Abstract
Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization. Data that are analyzed or entered manually are subject to human error. Digitization can remove human error, when dealing with data, via automatic extraction and data conversion. This paper presents methodology to identify automation opportunities and eliminate manual processes via digitized data analyses. The method uses a hybrid combination of Lean Six Sigma (LSS), CRISP-DM framework, and “pre-automation” sequence, which address the gaps in each individual methodology and enable the identification and analysis of processes for optimization, in terms of automation. The results from the use case validates the novel methodology, reducing the implant manufacturing process cycle time by 3.76%, with a 4.48% increase in product output per day, as a result of identification and removal of manual steps based on capability studies. This work can guide manufacturing industries in automating manual production processes using data digitization.
Highlights
The manufacturing industry contributes to 15.39% of the global gross domestic product (GDP) and 22.8% of overall employment [1,2]; it makes up more than 20% of the GDP in the EU [3]
Lean and Six Sigma were previously used in conjunction because Six Sigma uses statistical means to improve the process and Lean focuses on value stream mapping (VSM) for reducing waste and non-value-added operations [31]
The combined Lean Six Sigma (LSS), CRISP-DM methodology, and the “pre-automation” sequence was successfully executed, and the process was improved in terms of reduced manual steps, reduced cycle times, and improved product output by using newly digitized computer numerical control (CNC)–coordinate measuring machine (CMM) data
Summary
The manufacturing industry contributes to 15.39% of the global gross domestic product (GDP) and 22.8% of overall employment [1,2]; it makes up more than 20% of the GDP in the EU [3]. Manufacturing output is growing as more machines are being installed on factory floors in order to maximize machine availability, and satisfy product quality, delivery, and cost demands for the customers [4]. According to Fera et al [5], companies can gain competitive advantages by focusing on the six competitive priorities: quality products and services, reliability, flexibility, speed, cost, and innovation. Quality is arguably the most important priority for sustaining business and maintaining customer satisfaction in such industries [6]. The key elements that impact a company’s decision to improve quality are: cost, risk management, firm size, and the cost implications of scraps [7]
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