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Julio de 2020 Página 1 de 3

The 5 Key Steps to Deploy a Smart Manufacturing Shop

Dr.-Ing. Miguel Garzón

Today, the concept of the smart manufacturing factory gains relevance in times where the COVID-19 pandemic has pushed companies to re-invent themselves in order to provide products for possibly unexpected clients.

The following article is a summary in the form of five general steps which show the path that must be followed to start working according to the philosophy of smart manufacturing.

Product and development cycles are getting shorter and shorter. Therefore, the ability of a company to ready the production of a part for a customer in an agile manner will be a central criterion for the decision of who wins the contract. Metalworking companies will increasingly have to accompany their customers with their know-how. The workshops should include support services in the product development stage in order to guarantee an efficient design for manufacturing and in terms of costs from the beginning of the process. Clearly, here comes the application of new software technologies and even rapid prototyping to validate concepts early. Even the use of embedded sensor systems in the delivered products, which can feed back information during their final use for future improvements in subsequent versions, are required.In principle, a smart factory bases its success on the efficient exchange of information between operators, machines and resources in real time. A metalworking company based on these principles must be able to produce efficiently for the requirements of the current market: manufacturing small batches in an economically viable way, meeting time commitments with its customers and meeting the highest quality standards.

Each of the following presented steps will be accompanied by a set of tasks, aimed for the practical implementation of the concepts within the organization.

1. Knowledge management

The metalworking industry traditionally draws most of its know-how from the knowledge generated by their skilled workers within the organization. This knowledge is normally found only inside the heads of each of them and is not systematically stored or made available for others to access. In many cases, knowledge is still found in hard-to-access and probably outdated physical texts and manuals. In the context of Industry 4.0, knowledge and learning gain a lot of meaning. The collection of data from all stages of the process through the connection of all systems, machines, products and workers become a new source of knowledge. For this, workers must be given access to the network in an intuitive way, define who has access to what type of information and exercise discipline to keep databases up to date.

For workers, the use of these systems means securing their jobs through transparency of costs. It could even mean an increase in their compensation thanks to the savings generated by discovering hidden costs in the process. A reduction in their workload because a problem can be more easily discovered. It also means a type of social recognition, which is beginning to be understood with the use of social networks, which is generated with the successful exchange of experiences through the system.

A current challenge for the industry is that their staff must begin to have minimal knowledge of machine control, network techniques, and operating systems in order to take full advantage of these new technologies.

Task for implementation

Companies must digitize the physical information they possess and make it available in an internal database or webpage, so that it can be managed in a participatory manner to generate a continuous and healthy exchange of knowledge between collaborators of the company. One way to facilitate this implementation is by setting up an information access terminal for workers and, in the best of cases, giving them mobile electronic tablets to enter data into the system.

2. Online monitoring

Whenever possible, all the key data for a process should be acquired, digitized and structured. In initial stages of the implementation of monitoring 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 usage time), from which assembly and configuration times, dead times (errors, repairs, machine without production orders, etc.) can be calculated. Other data to be acquired can be the type of tool used, wear, initial hours of use, type of failure at the end of its useful life, etc. This type of information has yet to be supplemented by operators.

The objective of acquiring this data is to increase productivity, increase machine occupancy, minimize assembly times and downtime, in addition to understanding the reason for an incident with the machine. Its utility for planning of repairs and maintenance is also very high since the actual hours of operation of the critical components can be determined. Finally, having a data history of the entire production chain allows making more realistic cost calculation. It is also a beginning for the modelling of the behavior of the process based on its input variables and thus, the prediction of possible effects on the manufactured parts.

Unless the machines have the ability to deliver this data to a MES-type production information analysis and management system (Manufacturing Execution Systems), and even before investing in an external automatic monitoring system, all these data must be collected by workers manually in the most strict and disciplined way possible. As part of building a knowledge management culture, it is crucial that people are clear about the importance of retaining this data for analysis.

Palabras relacionadas:
How to Deploy a Smart Manufacturing Factory, Smart Factory, Online Monitoring, Digital Production Planning, Integration at the Customer Product Development Stage: Smart Innovation, Change in Metalworking Business Model, Smart Manufacturing in a Factory.

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