Power Your Equipment Manufacturing With Predictive Analytics: A Pathway to Greater Customer Value
A tale of caution
We have all heard about the poignant tale of a majestic vessel that met an untimely demise due to a lack of foresight - The RMS Titanic.
Despite being a technological marvel of its time, the Titanic met a tragic end. It sailed too fast despite iceberg warnings and lacked enough lifeboats due to outdated regulations. This, coupled with a chaotic evacuation plan, led to a disaster that could have been avoided with better planning.
Reflecting on such historical events should ideally shape our approach to preparing for future problems. Unfortunately, traditional manufacturing processes often lack planning and forecasting, leaving decision-making based solely on past experiences.
This lack of foresight mirrors the Titanic's ill-fated journey, leading us to question: Are we setting ourselves up for a similar disaster?
Now, let's turn our attention to the present – the challenges that smart equipment manufacturers are currently facing.
Why now?
In a world where "reactive" has become the norm, smart equipment manufacturers find themselves stuck in a perpetual game of catch-up. With no remote monitoring capabilities, they rely on a whimsical dance of reactive maintenance. They scratch their heads, wondering how to satisfy the diverse needs of their industrial clients when they cannot even understand how their own products would fare in the wild.
As we delve deeper into the challenges faced by smart equipment manufacturers serving their industrial clientele, it becomes clear that absence of predictive insights significantly hampers their effectiveness.
Let us examine these issues in detail:
- Lack of real-time monitoring and insights: Traditional processes hinder manufacturers' ability to obtain real-time performance data and make data-driven decisions.
- Dependence on reactive maintenance: Without remote monitoring, manufacturers resort to reactive maintenance, leading to prolonged downtime and increased expenses.
- Overlooking opportunities for operations optimization: Avoiding historical data limits their ability to unlock insights to optimize operations and control equipment failures.
- Constraints in customization and product enhancement: Standardized products struggle to meet diverse industrial clients' unique needs, creating a disconnect between manufacturer offerings and customer requirements.
Enhancing customer value through predictive analytics
Despite the myriad challenges faced by smart equipment manufacturers, predictive analytics shines as a transformative solution. This technology revolutionizes traditional processes by introducing real-time data, historical data, analytics, and intelligence, thereby shifting manufacturers from a reactive to a proactive & predictive stance.
Here is how:
- Real-time monitoring and insights: Employing IoT (Internet of Things) devices and sensors, manufacturers can gather real-time machine data, proactively tackle issues, and make informed decisions to better cater to customers' needs.
- Proactive maintenance: Predictive analytics allows early detection of potential equipment failures, enabling a shift to proactive maintenance. This not only minimizes downtime but also enhances equipment longevity and reduces repair costs.
- Operational efficiency: Predictive analytics empowers manufacturers to anticipate trends and predict equipment failures, leading to improved resource management. Enhanced operational efficiency results in quicker deliveries, superior product quality, and a better customer experience.
- Customized and continually improved products: Predictive analytics offers insights into real-world product performance, fostering informed enhancements and continuous improvement. With these insights, manufacturers can tailor products to meet each client's unique requirements, thereby bolstering customer satisfaction and loyalty.
Prerequisites for predictive analytics in smart equipment manufacturing
Before we delve deeper into the benefits of predictive analytics, it is important to note that, just like any other technology, it is not the omnipotent savior of all problems.
These are the key prerequisites that should be kept in mind while considering use case qualification:
- The problem needs to be something you can predict and have a clear outcome to aim for.
- A recorded history of the equipment's performance, encompassing both successful and unsuccessful instances, should be available.
- The quality and relevance of this history should be suitable for the use case at hand.
- It's crucial for the organization to have industry specialists who have a thorough comprehension of the challenge at hand and can contribute meaningful perspectives.
Predictive analytics in action
To better understand the importance of predictive analytics in smart equipment manufacturing, let us look at an illustrative example of a real-world use case.
The Problem: Battling delays and scalability in manufacturing operations
A global leader offering advanced technology for the engineering & manufacturing of thermal processing equipment was facing a peculiar challenge in their manufacturing operations.
The company faced two key issues.
Firstly, their on-site operations recorded equipment and plant performance data at the end of each shift or day, leading to delayed insights and making it challenging to take timely corrective actions. Manual collation and analysis of data from various sources added to the time-consuming process. Moreover, consumables like the refractory lining in furnaces were replaced or repaired based on set rules or operator experience. If the lining was damaged before repair, it posed a risk of failure, accidents, and even higher risks for furnace newcomers.
Secondly, managing customized monitoring software for diverse global customers proved non-scalable and resulted in increasing overheads for the client services teams.
To address these challenges, the industrial equipment manufacturer looked for a machine learning consulting partner and teamed up with Saviant. The team solved these challenges through a strategic approach that included the following steps:
- Assessing data maturity: Saviant assessed maturity of the machine data & maintenance records and came up with an idea for predictive maintenance based on data for the lining. If proven, this was further to be extended to other consumables and critical components like pumps, motors, etc.
- Processing data for analytics: The necessary data was then extracted, transformed, and labeled. Potential Machine Learning (ML) models were then shortlisted for testing and evaluation, emphasizing feature engineering and hyper-parameter tuning for accuracy.
- Building machine learning models: Models were then deployed, continuously monitored, and retrained as a service, ensuring their relevance and accuracy over time.
- Seamless system integration: Lastly, the solution was seamlessly integrated with the company’s existing system. Its efficacy was validated through running the model and obtaining desired outputs in real-world scenarios after successful proof of concept (PoC) implementation.
The Solution: An advanced IoT platform for performance monitoring of equipment
The team at Saviant created an advanced IoT platform that monitored and captured equipment data, stored it on cloud for real-time analysis and alerts. Virtualization of equipment details allowed customers to access an interactive view of their entire setup. Key performance indicators (KPIs) like OEE, throughput, and quality were available remotely, empowering proactive production changes. The service teams also benefited from accessing detailed equipment information from anywhere, simplifying analysis and repair. Its multi-tenant architecture ensured scalability and efficiency, streamlining operations, and enhancing equipment monitoring.
The impact: Remarkable results for the industrial clients of the thermal processing equipment manufacturing leader
Plant managers can now maximize equipment uptime by predicting issues before they occur. With the client’s induction furnaces and state-of-the-art IIoT (Industrial Internet of Things) platform, industrial clients like Boeing and NASA have increased productivity and reduced downtimes through better machine monitoring and control. Read the full customer success story here.
Wrapping it up
Leveraging predictive analytics, smart equipment manufacturers can effectively tackle pressing issues such as real-time monitoring, predictive maintenance, and optimized operations. Remembering the Titanic's tale, the key to averting potential challenges lies in predictive analytics, serving as technological binoculars towards a more resilient future.