Digitalization and networking are not just about connected driving and new mobility concepts for the automotive industry. Manufacturers and suppliers in production and logistics are also faced with new challenges represented by “Industry 4.0.” But what characterizes a "smart factory?" How can manufacturing companies use the Internet of Things as part of a future solution? How must the organization and its people adapt to the digital future?
BearingPoint is looking at solutions for current challenges in automotive production networks, from horizontal and vertical integration to the issue of how increasingly more existing data and data analyses can be used for process optimization.
An exemplary challenge is the ever-shorter go-to-market cycles, which require fast and error-free development processes. Digitalization and increasing connectivity are already enabling development and planning processes to optimize a virtual image of products, production facilities and entire plants. This is referred to as the digital twin, meaning the digital image of real objects.
BearingPoint experts agree that the digital twin has valuable potential to increase efficiency. The basis for the virtual image are intelligent 3D models, which are supplied in real time with all the process parameters and functions of the real object. This allows for a realistic simulation in a computer-assisted environment. The aim is that this model provides all the information that would be obtained during a physical inspection. By feeding real-time data into the model, predictions of malfunctions can be made or software can be used to optimize the process.
This new volume of information opens up completely new possibilities. During the conception and planning phases of production plants, the user can already use the virtual environment. For example, a digital prototype can be adapted at any time with little effort, ensuring uncomplicated commissioning.
The amount of collected data is uploaded to a cloud, guaranteeing access to information for later tooling change, maintenance or service. Using the digital image, service staff can quickly and easily handle and solve many different problems. In addition, logistics processes and material stocks can be surveyed and coordinated efficiently in the shortest possible time.
As a result, clients benefit from the following advantages:
Digital Process Twin transforming your factory into a smart autonomous one
In addition to the interlinking of development and planning processes for rapid market readiness, efficient production and logistics have become a decisive success factor. Despite the need to cut costs, it is expected that logistics processes will work flawlessly with minimal costs. Many companies find it difficult to balance these two contradictory goals.
BearingPoint helps clients achieve logistics excellence with the in-house software solution Factory Navigator. The Factory Navigator is an innovative software product for the simulation and optimization of production and logistics processes. The application areas are diverse. The Factory Navigator addresses and solves logistics problems from storage to distribution.
Typical challenges are:
BearingPoint experts use the Factory Navigator software to help clients achieve challenging goals. Through innovative analysis functions, the Factory Navigator is able to provide in-depth understanding through precise models and to capture processes at the right detail level.
These results are used to optimize logistics processes, carry out risk assessments or reduce costs. What-if comparisons allow for a quick and risk-free assessment of the proposed changes.
The software architecture of the Factory Navigator enables automated model generation from various data sources such as SAP ERP, thus providing an all-time feature on the market. Tailor-made analytical models are automatically created from company-specific data using a variety of interfaces. Consequently, the results are precise and the problem-solving cycles short and cost-effective.
As a result, the following advantages are obtained for the user: