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Internet of Things

Often the ability of a smart factory is described as collecting data, analyzing and understanding them, aggregating them to meaningful information and predicting future events and finally even deriving decisions from them to influence and stabilize the future state-of-the-art process. In consequence, to succeed in regard to becoming a smart factory definitely depends strongly on the ability to picture a vision from the data utilization point of view but it requires a more holistic approach to harvest the full potential of digitalization.

The Lean Digital roadmap

A smart factory gets strongly influenced by the produced product itself, its design and functionality, by the information, described by its content, integration and software, by the organization with its in built efficiency plus interfaces and by the human beings knowledge and experience. For each of these areas, a seamless strategy has to be developed which fits to each other to a common lean digital road map which paves the way to the digital future by driving speed, flexibility, quality and productivity to the limits in a secure environment.

(Read also Ralf-Michael Frankes blogpost about A roadmap to the Digital Enterprise)

We call it lean digital roadmap, due to the fact that lean principles generally have to be applied before digitalization comes into the picture. When we think about a product, nowadays it definitely gets born digital and a 3D-model is often the base. But beside of this, it must be enhanced to a full functional description of the product in multi dimensions. This is required to derive later test, manufacturing and service strategies. All this data and information need to be stored in a data back bone to guarantee that all involved parties have access to the same single source of truth.

The journey to become a smart factory should always begin with becoming a lean factory first and can be characterized by several lean rules stated 1999 by Spear and Bowen. One rule is known as, that all work and process steps, shall be highly specified as to content, sequence, timing and outcome. With other words, processes must be described without any waste, but in difference to the past, artificial intelligence and machine learning can be used to make them even more efficient. On the other hand, you do not want to automate or digitize waste neither. As an example, a full automation line can be analyzed in the same methodology as you analyze material flow by value stream mapping.

Streamlining production processes

Simulation tools can help to use existing data and analyze them to evaluate the process. While counting the inventory you have in your line to get transparency regarding “work in progress” and therefore the relation between through put time and cycle time you can evaluate how much money you have spent in a full automation line to automate non value add contribution. In our studies we found out that between 50-70% of automation costs are related to handling, transportation and manipulation of the product and components and this is generally non value add.

The objective must be to streamline the production process steps even before you automate them and measure the effectiveness by the relation of costs spent for value added and non-value added automation. In the same way you can analyze your business processes in regards of the information handled in the work stream for your engineering or administrative tasks. Only streamlined processes can be automated and digitalized efficiently. With digital methods, the lean vision first time right could really become true due to virtual implementation of efficient and proved concepts of machines, lines and processes that enable a fast ramp-up and line utilization in the real world.

The importance of direct customer-supplier connections

Another lean rule states, that every customer-supplier connection must be direct, and there must be an unambiguous Yes or No to send requests and receive responses. This can be realized very efficiently with defined digital supplier interfaces with standard communication protocols for tracking and tracing. So called “digital knowledge management” supports the preventive and operative supplier management with sustainable benefits as a contribution of the business success. The full digital integration of suppliers and the early involvement in engineering and series production generates data transparency over the whole lifecycle, even opens new business models for service and improves data analytic potentials due to new data sources. In this context, thinking “lean digital” means also, that the pathway for every product and service must be simple and direct. Which aims for shorter lead times during product development and production introduction by utilization of the digital twins product, production and performance. New services via internet and cloud computing allow direct access to valuable insights about utilization of the product or the services.

From the plant development point of view, a very important lean rule states, that any improvement must be made in accordance with the scientific method, under the guidance of a teacher, at the lowest possible level in the organization. Personal experience shows that well established lean performance systems who are describing the principles, methods and tools are able to improve operations by about 3-5% p.a. The nature of lean principles is to drive, as stated the continuous improvement program on the lowest possible hierarchical level. Today software and information technology is used in limited way or using proprietary systems with no interconnection to other function, departments or even plants. In this area, re-thinking is necessary to participate from future digital solutions benefits.

Digital twin solutions and process-orientation

While productivity benefits via continuous improvement programs will melt down by better designed an virtually tested products, virtually commissioned on optimized production lines, the integration of digital twin solutions will drive further productivity by synergies and scale effects harvested best with new technologies, data analytics and consistency while reducing IT and software investment risks.

A common reason why some companies struggle in their smart factory initiative is that their vision of smart factory is more technology than business and process oriented. Those companies also often underestimate the effort and investment required to achieve their smart factory vision. Some reasons for example why artificial intelligence and reinforcement learning is so hard to put into production are that it requires an accurate simulation of the environment, or with other words a full digital twin of product, production and performance, which is way much harder for robotics and automation than for other areas.

Employing artificial intelligence to reduce investment risks

Typically, such algorithms require profound domain knowledge and a massive number of training iterations before they converge to meaningful information making the outcome uncertain, expensive and in many cases difficult to reproduce. It looks like, that many production and automation challenges can be solved with artificial intelligence and the investment risk can be lowered by following principal lean implementation guidelines while rethinking the basic continuous improvement approach.

Digitalization enables customized information on the right level of employees. This can lead to powerful solutions with immediate impact while closing the gap between ideation and production with digital twin solutions. Nevertheless, the way to a fully lean digital factory is not a short distance sprint. It takes long breath with a clear implementation path with clear defined evaluation steps and further productivity benefits will come first due to lean (1 to 2 years), than via further automation (2 to 3) and later due to digitalization solutions (3 to 5 years).