Data-Driven Maintenance: Tailored insights for drastic improvements in operational efficiency
By August Emil Stokkeland (Senior Manager) and Vebjørn Axelsen (Partner) at BearingPoint
Data-driven maintenance is a key component of Industry 4.0, utilizing technologies such as IoT, sensors, statistical analysis and machine learning to achieve the ultimate goal of reduced maintenance costs and shorter downtimes. But how can we effectively leverage these technologies to create lasting improvements in maintenance? Let’s explore the best approaches to optimize maintenance with a data-driven approach.
At BearingPoint, we have extensive experience in guiding clients toward effective data-driven and predictive maintenance practices. In this article, we will describe the core elements of our methodology for data-driven maintenance improvements;
- Be smart in using data for effective, if not always predictive, maintenance
- Let value, not technology, be the driving force behind realizing data-driven improvements
- Work iteratively with rapid prototyping in cross-functional teams
- Ensure that data-driven work methods are fully incorporated into operative maintenance
Be smart in using data for effective, if not always predictive, maintenance
There are many discussions around predictive maintenance, and the potential benefits of implementing predictive models in maintenance processes are obviously big. However, predictive maintenance should not necessarily be a goal in and of itself. Sometimes it can be more effective to detect faults as they occur, using simpler models which analyze sensor data (often in real-time), based on an understanding of the workings of each component. Depending on the domain there may also be types of faults and failures that are near impossible to detect and correct before they happen, and where predictive maintenance will therefore not be useful.
Generally, we still wish to move from corrective to preventative, from the calendar based to the more advanced, data-intensive modes, as shown in the figure below.
Data-driven maintenance is therefore about using data smartly, meaning cost-effectively and fit for purpose given the prioritized strategic goals to:
- Enable more advanced and data-intensive maintenance strategies where these are most suitable (condition-based, predictive, and prescriptive; where the latter both predicts the fault and recommends the action necessary to correct it).
- Choose the most effective maintenance strategy for each component and type of fault or failure, based on available insight into what is most cost-effective in each case.
- Effectively prioritize and control the actual maintenance work being performed day-to-day across components, fault/failure types, and maintenance strategies.
It thus becomes clear that data-driven maintenance is about using data broadly and systematically, well beyond understanding the current and future condition of each component under maintenance.
Let value, not technology, be the driving force behind realizing data-driven improvements
To succeed with data-driven improvements we suggest some central principles, which build upon traditional capabilities such as change management and benefits realization during implementation.
1. Be clear on strategic focus and potential
Possible improvements must be identified based on concrete business goals, typically reduced costs and downtimes, so that we focus on what is strategically important and relevant.
2. Identify concrete actions
We must be completely clear on how actions and decisions in each process will be supported by targeted insight from data to create the necessary improvements.
3. Think end-to-end
The implementation of a data-driven improvement must be pushed all the way out into day-to-day processes in the maintenance organization. The pitfall here is to be content when the data/technical disciplines are under control, but that is of course not enough. Effective and continuous use of new insight from data demands changes in ways of working across systems and processes, as well as culture. This requires focus and effort over time to do right.
4. Measure actual improvement effects
We must measure the effects of the process changes we make in a structured way to verify that we create real value (such as reduced costs and downtimes). This kind of effect measurement is of course done with data, and our concept of data-driven thus applies not only to the underlying data analysis to understand the component conditions, but equally to inform how we should design and implement a process change.
In practice, this means designing from right to left in the figure below. Only when we have a good understanding of what type of insight from data is to be used where and for what, can the technological aspects around sensors, data and analysis be considered effectively.
Going back to the topic of designing based on business value: Data-driven maintenance will often influence the organization broadly, since there can be great value in using relevant data well beyond the most obvious operational use cases in condition-based and predictive maintenance. The figure below shows typical use case areas from the most operational to the more strategical, all of which find support in tailored insight from data.
In the following paragraphs, we’ll focus on the most common place to start implementing data-driven maintenance: operational use cases in discovering and predicting faults and failures on equipment.
Work iteratively with rapid prototyping in cross-functional teams
To be successful it is important to not just focus on what is expected to be the most valuable improvement candidate (1 in the below figure), but also to confirm/reject candidates through rapid prototyping (2) so that organizational, data-related, and technical risks are addressed before spending time and resources in scaling implementation in the organization (3). This last part also encompasses the development of production ready technical solutions (sensors, data flows, analytical models, and integration with other systems on a scalable platform).
Through all three phases a close day-to-day collaboration between business functions (result owners, operators, and domain experts) and data science functions (data and model developers) is essential:
Phase 1
The most promising improvements are identified through discussions between the business and data science functions, where an in-depth understanding of the problem area (for example, specific knowledge about the equipment and reasons for failure) is combined with analytical creativity and realism (what data is available, and which analytical techniques can be applied to create the needed insight based on these data). Additionally, deep-dives into historical fault and failure data is useful to solidify business case estimates for improvement candidates in focus.
Phase 2
To accurately describe e.g. failure modes in analytical models we need very close (daily) collaboration between a domain expert and a data scientist, working together in short iterations to attack the problem from different angles, and jointly arrive at good solutions. If one had “perfect data”, where sensor data, historical failure data, and related master data was available completely and consistently in high volumes, machine learning algorithms could infer patterns for us quite easily. In practice, however, the data seldom meets such quality levels, and good collaboration between the disciplines is then essential to create relevant and reliable models using the data we have best-effort.
Phase 3
Before roll-out, insight from the prototype phase should be used both to update the business case and to tailor the implementation. For example, when introducing a predictive maintenance mode for a certain component type, data regarding actual failure rates and the known precision of our failure prediction models should be the foundation for redesigning maintenance routines, so that the actions are planned optimally and cost/capacity for maintenance work is balanced against the risk tolerance of equipment downtime.
Ensure that data-driven work methods are fully incorporated into operative maintenance
To succeed with lasting and significant improvements, the data-driven ways of working and related data and models must be incorporated as a natural and fully integrated part of the maintenance system. This allows two success factors to be met:
- Specific and tailored information integrated into the operative maintenance
The creation of work orders (WO) based on recommendations from analytical models can be standardized. For equipment and failure types where we achieve high model precision in detecting/predicting faults, and where we know what action must be taken, the creation of work orders can be automized. - Structured feedback from performed maintenance
Ongoing, continuous feedback on an adequate scale (Was the alarm relevant, and what happened as a result?) can be used systematically, so that we can develop better analytical models. With increasing maturity and standardization in this process, feedback data can be used automatically for machine learning: Models that detect/predict faults and failures will continually learn and improve from feedback given by maintenance workers in the field.
This demands not only system technical integrations between analysis platforms and ERP/work order systems, but also a redesign of operational maintenance routines. With the latter comes also a change in culture, from strategic planners and all the way out to operative maintenance personnel in the field. A change like this can be demanding and take time, but with close collaboration across teams and a clear path to profit realization, the framework is in place to take gradual steps and to build experience, commitment, and maturity over time.
Want to learn more? Get in touch!
In conclusion, data-driven maintenance is not really about the data you collect. It is about understanding how maintenance processes can be improved by tailored use of data, strategically and operationally, focusing on the potential for significant and lasting value creation. At BearingPoint, we combine solid expertise in data and data science with broad experience in operational excellence across technology and business disciplines.
Get in touch to learn how we can help you use data smartly to increase uptime and reduce maintenance costs! Feel free to reach out to August Emil Stokkeland or Vebjørn Axelsen at BearingPoint.
