AI in the manufacturing industry.

Smart Production Intelligent Factories of Tomorrow: The Future of AI-Supported Production

2024-05-15 A guest post by Dimitri Schweigerdt* | Translated by AI 5 min Reading Time

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The incorporation of AI into the manufacturing industry has long since begun. However, several hurdles still need to be overcome for the industry-wide shift towards data-driven production. The DataFactory.NRW project provides a pioneering blueprint for the factory of tomorrow.

The DataFactory.NRW project is intended to serve as a blueprint for manufacturing companies in North Rhine-Westphalia and beyond.(Image: Fraunhofer IEM / Janosch Gruschczyk) — The DataFactory.NRW project is intended to serve as a blueprint for manufacturing companies in North Rhine-Westphalia and beyond.
(Image: Fraunhofer IEM / Janosch Gruschczyk)

Dimitri Schweigerdt is Project Manager Smart Factory, Global Innovation & Industry Consulting at NTT Data Business Solutions.

The increasing digitalization of production environments through Artificial Intelligence is becoming particularly important for medium-sized companies in the manufacturing industry. In the future, these companies can undergo the transformation towards a data-driven Smart Factory, in which all processes and production resources are optimized based on AI.

On the path to intelligent production, data is crucial as it forms the basis for the transformation into smart factories. Pallets effortlessly making entries and exits in the rack warehouse without human intervention at the computer, or an innovative application for guiding tractors in yard logistics, which allows storing the GPS coordinates of a parked vehicle and directly transmitting them to the SAP system, are examples of AI-supported production. Currently, the real transformation is primarily failing due to lack of networking and cooperation between companies and research institutions, as well as inadequate data strategies and information architectures for optimal use of existing potentials. Indispensable for the digital transformation of the industry is a tried and tested and reliable blueprint. This is where the project "Data Factory.NRW - Artificial Intelligence in Production" begins.

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The solutions developed in the project are integrated into the existing IT architecture of the companies.(Image: Fraunhofer IEM / Janosch Gruschczyk)

In the digital transformation, the initial focus is on the implementation of intelligent solutions in industrial production.(Image: Fraunhofer IEM / Janosch Gruschczyk)

Focus on Artificial Intelligence: DataFactory.NRW shapes the factory of the future

A consortium of nine actors from industry and science, including the Fraunhofer Institute and the SAP consulting firm NTT Data Business Solutions, are pooling their expertise to significantly advance the optimization of production using artificial intelligence. The project is considered groundbreaking and is set to provide a blueprint for manufacturing companies in North Rhine-Westphalia and beyond. The goal: The development of best practices and reference concepts for companies. The focus here is on the areas of factory planning and engineering, production and manufacturing, as well as logistics. The project aims to accelerate the development of intelligent solutions, taking into account the latest technologies for manufacturing companies. It is also set to boost networking and cooperation between companies and research institutions.

A partner in the Data Factory.NRW project is NTT Data Business Solutions, which contributes its comprehensive IT expertise and industry knowledge. In this context, NTT Data Business Solutions advises and sensitizes the user companies, for example, whether it makes sense to use SAP solutions instead of in-house developments. The solutions developed from the project are integrated into the companies' existing IT architecture. About ten experts from NTT Data Business Solutions are working on the data factory. The first prototypes – such as a worker assistance system for assembly that supports staff with 3D and augmented reality instructions – have already been completed.

Fields of action and transformation areas on the way to the Smart Factory

The transformation from traditional manufacturing to smart production requires companies to particularly consider three crucial fields of action. Firstly, the implementation of intelligent solutions in industrial production is crucial. Here, various technologies are used that use data to optimize manufacturing, such as Automated Guided Vehicles, Cyber Physical Systems, or the Industrial Internet of Things. The subsequent step is the integration of these intelligent solutions into real work environments, making them an integral part of an overarching information architecture that connects all relevant data sources and targets. A stringent organization is indispensable for the success of the endeavor. Because the introduction and synchronization of intelligent solutions must be carefully planned and controlled to ensure a smooth transformation into a Smart Factory.

The three central fields of action for the transformation from traditional manufacturing to intelligent production are applied in concrete terms in the project in four transformation areas: Data-Driven Engineering, Data-Driven Manufacturing, Data-Driven Logistics, and Data-Driven Enterprise Architecture. These transformation areas, also referred to as Transformation Areas, represent typical areas of task where manufacturing companies carry out concrete activities. As part of the Data Factory.NRW project, these activities are defined in the form of structured work packages and use cases that are progressively implemented:

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Transformation Area 1: Data-Driven Production Engineering: This area focuses on how production data can be used to improve factory planning and optimization. Objectives include the creation of digital twins of the factory and production, the use of AI-based methods to support factory planning and optimization, and data-driven decisions for increased efficiency, quality, and flexibility of production. The development of learning factories, which can continuously adapt to changed requirements, is another aspect.
Transformation Area 2: Data-Driven Manufacturing: The focus here is on data-driven manufacturing to ensure future competitiveness. Six work packages address issues relating to human-machine interaction and the integration of data for added value and accurate predictions of machine maintenance needs. The goal is comprehensive digitization and the application of AI in various production areas, thereby effectively aligning the IT and production landscape towards data-driven processes.
Transformation Area 3: Data-Driven Logistics: The development of data-driven AI solutions for the entire logistics value chain is the focus here. From goods delivery to picking and assembly, research areas such as AI in inbound logistics, intra-company transport, provisioning, picking, and assembly are addressed. Implementation is carried out through already formulated use cases, data analysis, training of AI models, piloting, proof of concept, and integration into business processes.
Transformation Area 4: Data-Driven Enterprise Architecture: The fourth Transformation Area has the task of creating a consistent process and IT landscape for the project. By working holistically across all work packages, solutions should be developed that interlock seamlessly and form a stable IT environment. Ensuring synergy effects between different transformation areas and the integration of solutions through modeling and regular meetings for coordination is crucial.

Successes, challenges and the path to the future to the AI factory of tomorrow

With the successful start phase of Datenfabrik.NRW, it becomes clear how significant the collaboration of different expertise is in advancing pioneering future projects. The project demonstrates how ongoing digitization of production environments through AI opens up new horizons for small and medium-sized companies in the manufacturing industry. The focus here is on the next steps in the Transformation Areas. In Data-Driven Production Engineering, the development of a design guideline for digital factory layouts, the selection of methods for semi-automated standard time determination, and the validation of this standard time are pending. In the Manufacturing area, recommendations for action to minimize routine tasks are to be developed and user-friendly worker assistance systems are to be partially implemented and validated. An initial validation of the forecast in the delivery and material disposition, as well as the testing of algorithms for freight bundling, form the next steps in Data-Driven Logistics. In Data-Driven Enterprise Architecture, a fit-gap analysis is planned in perspective and a concept for the technical infrastructure for the integration of the project solutions is being developed. These next developments not only represent the continuation of a project that is already successful; they further break up the still partially existing silo thinking and thus pave the way for the AI factory of tomorrow.