IoT in Manufacturing: Take Your Program to the Next Level
Our research shows that too many manufacturers are stuck in IoT pilot purgatory with initiatives that fail to scale. We have pragmatic advice to step up and develop an IoT program that adds genuine business value.
Scaling Internet of Things (IoT) initiatives can be more challenging for manufacturers than for companies in other industries. After all, manufacturing is one of the more mature sectors; its businesses typically have long histories of substantial investment in facilities, tooling, processes, supply chains and other assets that cannot be changed with a snap of the fingers.
Network reliability and device connectivity that operate without failure on a factory floor is a challenge in a rugged, dense environment filled with heavy engineering equipment. But it’s a challenge that must be met if a business is to use structured, unstructured and time-series data from plant-floor devices, machines and systems to fuel smart decisions.
New industrial point solutions often require using a light manufacturing execution system (MES) to fill gaps in order to connect the workflow engine to an open architecture that will integrate with both the MES and the organization’s ERP system. On top of all this, the COVID-19 pandemic’s disruption of operations has fueled a greater need for innovation, collaboration and remote enablement.
Nevertheless, to be full participants in Industry 4.0, manufacturers must up their IoT game, escaping the pilot purgatory cycle to develop scalable programs that deliver lasting value. The pandemic, which shuttered plants worldwide with supply-chain and attendance challenges, underscored this need.
In February 2020, we commissioned Forrester to evaluate challenges companies face when expanding IoT initiatives. Our goal was to determine what makes some companies more successful than others in growing their IoT capabilities. While the main study crossed industry boundaries, we zeroed in on the manufacturing sector through an online survey of 161 IoT strategy leaders and data and analytics decision makers.
Manufacturers were placed in one of three categories, depending on their survey responses (see the following figure).
Interestingly, when compared to other industries, manufacturing has the highest percentage of firms in both the Novices and Committed categories. Manufacturers correctly believe there is value in their factory data and many are accessing it using IoT applications to drive better performance outcomes across yield, inventory, productivity and asset health. In many cases, they’re beginning to work on these data challenges from the initial stage — not merely integrating, but developing a complete data strategy.
Additionally, it’s growing more difficult for manufacturers to get CapEx budget approvals; many cannot readily justify swaping out capital equipment for improved capabilities. As a result, they turn to pilot programs to prove out results.
Key industry levers
Through research and experience, we’ve learned that IoT success or failure is not determined by the breadth of IoT capabilities in an organization, but rather the degree to which a firm is organized and prepared to test and expand its IoT initiatives quickly and efficiently. As a general rule, five key levers impact IoT project success. However, our statistical analysis found that where manufacturing is concerned, only three levers have a significant impact in enabling businesses to quickly move IoT initiatives beyond the pilot phase:
Integration with the enterprise. This lever is focused on ensuring IoT initiatives are well integrated with, and supported by, operational technologies and processes, and are designed to eliminate silos to unlock critical data that was previously inaccessible.
Data and analytics. Data and analytics address a company’s ability to use data from assets, systems or IoT-enabled products and processes to drive improvements in decision-making; optimize actions; and generate new sources of revenue. This includes the ability to apply more advanced analytics capabilities, such as predictive analysis, rather than merely using descriptive analysis of IoT data.
Infrastructure and technology. This includes a company’s architectural vision, building blocks and technical capability to support and manage IoT deployments and corresponding data.
What are the other two levers? Strategy and organizational enablement. And make no mistake, they are important to successful IoT deployment among manufacturing companies. But we found that when manufacturers are at the inflection point of wanting to quickly scale IoT initiatives, these two levers will not have a significant impact compared with the other three. To learn more about the levers and our research, see “No More IoT IOS: Start Scaling IoT with Five Key Levers.”
Exploiting the levers
In light of the Forrester study, client engagements, and discussions with decision-makers, here are our thoughts on how manufacturers can use the key levers to improve the scalability of their IoT initiatives:
LEVER: Integration with the enterprise
Relevance for manufacturing: The true value of IoT lies in integrating captured IoT data with existing operational processes and data streams to drive ongoing process monitoring and improvements. This is not easy to accomplish; 59% of manufacturers cite difficulty in unifying operations technologies and information technologies as a top integration challenge.
Business and operational silos as well as a lack of needed skills further exacerbate these challenges. More often, due to the complexity of interconnecting machines, enterprise systems and workforce data, manufacturers turn to system integration partners for IT skills to support IoT programs and to design and build out a roadmap for process workflow and data integration for visualization of performance indicators.
Recommendation: Strive to create a unified view of how IoT impacts manufacturing operations and customer experience. Evaluate these impacts as start-to-finish processes as they relate to a workflow or operational function, rather than as isolated component silos. Key strategies to eliminate silos include creating new metrics and goals tied to business and operational incentives; enabling stakeholders to share responsibility for overall outcomes; and deploying role rotation and cross-pollination to widen stakeholder perspectives and deepen connections.
LEVER: Infrastructure and technology
Relevance for manufacturing: The right infrastructure is critical in deploying IoT at scale. One interviewee noted that infrastructure was table stakes — that is, a clear prerequisite — when beginning any IoT journey. Top infrastructure challenges for manufacturers include the lack of modernized infrastructure to support IoT and ensuring the security of the software, hardware and infrastructure that enable IoT technologies.
Recommendation: Assess your technology infrastructure requirements related to deploying secure, scalable edge and cloud infrastructure to support current and planned IoT use cases. For manufacturers, key IoT use cases requiring infrastructure include predictive maintenance solutions that use sensor data to optimize maintenance downtime; supply chain processes; and quality control solutions to ensure operational process and product specification compliance. Stakeholders must continually assess their IoT initiatives to identify new technology, strategy and process requirements for seamless deployment. Infrastructure executives must continually monitor these changes to identify required architectures, processes and skills updates.
Of course this is hardly an easy task, but it can be — and has been — done. One leading manufacturer of industrial tools prioritized piloting an overall equipment effectiveness (OEE) use case to address immediate factory challenges as a way to address machine availability, performance and quality (APQ). The pilot succeeded, and the manufacturer is now scaling the program to additional plant locations, having demonstrated a proof of value to continuously fuel new investments in developing an application pilot for improving first-time pass (FTP).
The company developed both OEE and FTP apps to assess APQ and OEE calculations at both line and workstation levels, with alerts that call out deviations in configurable thresholds per shift schedule to improve uptime and quality production.
LEVER: Data and analytics
Relevance for manufacturing: Having the right information model to build out IoT data and analytics capabilities that align to lifecycle and operating context and industry standards is necessary to measure the success of visualizing efforts regarding the other levers, and therefore is integral to the IoT solution scaling process. Top challenges for manufacturing include identifying the best location (e.g., sensor level, device level, cloud environment, data center) to analyze data, and subsequently ensuring that the right people, with the right skill sets, have access to relevant data to provide actionable insight.
Recommendation: IoT solutions deliver business value by analyzing huge volumes of structured, unstructured and time-series data to identify trends, provide actionable insights and predict events to unlock value. Enterprise stakeholders must understand the value of advanced analytics and be able to work with line-of-business executives who can provide feedback into the analytic models and algorithms to advance decision-making capabilities to create new sources of revenue.