In the discussion around big data, automation and the Internet of Things (IoT), more attention is often paid to the technologies, sensors and data-collection devices than to how analytics can be leveraged for business benefit. Enterprises should worry less about “things” and focus more on how those things can transform their organization and business processes to achieve operational excellence.
To date, manufacturing operations have focused their efforts on optimizing their physical assets. They look at improving their waste removal, refining their supply chain and working to create lean operations on the factory floor. These efforts and the metrics that drive them are cost-focused, seeking to cut procurement, conversion and execution costs and create more efficient inventory levels.
But today, data resides everywhere in manufacturing—in Enterprise Resource Planning (ERP) systems, Product Lifecycle Management (PLM) systems, Manufacturing Execution Systems (MES) and Supplier Relationship Management (SRM) systems, in machine tools and in thousands of spreadsheets, files and folders across the company. Data also resides outside the enterprise, across the value chain with partners on both the supply and the sales sides. The goal of sound Industrial Internet strategies is to break down organizational, process, data and system silos and automate the collection of data across operations. An enterprise that uses a deeper, wider and smarter analysis of its data will see big operational dividends.
A few daring manufacturers are testing this ground by deploying a combination of technologies to take advantage of big data, automation and the IoT to create the Industrial Internet of Things (IIoT), with the aim of improving employee health and safety, decreasing financial risk, reducing production downtime and time-to-market and improving quality in processes and products. However, these successes do not come from technological prowess. Manufacturers succeed in IIoT because their deployments create measurable business value.
The following six use cases are examples of how manufacturers are putting IIoT to smart business use:
1. Rapid Costing
In many industries, manufacturing functions are considered as internal suppliers to the product management group or the sales team and, therefore, must provide cost estimates during tendering and business development cycles. Tough market dynamics require rapid costing on price indications about a particular piece of equipment, and this quick turnaround can be a decisive factor in whether the enterprise wins or loses major orders. Historical data including hit-rates, customer preferences, footprint requirements, past tendering records, executed projects and product definitions must be combined in an IIoT strategy to inform tendering feedback, reduce lead time and increase quality of tendering.
2. Non-Conformance Report (NCR) Analytics
Manufacturing organizations usually collect data points regarding non-conforming events that arise on the factory floor. An NCR is issued when a product, process or procedure does not comply with set standards. It can also represent a significant deficiency. An NCR is generally used as a tool to reduce errors as much as possible and keep faulty products and equipment from reaching customers. IIoT technologies can help analyze NCR data, find relationships between NCRs and support the prediction of future non-conformances.
3. Plant Load Optimization
Sales and Operations Planning (S&OP) processes are the core of a manufacturing company. They allow management not only to get a handle on the business but also to create a command and control system that integrates strategic business plans and tactical day-to-day operations. S&OP helps guide daily operations and monthly plans toward long-term business goals and aligns manufacturing, suppliers and customers. Depending on the product´s lifecycle, the S&OP process can define the load forecast over time, which helps determine which products an enterprise will manufacture at which plant—and creates the basis for plant loading. This decision has implications on operational and financial performance. Historical load, industrial footprint, executed projects, scope changes and customer behavior are data points that can optimize plant loading. To understand and balance the trade-offs to optimize loading requires an IIoT strategy.
4. Shop Floor Operational Improvements
Manufacturers are increasingly interested in the use of low-cost sensors attached to machines for preventive maintenance and condition-based monitoring. Some are finding wireless connectivity and big data processing tools can make it cheaper and easier to collect actual performance data and monitor equipment health. For example, critical machine tools are designed to operate within certain temperature and vibration ranges. Sensors that can actively monitor and send an alert when the tool deviates from these prescribed parameters can aid in preventing malfunctions. When critical equipment fails, operations can quickly fall behind and miss on-time delivery, leading to delayed projects and cost overruns. Big data in an IIoT solution can help improve overall equipment effectiveness (OEE), minimize equipment failure and enable proactive maintenance to reduce or eliminate downtime.
5. Suppliers and Supply Chain
Access to real-time supply chain information helps identify issues before they happen, reduces inventory and potentially reduces capital requirements. The IIoT can help manufacturers gain a better understanding of this information. By connecting plants to suppliers, all parties involved in the supply chain can trace interdependencies, material flow and manufacturing cycle times. IIoT-enabled systems can be configured for location tracking, remote monitoring of inventory and reporting of parts and products as they move through the supply chain. They can also collect and feed delivery information into ERP, PLM and other systems.
6. Health, Safety and Environment
Key Performance Indicators (KPIs) for health, safety and environment (HSE) include data for injury and illness rates, short- and long-term absences, near-misses, vehicle incidents and property damage or loss during daily operations. These measurements are typically stored in myriad systems, spreadsheets and emails and are reported sporadically during management reviews or audits. Lagging indicators do not have any relational value and companies rarely perform thorough root cause analyses. A well-defined Industrial Internet and analytics strategy will help isolate and address HSE issues.
Though enterprises’ may have a general awareness about their carbon footprint, they are usually missing cost-effective measurement systems and modeling/performance management tools to optimize energy and heating. Using IIoT and automation to monitor environmental controls, such as HVACs and electricity grids, can generate cost-saving opportunities by helping companies better understand applicable economy model operations and avoid peak demand charges. Integrating weather data and predictive modeling also can help reduce energy expenses and plan energy usage, a large component of manufacturing costs.
These six use cases represent practical, on-the-ground implementation of IIoT using big data, analytics and automation technologies. Manufacturers considering how they may benefit from these advanced and interconnecting technologies will need to do the following:
- Define the business value in specific use cases and prioritize against your budget limitations;
- Consider how you will control access to data and protect it from theft or misuse;
- Decide which analytic solutions and data management systems will best achieve your goals;
- Hire system integration specialists to optimize a solution across disparate IT application landscapes;
- Secure the right skills to analyze the data and make recommendations, and
- Find the right providers with the technical capabilities and domain subject matter expertise to help achieve your business objectives.
To kick-start your IIoT strategy, bring the relevant leaders and stakeholders together for an innovation workshop. Help them understand the concepts and how other companies are applying these technologies to their environments. Brainstorm the value proposition for your enterprise, including potential opportunities that could arise. Start with one or two pilot projects, for which business requirements are the highest and the data is most available.
This article originally appeared in our sister site, IndustryWeek.