The cloud, the Internet of Things, machine learning, artificial intelligence—all are in the script making enterprise resource planning a rising star again.
Now might be a good time to see the latest version of enterprise resource planning (ERP). New systems are putting on a good show with artificial intelligence (AI) and the connectivity fostered by the Industrial Internet of Things (IIoT). This new generation of ERP systems is helping intelligent enterprises to make sense of the data gleaned from their networks of smart machines so their people can merge that operational data with business metrics to make better decisions.
“The role of ERP systems has evolved,” says Ian Tooke, chief innovation officer and vice president of strategy at Grantek Systems Integration. Much of this evolution has centered around role-based interaction with the system at levels in the enterprise, including production. “The efficiency and transparency of process execution can increase significantly—thanks to IIoT, real-time access to data, and smart analysis of that data.”
Tooke credits the proliferation of IIoT technology for laying the foundation for ERP’s expanded ability to leverage manufacturing data. “It lowers the price point for gaining access to cleansed data from manufacturing and providing decision support for the predictive planning and execution of processes,” he says.
ERP vendors have responded accordingly. “Today’s ERP systems have operational extensions that simplify the interface to the factory floor,” explains Glenn Graney, director of industrial and high tech at ERP supplier QAD. As a result, even smaller manufacturers are finding it much easier to reduce the lag that has existed in the upward flow of information from operations to business systems.
These lags were due in large part to the need to wait for someone to enter progress reports into a computer somewhere. “Pretty much, you would be in a vacuum until the end of the shift, or even the next day, when somebody would enter the production data into the appropriate system,” Graney notes. “More likely, though, any reporting would wait until the job was done.”
Now, he says, there is no reason to wait. A worker can either report progress on a customized touchscreen or let sensors do it. For an example of the latter, Graney points to an automotive supplier that produces drive-link assemblies resembling heavy-duty bicycle chains. The company could receive an order for 10,000 of these chains, which are used in transmitting power from engines to transmissions. Rather than trying to count them as they come off a pressing operation, the operator periodically presses a button to actuate a scale that weighs the pallet of finished assemblies. Software then translates the weight into a piece count and reports that value to the ERP system.
In the past, establishing this kind of flow of production data into the ERP system required a capital investment and all the effort that goes into getting such a project approved. “You had to put the purchase of some PLC input and output cards in the budget,” Graney says. “And then you had to pay somebody to change the ladder logic in the PLCs.” Not only could these and other items in the project cost $100,000, but you also had to accept the risks associated with fussing with the ladder logic running your processes.
A new act
The operational extensions that QAD and other ERP vendors are offering today bypass most of these expenses and risks by tapping into data that is readily available in existing layers. With new equipment, the operational extensions capitalize on the well-established industrial communications standards that are driving IIoT and are found in today’s human-machine interfaces (HMIs) and products for supervisory control and data acquisition (SCADA).
These operational extensions can even receive data from old machinery retrofitted with the appropriate smart sensors. “You hook a sensor to a $200 TCP/IP I/O drop,” Graney explains, and “if you have Ethernet, the data is available across the network.”
An important industrial standard for ERP is the Business to Manufacturing Markup Language (B2MML), which establishes common data definitions between ERP and production systems. Another improvement in interfaces has been the adoption of RESTful web services, according to Kevin McClusky, co-director of sales engineering at Inductive Automation. “Both cloud-based and traditional ERPs are starting to offer direct web services with the necessary business logic for exchanging information,” he says.
“Automation vendors have made it easier for us,” adds Mike Lackey, global head of solution management for digital manufacturing at SAP. “Most PLCs on the factory floor today are connected to an OPC server, so our ERP system can go through our connectivity layer to the OPC layer, listen for the data tags that it needs, and pull the desired information from there.” Getting that data no longer requires writing a custom interface.
Another important support for ERP’s enhanced role in manufacturing enterprises has been the various IT services available in the cloud. ERP vendors have been adapting their offerings to take advantage of the computing economies available there. The cloud, however, does more than lower the cost and simplify the task of integrating multiple plants belonging to an enterprise and its supply chain. “It also enables easy integration to services outside your four walls,” notes Antony Bourne, president of IFS Industries. “There is plenty of cloud-enabled services that use artificial intelligence to help us analyze data.”
Some services will use machine learning, a type of AI that looks for trends in data from IIoT-enabled machines and other sources to make predictions about the future. For example, a maintenance service could develop an optimal maintenance schedule by taking into consideration trends in past usage and performance data, as well as demand for the machine in the next few weeks. “In the process industry, a food-and-beverage manufacturer may want to work the weather forecast into its demand planning engine,” Bourne says, because “a heat wave may affect demand for some of its products.”
SAP’s Lackey notes that AI is often applied at the edge—the point at which an ERP system gathers information from production machinery and processes. “The ERP system can use decisions and optimizations at the machine level to determine the effects on inventory, capacity, quality and even cost structure,” he says.
Staying on script
System integrators like Grantek report that they are implementing analytic platforms that give workers access to information about production processes via mobile devices. “The focus in ERP is shifting from the central data repository to facilitating mobile, role-based user interactions,” Tooke notes. With these platforms, “information is called, processed, and displayed immediately.”
One effective strategy is to create dashboards tailored to presenting timely information specific to the work that users are performing, says Andrew Bolivar, senior consultant at Ultra Consultants and director of the company’s Center of Excellence. “Role-based information presented graphically simplifies the presentation and increases response time,” he says.
Not only should dashboards present information in a visually intuitive format, but the metrics displayed should be connected somehow to the business’s key performance indicators (KPIs), Bolivar adds. He and his colleagues have been working with manufacturers to create simple metrics that are pertinent to the task at hand, yet also have a connection to KPIs such as overall equipment effectiveness (OEE), production volume, and inventory turns.
The ability to track key metrics in real time not only helps people to make decisions, but also can support automated decision-making. The ERP system, for example, can reorder materials from existing suppliers. “Sensor 12 on machine 20 on line 14 may send a signal that alerts suppliers in real time to replenish needed materials,” Lackey describes.
This ability is also crucial for participating in the e-business networks, or e-stores, that are proliferating in the digital world. “How are you going to compete where there are no conversations? If someone posts a request for a bid for an individualized product, you have to respond with your bid within seconds,” Lackey says. “Your rating—whether you get four thumbs up or only two stars—will depend on your ability to deliver on your commitment.”
The intelligence available through the business context of the ERP system can get you those good marks by determining whether you have enough open capacity, access to the necessary materials, and ability to ship by the delivery date.
Managing service in the field
Besides allowing ERP to play a greater role in production, greater connectivity and intelligence have also extended the role it plays in the life of products beyond manufacturing. Machine builders, for example, are able to keep tabs on their installed base of machines over cloud-enabled networks. Not only can ERP help builders to collect and analyze performance data to look for ways to improve their designs, but it also can assist them in managing after-sales service. “This access to performance data gives them the ability to monitor assets and develop a maintenance plan that maximizes uptime,” Lackey says.
To show how cloud-enabled ERP can help manufacturers improve service on other kinds of products, IFS’s Bourne points to the experience of Anticimex International, a Swedish company that provides pest control and other environmental services to homes and businesses in 17 countries. In response to both a growing resilience of pests to conventional pesticides and increasingly stringent environmental regulations, Anticimex has developed a line of digital traps equipped with IoT sensors and cameras to control rodents. Besides using an IFS Applications ERP system to manage its manufacturing operations, the company also relies on the system to coordinate the technicians who service the installed base of traps.
Anticimex’s Finnish operation spearheaded the company’s use of a module called IFS IoT Business Connector. Through IoT connectivity, the installed base of digital traps throughout Finland now transmits data directly to the ERP system. Technicians no longer have to visit all traps periodically just to check which ones might be full. Not only does the software tell them which traps need to be emptied and reset, but it also suggests an optimal route for their visits, thereby eliminating unnecessary travel.
The IoT module has also given Anticimex another way to cut costs and improve service. Besides indicating whether the traps are full, the sensors also track battery power to report which batteries are running low and need recharging. By analyzing patterns in power depletion for each trap, AI can predict when batteries will fail and determine which batteries should be replaced rather than recharged.
“These analyses have affected the supply chain in that Anticimex can identify any faulty batteries and send them back to the suppliers, which is a capability that the company didn’t have before,” Bourne says. The result is that batteries last longer between charges and require less service, cutting costs and improving customer satisfaction ratings.