The 5 key steps for a successful industrial digitalization
Today, the concept of industrial digitalization or the smart factory is gaining in relevance at a time when the COVID-19 pandemic has pushed companies to reinvent themselves in order to supply products to potentially unexpected customers.
The following article is a summary in the form of five general steps that show the way to start working according to the intelligent manufacturing philosophy.
Product and development cycles are becoming ever shorter. Consequently, a company's ability to prepare the production of a part for a customer in an agile manner will be a central criterion in deciding who wins the contract. For example:
Industrial companies will increasingly need to support their customers with their know-how. Workshops should include support services in the product development phase to ensure a cost-effective design for manufacturing right from the start of the process. .
Clearly, here is the application of new software technologies and even rapid prototyping to validate concepts as quickly as possible. Even the use of sensor systems integrated into the products delivered; which can feed back information during their final use for future improvements in subsequent versions, is necessary.
In principle, the success of an intelligent plant depends on the efficient exchange of information between operators, machines and resources in real time.
Every industrial company needs to be able to produce efficiently to meet the demands of today's market: manufacturing small batches in an economically viable way; meeting deadlines with customers and meeting the highest quality standards.
Thus, each of the steps presented below will be accompanied by a set of tasks aimed at the practical implementation of the concepts within the organization.
1. Knowledge management
Industry traditionally derives most of its know-how from the knowledge generated by its skilled workers within the organization. This knowledge is normally found only inside the heads of individual workers, and is not systematically stored or made available to others.
In many cases, knowledge is still to be found in physical texts and manuals that are difficult to access and probably obsolete.
In the context of industrial digitalization, knowledge and learning take on a whole new meaning. Collecting data from all stages of the process by connecting all systems; machines, products and workers becomes a new source of knowledge. For this to happen, workers need to be able to access the network intuitively; define who has access to what type of information; and be disciplined in keeping databases up to date.
For workers, using these systems means securing their jobs through cost transparency.
It could even mean an increase in their remuneration thanks to the savings generated by the discovery of hidden costs in the process. A reduction in their workload because a problem can be detected more easily. It also means a kind of social recognition; which is beginning to be understood with the use of social networks, which is generated with the successful exchange of experiences across the system.
A current challenge for the industry is that its staff must begin to have a minimum knowledge of machine control, network techniques and operating systems in order to take full advantage of these new technologies.
Practical implementation
Companies need to digitize the physical information they possess and make it available in an internal database or web page; so that it can be managed in a participative way to generate a continuous and healthy exchange of knowledge between company employees. One way of facilitating this implementation is to set up an information access terminal for workers and; in the best case, give them mobile electronic tablets to enter data into the system.
2. Online monitoring
Wherever possible, all key process data should be acquired, digitized and structured. In the early stages of implementing follow-up processes, it is important to obtain data on the number of parts (and if possible, the number of good parts). Acquire actual production hours (spindle uptime); from which set-up and setup times; idle times (errors, repairs, machine without production orders, etc.) can be calculated. Other data to be acquired may be the type of tool used, wear and tear, first hours of use, type of breakdown at end of service life, etc. This type of information still needs to be defined. This type of information still needs to be completed by the operators.
The aim of acquiring this data is to increase productivity; increase machine occupancy; minimize assembly and downtime; and understand the reason for a machine incident.
Its usefulness for repair and maintenance planning is also very high, since the actual operating hours of critical components can be determined. Last but not least, having historical data for the entire production chain enables more realistic costing. It is also a starting point for modeling process behavior as a function of input variables, and thus for predicting possible effects on manufactured parts.
Unless the machines have the capacity to provide this data to a MES (Manufacturing Execution Systems) type production information management and analysis system; and even before investing in an external automatic monitoring system; all this data must be collected manually by workers in a strict and disciplined manner.
As part of building a knowledge management culture, it's essential that people clearly understand the importance of retaining this data for analysis...
The latest operating data acquisition system solutions record values directly from the machine and from auxiliary sensors; synchronizing them with actual production parameters (speeds, feeds, depth of cut, effort, energy consumption, etc.); also enabling real-time visualization so you can see why changes in productivity and quality are actually occurring.
Having data at your fingertips and acquiring knowledge about the influence of different production parameters on the final result; allows greater flexibility when making decisions in the event of unexpected changes.
In this way, something radical is happening in business...
Organizing each process according to its own conditions eliminates the need for rigid, time-consuming planning. What's more, the type of purely routine work that has prevented many plant operators from using their analytical skills to solve problems; now enables them to make decisions during the process due to the consequences of variations in different parameters. In this way, they are empowered to generate alternative solutions that make production more efficient; thus increasing the company's internal knowledge and adding value to the production chain.
Practical implementation
Invest in software for data collection, storage and analysis. In addition, it is necessary to invest in direct communication systems between machine controls and software systems; plus additional external monitoring systems to measure critical process variables that the machines themselves do not provide. This includes manufacturing, quality control, assembly and packaging. All these systems need to be connected to the central network, and the data stored synchronized in time for detailed analysis. Companies are advised to start in stages, and to choose a turnkey system to reduce set-up times and investments in time and money.
3. Digital production planning
The production transparency generated by automatic data acquisition enables companies to plan their production lines much more efficiently. A data acquisition and analysis system enables us to observe in real time the "expected" state of production, compared with the "current" state, not only in terms of the parts manufactured, but also the availability of personnel and materials (raw materials, machines, tools, etc.).
By carrying out this procedure digitally, workers always have an up-to-date version of the status of the entire line, and can act with the latest information, often without waiting for the production manager's order.
Production planning systems make it possible to include standardized rules for prioritizing decisions, thus contributing even further to the workflow. It is even possible, on the basis of experience gained from past data, to simulate the effect of changing the order of manufacturing stages on final delivery times, including assembly and quality control stages.
All this is a fundamental basis for continuous improvement initiatives, in which workshop workers are clearly increasingly involved.
The next stage, derived from the use of this type of system, is that of automated control of the production line, based on predictions generated from the analysis of information tracked from all production stages. This stage, which requires effective communication at all levels of the company, is fed by historical data and only becomes effective several months after commissioning.
Practical implementation
Workers should be responsible for filling in the necessary (mandatory) information to complement that received by automated systems. Filling in this information ensures that the data stored in the system can be analyzed at a later date. Production management must therefore ensure that the data collection interface is intuitive and quickly accessible on the shop floor.
4. Integration into the product development phase
The aim of intelligent innovation is to accelerate the customer's path to the final product, by supporting him with the company's own knowledge. To achieve this, it is advisable to integrate into the customer's development phase to increase the degree of innovation of the final product and ensure that the design is easily manufacturable.
Nowadays, there are many solutions to the concept of digital twins.
A product manufacturer can use it to digitally describe his production line; including all NC machine movements, holding equipment, automation and even assembly; without ever having to consume any of his raw material.
In this way, it can help with early detection of your customer's design flaws; proving to be an excellent opportunity to use shop floor experience to improve product performance. In addition, a digital matching approach can be used to generate feasibility studies; to help the customer check whether the product is also optimized from a cost point of view.
Practical implementation
Investment in the use of augmented reality systems to compare geometries or assemblies with existing CAD. Simulation systems for production processes and structural design enable us to enter the customer's development phase, providing key manufacturing knowledge that may be lacking. The use of 3D printing systems for prototyping purposes can also increase customer responsiveness under certain conditions.
5. Change of business model
Because of the collaboration between the production shop and its customers, the boundary between product and service is blurred. In this way, a customer doesn't ask for a specific product; he asks for a complete solution.
Ideally, industrial digitization should lead plants to increase their portfolio of services for their customers.
In the case of workshops that machine complex parts, such as tool and die makers; they need to complement their molds with sensors and data processing. The latter because molds can increase the efficiency of plastic part production for the customer; by being connected to production machines and operators; to control the process on the basis of more complete information; guaranteeing greater productivity with higher quality. The moldmaker becomes a data service provider; and "not just" a metal cutter.
Practical implementation
Include in the organization's structure the ability to generate intelligent solutions that help customers improve their productivity with the products on offer, through services that use the disruptive capability of technology developed for Industry 4.0.
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