5 Data Science Techniques for Improved Business Maintenance Prediction
For decades, downtime has remained a persistent issue regardless of industry. Now, with business maintenance prediction, you can anticipate when equipment failure will occur before any indicators are visible.
Naturally, you need to properly collect, aggregate, and analyze information to see a difference. If you know which data science techniques to leverage, you can maximize your approach’s impact, minimizing downtime-related losses.
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Why Must Business Maintenance Prediction Improve?
Many firms suffer from costly hardware or software failure despite being well-managed or funded. In fact, 59% of Fortune 500 companies experience at least 1.6 hours of downtime weekly, translating to an estimated $896,000 in losses per week on labor alone.
Indicators suggest equipment failure is increasingly common. Another report states Fortune Global 500 enterprises lost over $1.5 trillion to unplanned downtime in 2022 — up from $864 billion in 2020.
Reactive and preventative approaches are some of the main drivers of downtime — they either address the issue once something has gone wrong or when something is about to. Prediction is the only method addressing this gap, as it revolves around data instead of a trivial schedule.
While the upkeep of standard office equipment may seem insignificant, business infrastructure must be maintained, too. The sudden failure of IT hardware, software, or networks could cause significant productivity losses, delay time to market, or create security vulnerabilities.
Decision-makers should feel concerned about security compromised by hardware failure since it can be damaging. For example, considering 90% of data breaches originate from malicious emails, a lapse in security features or spam filter uptime could result in a cyber attack. Proactive action is essential for avoiding financial losses.
Benefits of Improving Business Maintenance Prediction
Experts expect business maintenance prediction to gain traction swiftly, meaning early adopters may gain a competitive edge. Estimates put its market value at $64.3 billion by 2030.
More importantly, companies will likely experience significant cost savings, as they’ll know which redundancies to cut back on and when to order replacement components. One source estimates a 5%–20% reduction in carrying costs and a 3%–5% decrease in replacement equipment expenses.
Business maintenance prediction can also extend equipment’s life span significantly since catastrophic failures become less common. It also allows for greater precision during upkeep, addressing the root cause before issues develop further.
Data Science Techniques for Predictive Maintenance
The only way to anticipate equipment failure is with properly acquired and applied information. You should consider these data science techniques to improve your organization’s predictive maintenance approach.
1. Anomaly Detection
You can use anomaly detection to identify outliers and novelties, meaning equipment remains safeguarded against abnormalities and previously unknown instances. This technique is especially useful when failures are relatively uncommon or you lack thorough historical records.
2. Data Visualization
Data visualization transforms images, sensor readings, or text into an easily understandable graph or chart. In addition to simplifying the necessary actions technicians must take, it can help secure buy-in from the boss — or the board.
Those unfamiliar with data science are more likely to accept your stance if you present your information in an easy-to-follow format. It may prompt them to agree to order replacement parts or update legacy systems.
3. Natural Language Processing
With natural language processing, you can aggregate all written or typed maintenance records, inspection logs, and technician notes to generate data-driven insights. This technique can add a valuable knowledge layer to upkeep, especially in sectors where equipment failure is complex.
4. Decision Tree
A decision tree is particularly useful for predictive maintenance because it follows a simple, logical process. This machine learning algorithm won’t just notify you of anomalies — it will also interpret them, streamlining your response time. As it answers each question, you get closer to a solution.
5. Segmentation
Segmentation can sort factors like usage patterns, environmental factors, equipment age, level of wear, and operator errors into categories, enabling you to extract valuable insights on which are persistent pain points. If the IT infrastructure you manage is extensive, you should segment it based on factors like vendor, hardware type, or system age. This approach could help you assess risk levels and determine priority.
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Leveraging Data Science Techniques for Prediction
While you could just as easily go with a time series analysis or standard predictive modeling, these data science techniques address company-specific pain points that may apply to your firm. You should consider each one’s potential impact to decide which best suits your organization.
Originally posted on OpenDataScience.com
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