IoTÂ started as a way to connect more devices but it has evolved into a transformative tool for manufacturers, providing a new avenue with which to collect and utilize data, writes Poornima Ramaswamy, EVP of global solutions and partners at Qlik.
Manufacturers were among the first to recognize the power of the Internet of Things (IoT) to strengthen differentiation and meet customers’ shifting demands. The technical advancements they adopted across their plants and facilities ranged from cyber-physical systems to predictive maintenance and smart sensors, and it provided them with an indispensable ability to derive insights on production and operation through a network of sensors.
When paired with business intelligence tools, artificial intelligence (AI) and machine learning (ML), IoT accelerates the ability for manufacturers to meet their goals with “smart†manufacturing. With these technologies, producers have been able to identify patterns and detect anomalies from the growing amount of data that is being generated by sensors and other IoT platforms. Some of the end results are fewer equipment errors and product failures and even the mitigation of cyberattacks. The question these producers face is how to continue maximizing the utility of these IoT platforms as the volumes of data they generate skyrockets. To take control of all that data, manufacturers can take three practical steps to confidently amplify their future investments in IoT:
1. Develop a DataOps Strategy as Soon as Possible
The Industrial IoT market was predicted to reach an estimated $26 billion in value domestically by 2020—before the Biden Administration announced the “Made in America†initiative to accelerate US-based manufacturing. It appears manufacturing and IoT will receive support from many sides in 2021, and as IoT proliferates, it will challenge manufacturers’ abilities to manage unprecedented data volumes. A DataOps strategy is the essential first step to sifting through the data glacier and capturing the subset capable of driving material business impact.
Developing a DataOps strategy is not a one-and-done process. It is a journey that requires decision-makers to align technologies and how they are used to address key business issues, such as an agile supply chain and proactive plant management. It requires consideration of the people, processes, and technologies that decision-makers will need to successfully access, transform, and deliver data across the organization. It requires a deep understanding of what data is essential to the way your business makes decisions; who needs that data to make the best choices in their roles; and which technologies can sort and convey the “right data†to the appropriate decision-makers in their respective roles.
Valuable data can be accurately and consistently ascertained through a data catalog. A data catalog is a great tool for manufacturers operating at scale, because it can house all of the data that a business has identified as relevant, whether that data sits in a data warehouse or data lake. In that way, data catalogs help manufacturers avoid wading through waves of information by providing one place for all crucial data, regardless of source. From there, it can be funneled into analytics and AI-enabled tools to be crunched and distilled for informed decision-making.
Learn More: Top 3 Trends Influencing the Adoption of IoT Testing Solutions
2. Employ AI-Enabled Tools To Identify Problems Proactively
Maintenance is a necessary but enormously expensive part of manufacturing and can be mitigated when data and analytics are deployed to make the process more efficient. McKinsey estimatesOpens a new window that servicing and repair costs can account for as much as 70 percent of operating expenses. AI-enabled predictive maintenance allows manufacturers to predict when and where machines will fail and manage the expense of breakdowns, but it also goes one step further. McKinsey foundOpens a new window that AI-enabled maintenance can reduce machine downtime by 30 to 50 percent and increase machine life by as much as 40 percent. This is a tremendous benefit to any manufacturer, considering that a breakdown in an industrial manufacturing setup can cost as much as $30,000 to $50,000 per hour.
Even the time to plan maintenance can be reduced by advanced analytics that sense machine reliability, known as predictive analytics. Per Deloitte, maintenance conducted with the help of these analytics can reduceOpens a new window the planning period by 20 to 50 percent, increase equipment availability by 10 to 20 percent and decrease overall maintenance costs by as much as 10 percent by generating actionable insights from Big Data and AI.
Predictive analytics and modeling tools work by finding patterns and relationships in historical data. Yet, there is a limit to what analytics can accomplish on their own. Reaping real value from predictive technologies requires a strong DataOps strategy that keeps the data pipeline clean and involves pulling information from accurate, relevant data in real-time. Once a clean data pipeline is established, subject matter experts can tackle their own questions and generate insights unique to their expertise.
A great example of analytics working across the organization is in process efficiency. To really understand how efficient a manufacturing process is, the product designers, engineering teams and shop-floor managers all need real-time data into how their areas contribute (or fail to contribute) to the business goal of process efficiency. Are all assets available on time? Do work order cycle times need adjusting? Is everyone adhering to the current schedule? These disparate questions come together to help decision makers eliminate waste and improve quality, particularly when data is analyzed in real-time.
3. Establish Real-Time Monitoring
IoT platforms have equipped manufacturers with the power to monitor the performance of each connected device or part in real-time, but they also deliver insight into the end user by helping to forecast problems with products and suggest solutions. We have seen automakers use sensors to scan for potential component failures to get ahead of any problems and, when possible, fix them automatically with an over-the-air update. Tesla has been particularly proactive in this regard, updating their vehicles remotely to address automotive issues remotely.
Manufacturers and service delivery systems can further utilize sensors to gain more insight into device performance. General Motors subsidiary OnStar uses IoT to send drivers monthly diagnostic reports regarding multiple key systems, including engine, transmission, and anti-lock brakes. OnStar also sends notifications to dealerships, which in turn send their own alerts to consumers to remind them of any issues that need to be rectified. These features are based on analytics that allow manufacturers to connect with consumers on a more regular basis, building customer loyalty.
Learn More: Deploying IoT to Enhance Warehouse Security
Data Is At the Heart of These Innovations
IoT is turning data into a transformative tool for manufacturers. Together with AI and ML, datasets are being used to improve maintenance, predict accidents and anomalous events, reduce downtime, and address product issues after shipping and deployment. As more sensors are introduced, businesses will be able to convert even more data into valuable information, regardless of volume.
Did you enjoy reading this article? Let us know your thoughts in the comment section below or on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!