You can access this support from an unbiased consultant or even leverage the services of a consultation company to devise a strategy to find the right solution. After determining the right solution you can collaborate with the right vendor and create a solution within your budgetary constraints.
Challenge 5: Ineffective Change Management And Strategy Implementation
Factors like user training, change management, and continual solution support are very important. Suppose an operation team picks an intelligent solution with little knowledge chances are the solution will be lost or underutilized.
This is where professional change management practices can help manufacturers in Illinois to facilitate the effective adoption of predictive models. Predictive solutions are continuously evolving making it important you have seamless access to active support. This will help your solution's preventive models evolve with time and help you get a competitive edge over others.
