This study investigates the effect of COVID-19 lockdowns and non-lockdown phases on managers and employees in the Czech Republic in the year 2020. The Czech Republic came through the first COVID-19 wave in spring 2020 with low case numbers, but became one of the countries with the highest case incidences in the second autumn wave in Europe. The study focused on examining the differences of perceptions on digital readiness of the company, working style, and mental health variables of working personnel in lockdown and non-lockdown phases. Data was obtained by an online survey conducted monthly from March-2020 to December-2020 with the same questions each month. Collected data consisted of respondents’ basic information on the actual situation, on perceptions on company and technology and on perceptions of the own mental state in the given month, retrieved from a pool of employees and managers from the Czech Republic machine and equipment manufacturers’ industry. Statistical analysis was conducted with the Kruskal-Wallis test for ordinal variables to check for significant differences in perceptions during 2020. Results show that managers in general and telecommuting-experienced workers in particular are better able to adapt to forced home office, while telecommuting-inexperienced employees struggle to adapt positively even with increasing company support and with an increasing digital team communication.
The paper deals with the development and application of computer-aided standardisation (CAS) for the distribution of standardisation data of production and maintenance processes within the company network. Rising integration pressure of company software tools also include CAx technologies. These CAx technologies provide software solutions for different applications, being able to work closely together with ERP-systems, Business Intelligence (BI) tools and further systems. The possibility of integration makes it also feasible to look on the CAS tool and its potential. While CAS is a topic not yet fully discussed, future development may lead to the requirement of integrating the CAS with the ERP and planning system. Approaches, such as CIM, digital factories (DF) and Industry 4.0 benefit from a broader database available with the CAS system employed
This study investigates the application of a smart manufacturing execution system (SMES) based on the current controlling structure in a medium-sized company in the Czech Republic. Based on existing approaches on the architecture of SMESs, this paper develops a sample architecture grounded in the current controlling structure of small and medium-sized enterprises (SMEs). While only a few papers on approaches to the given topic exist, this approach makes use of operative production controlling data and uses a standardisation module to provide standardised data. The sample architecture was validated with a case study on a Czech SME. This case study was conducted on two different entities of one production company suggesting differences in the entities due to the nature of production. The research showed that simple tasks with intelligent welding equipment allow for a working SMES architecture, while complex assembly works with a high extent of human labour, and a high number of components still remain an obstacle. This research contributes to gathering more understanding of SMES architectures in SMEs by making use of a standardisation module.
Purpose of the article: The paper focuses on the potentials and benefits controlling provides for companies in the transition period towards industry 4.0. Operative production controlling provides data that shall be used in the future to apply the concept of smart factories. This article proposes a controlling archtitecture based on computer-aided standardization. Methodology/methods: The paper develops an architecture on operational production controlling based on an international literature review. Literature on controlling 4.0 are found mostly in publications in German language. While this literature has its focus on controlling as a whole or on strategic controlling, the paper has a look on operational controlling and its further usage and development towards smart factories. Scientific aim: The aim of this article is to develop a model of an operational production controlling architecture that is able to suite the requirements of smart factories by using computer-aided standarization. Findings: Research is working on concepts for industry 4.0 and its way towards real implementation. Competitive advantage in industry 4.0 is created through digitization and robotization. An architecture that fully complies to industry 4.0 is still waiting in real companies due to technical limitation in data storing, retrieval and processing as well as storage capacities. Conclusions: The paper discussed the devlopment of a controlling architecture suitable for industry 4.0. Already today controlling is making use of data. Smart factories shall make use of production data. Production controlling together with the CAS, that is able to provide standardized data on all manufacturing, maintenance, and auxiliary processes, systems are able to make a step forward towards smart factories. The concept of production controlling combined with the strengths of a CAS may be seen as the basis from which to target smart factories and industry 4.0.
Six sigma is understood as a technique for the continuous improvement in process quality; however, it has been rarely scientifically analysed in small- and medium-sized enterprises (SMEs). SMEs representthe vast majority of enterprises throughout economies and contribute to automotive supply chains in various tier ranks. As SMEs are known to lack resources and skills while focusing on short-term benefits rather than on long-term gradual improvements, the aim of of this paper is to analyse the perception of six sigma process capabilities in automotive supply chains assuming differences in company size, supply chain rank and six sigma duration. This was tested with Fisher’s exact test. Companies with less than 1000 employees, subsuppliers and companies with a six sigma implementation in the last 3 years struggled to meet six sigma principles, suggesting that mainly small companies inhibit a risk for the supply chain. These findings contribute to the existing theoretical body of knowledge by identifying a three-to-five-year period for six sigma implementations until six sigma maturity. Practically, the findings contribute to the research by explaining the need for a continuous supplier development over a three-to-five-year period until the company meets its performance requirements, with a supply chain risk incorporated in lower-tier ranks and with small companies.
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