The manufacturing industry is getting ready for the future – thanks to AI and RPA
You can't just order a company to be smart. You have to embed intelligent action into its DNA – and that is only possible on the level of Enterprise Resource Planning (ERP), the back-bone of a company. Best practices illustrate the new perspectives opened up by artificial intelligence (AI) and robotic process automation (RPA) as value-added key technologies.
The potential of artificial intelligence (AI) and robotic process automation (RPA) for the industry is huge, be it plants that optimize their own energy consumption while running, machines that conduct quality checks while producing or mobile robots that navigate the factory floor on their own. Not only do these new technologies provide SMEs with added efficiency, flexibility and reliability in production, but also with a real competitive edge.
The days when AI was just a vision are long gone: a survey by reichelt elektronik has shown that already 58 percent of the German manufacturing companies interviewed are using artificial intelligence in production, 31 percent of which are using it throughout their production floor, whereas 27 percent only use it in some areas. They implement AI for the purpose of increasing their productivity, quality assurance, process optimization and improving their own cyber security.
The endless possibilities of AI-based ERP systems
Already today, AI is used at various stages of business processes to create real added value, for example in field service management. Service agents on site often have to deal with the problem that urgent service cases are hard to solve since the procurement of spare parts can take some time. The field service agents then draw on the colleagues from engineering to identify the problem and recommend a course of action. This takes up a lot of time and resources, and every minute of downtime on the customer's side is one minute too much.
An AI-based service solution in combination with the ERP system can be a great help here: In a global data and knowledge hub, you can search service documents, the machine documentation and the information basis for a present service case. This allows you to quickly find results based on which you can make the best possible decision for the service call concerned.
Knowledge articles from the engineering and service department as well as the implicit knowledge of the technicians are bundled in a data and knowledge portal and integrated via a document management system. The ERP system serves as a digital AI-aided process and data hub where all information is collected. Repair and maintenance work can therefore be carried out faster and ideally proactively, meaning before a specific service call occurs. And the solution offers even more potential: You can use it to develop entirely new business models. For instance, you can give your customers direct access to the knowledge content so they can conduct searches as a self service. This way, you enable your customers to resolve an incident by themselves at best.
Put an end to dusty records, RPA brushes up your data quality
AI and RPA can take care of tasks surrounding ERP that previously required manual work, most notably the repetitive processing of business data. Thanks to RPA, your employees won't have to manually transfer them to the ERP system anymore.
This holds huge potential for saving time and personnel resources. There's yet another advantage: Robotic process automation makes data within the ERP system reliable and prevents processing problems, duplicates, corrupt copies or disturbed business processes. This eventually increases the data quality, which is the actual Achilles' heel of digitalization projects.
RPA also uncovers further optimization potential for entering and processing orders. The order process is a standardized process in most companies that can be improved by RPA to make it more efficient and cost-effective. A software bot can automatically complete tasks such as sending the order confirmation, printing the shipping document and issuing the invoice.
Standardized processes also enhance the communication with customers: RPA is capable of processing recurring requests automatically and therefore faster, which in turn has a positive effect on customer satisfaction. In addition, the system prioritizes customer requests by assigning important issues directly to the specialist responsible.
AI in action: 3 examples of common ERP processes
In general, AI can optimize different business processes. The solutions can be divided into two categories:
- User help and enhancements
- Process automation and improvement
Conversational AI bots, for example, are similar to familiar digital assistants like Siri and Alexa. The skills of these chatbots are becoming increasingly extensive and can be used in various departments of production. This is the case in downstream logistics, for example. Here, conversational AI can be used in all management systems to manage processes like quality control, product recalls, inventory and supply chain management. By interacting with the bot, employees can enter procurement requests and regularly check the status of deliveries. Furthermore, they can query the status of an order or a delivery using a voice command. This saves time and resources while also reducing the error rate as typical mistakes in data entry are avoided.
AI is also used for monitoring and modeling the plant behavior to optimize the overall equipment effectiveness (OEE). The AI tools draw on the massive data volumes of the machine systems connected to the ERP system, and the Internet of Things (IoT). The costs for IoT equipment have become fairly moderate so that industrial companies are able to monitor hundreds of machine sensor readings in a production line in real time. These massive data volumes then form the basis for machine learning (ML) algorithms. The insights into the operating time, performance and quality of products are therefore facilitated.
AI technology can also be profitably used in warehouse and production planning, for example, for providing and moving materials and products in a warehouse. AI algorithms can combine data from ordering, production and warehouse systems and hence determine the optimum stock level. Furthermore, they can change configurations in order to meet the demand. AI can also support the planning processes in production. When planning on macro level, you can use AI to predict how many products will have to be produced within a certain period of time. It's also possible to gain additional insights on the buying behavior. The planning of the individual production processes also becomes smarter when it can access and react to dynamic order changes.
Hit the bulls-eye instead of a shot in the dark
AI and RPA are going to change the industry in the medium term – especially in small and midsized manufacturing companies. AI already plays an important role in demand planning, IIoT projects and the digitalization of the supply chain. For instance, such tools are capable of predicting the effects of supply bottlenecks or price changes, and suggest alternative actions.
In companies that have been successfully digitalized, an AI-based ERP system combines data from different units – faster and more reasonable than ever before. Self-controlling, self-optimizing processes and digital assistants from the field of robotic process automation and business analytics draw on a single source of truth that is the basis for further digitalization projects and new profitable business models. Companies have to act now in order to remain competitive.
