In the manufacturing industry, traditional optimization levers are no longer sufficient to keep pace with the dynamics and complexity of the environment. This is particularly true for companies that have to manage global production networks and supply chains in a cost-efficient and adaptive manner. Against this backdrop, maintaining and continuously improving Operational Excellence has become a critical task. The use of Artificial Intelligence (AI) opens up radically new ways here.
The time to systematically integrate AI into industrial processes has never been better. The rapid technological development of recent years is reflected in a solution landscape and an ecosystem that are growing rapidly in breadth and depth. Today, companies can already draw on a large number of tried-and-tested and increasingly easy-to-adapt application scenarios and implement them with robust business cases. At the same time, more and more data and high-performance IT infrastructures are becoming available in the course of industrial digitalization to enable the productive use of AI.
A distinction can be made between different types of AI that are suitable for different use cases, such as generative AI in the development of scenarios and in innovation management or advanced data analytics in planning, predictive maintenance or process monitoring. By using artificial intelligence, companies can actively accept more complexity and deal with it much more effectively. This makes it possible to successfully tackle issues that cannot be solved effectively and efficiently with other tools, e.g. in areas such as cross-plant planning optimization. At the same time, it creates opportunities to define fundamentally new classes of solvable problems and thus radically new use cases, for example with regard to issues such as self-centerlining of plants. Factors and framework conditions that were previously considered unchangeable can now be addressed.
These options can be implemented particularly effectively in the manufacturing industry. Here, AI can help to reduce capital commitment (Working Capital) as well as investment and operating costs (CapEx / OpEx) and avoid rejects and waste. Operational processes can also be made simpler, more flexible and more efficient and the productivity of machines and systems (OEE) can be increased. And last but not least, AI offers new approaches to identifying trends and accelerating and optimizing planning processes (Advanced Planning).
AI can therefore become a decisive factor in a market environment in which even small efficiency gains or knowledge advantages can have a decisive influence on the competitive position. By using AI solutions in a targeted manner, significant potential can be realized in various areas of operations:
Today, there are already a large number of successfully implemented AI projects in Research & Development (R&D), production and Supply Chain Management (SCM). ROI-EFESO's experience shows that these projects focus in particular on four central tasks:
The following application examples can be described as examples:
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
Challenges
Impact & Benefit
As a leading operations consultancy, ROI-EFESO has extensive experience with every aspect of industrial processes relating to development, supply chain, production, footprint and Industry 4.0 as well as their industry-specific characteristics and special features. We also work intensively with numerous research and implementation partners on technical and digital future topics relating to the AI industry. On this basis, we realize integrated and scalable AI use cases for our clients that create sustainable value and significantly improve their competitive position.
Our approach is based on a four-phase model that ensures successful project implementation:
Definition of a top-down and bottom-up strategy for the use of AI in order to identify the real added value of the solutions and prepare the company for this.
Development and implementation of solutions with the right IT architecture.
Continuous generation of knowledge in order to keep developing solutions and scaling them within the company.
Best possible preparation of the organization through training, qualification and change management in order to implement new solutions effectively.
Transformation through smart product development. A job for the "I-Team": A strong development team at a manufacturer of household appliances recorded solid success. But now customers want to network kitchen appliances, refrigerators and mixers in the “smart home”. ROI-EFESO established an "I-Team" with the fresh perspective of "digital natives" and accompanied the internal change with great success.
In the furniture industry, the use of digital technologies can pay off in several ways: with virtual reality, big data analytics or online configurators, additional sales channels can be opened up. With a globally represented bed manufacturer, ROI-EFESO implemented an "end-to-end digitization" project that took into account all relevant stages of value creation: from the customer experience to ordering to production and logistics.
An automotive supplier improved the transparency of work and organizational processes in a production plant for dashboards. With a "Digital Process Twin" from ROI-EFESO, the company reduced the reject rate and made improvement potentials in its value creation networks visible.
The energy market is data-driven, smart solutions determine the business model. In order to always be a step ahead of the competition, one thing is required: flexibility in thinking and acting. A utility company wanted to take the performance of its global R&D organization to a new level. In the first step, together with ROI-EFESO, it obtained a general overview of the respective degree of agilization of the various R&D units and processes.
Performance increase on the shop floor. Should every employee receive shift planning via smartphone? Or the plant management can compare solution proposals on a virtual dashboard in real time in the event of disruptions in the production flow? Thanks to the available IoT technologies, these and many other work simplifications of a digital shop floor management can be easily implemented today. Automobile manufacturers in particular like to take advantage of the opportunities offered by digitization and already have various systems in use, at all stages of maturity.
A manufacturer of thickening, binding and coating agents is established as a reliable supplier in the pharmaceutical industry. Master an Industry 4.0 transformation in 12 months and fulfill the growth strategy. With the first test pilots for the use of smart analytics, RFID trackers and a customer portal, the company is already on the right track. Because without digitization or Industry 4.0 technologies, the set goals cannot be achieved.
A manufacturer of machinery and special machines has already achieved a high level of automation in its production processes. Now the company is setting its sights on further, cross-departmental goals for process automation. Together with ROI-EFESO, it is defining fields of application in the operations area in which Robot Process Automation (RPA) tools are to ensure time savings and relieve employees.
The ROI-EFESO case study describes how a confectionery manufacturer is expanding its competitive position with a comprehensive digitalization initiative. With ROI-EFESO, it is expanding the successes already achieved by its WCOM (*World Class Operations Management) program in the direction of highly digitalized production.
The ROI-EFESO case study describes how a chocolate manufacturer started its digital transformation with ROI-EFESO. The goal: to activate potential in production and logistics to increase efficiency. The project team laid the foundations for this with a group-wide digitalization strategy and a roadmap for its implementation in several plants around the world.
