Lean Production Implementation on Coffee Beans Business

The goal of this research is to identify, inspect, and assess the existing system, performance, limitations encountered, and lean supply chain solutions that may be used in the banking industry’s account opening process. The qualitative exploratory analytical research approach was utilized, using a sample of state-owned banks such as Bank Mandiri, Bank Negara Indonesia (BNI), and Bank Rakyat Indonesia (BRI). The process of developing a lean supply chain strategy include identifying problems using SIPOC and VSM tools, analyzing problems using VALSAT tools, improving processes using FMEA tools, and controlling processes using SQC tools. The system of account opening that is now in use in the banking industry is partial, the performance of the banking industry’s account opening system is not ideal, some of the issues that have arisen as a result of the banking industry’s account opening system include, among other things, an increase in the number of employees and a lack of ergonomic in the design of the teller machine, and Process Activity Mapping may be used in the banking industry as a lean supply chain strategy

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“…Lean six sigma in the financial industry [46]. Manufacturing industries that practice lean production [47], [48].…”

Section: Literature Review and Propositionmentioning

confidence: 99%

Lean Supply Chain System in Indonesian Banking Industry

Sari

Nugraha²

2021

TSSJ

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“…Data from the International Coffee Organization (ICO) shows that Indonesian coffee consumption increases yearly [1]. The Indonesian government supports the coffee business and avoids low price pressures for coffee farmers [2], [3]. Indonesia's coffee production comprises 72% Robusta, 27% Arabica, and 1% Liberica.…”

Section: Introductionmentioning

confidence: 99%

Prediction measuring local coffee production and marketing relationships coffee with big data analysis support

Sinaga

Putra²,

Girsang³

2022

Bulletin EEI

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Following the increasing enthusiasm of the coffee market in Indonesia, a machine learning model is developed to study the relationship between coffee producers, consumers, production, and the market. Machine learning work flow is constructed in various stages; explore, develop, and validate the models. In this research, the building model predicts the production and market of late departure coffee based on labeled and unlabeled variables. The best predictions from the trained type of model algorithms of machine learning like tree accuracy of 85.7%, support vector machine (SVM) accuracy of 82.9%, and k-nearest neighbors, the accuracy of 82.9%, which produce three categories, namely, high production of 2 provinces, medium production of 21 provinces, and low production of 11 provinces. The accuracy classification is supported by the AUC value obtained for a high class, a medium class, and a low class. In addition, local coffee marketing modeling used in logistic regression was found with an accuracy of 88.9%, aiming to classify coffee interests between Arabica coffee and Robusta coffee. We found that the AUC value logistic regression for arabica coffee is about 0.94, while for Robusta is 0.92. The analysis of the classification modeling results shows a high level of accuracy of 93.0%.

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“…Saini and Singh (2019) investigated the impingement of lean manufacturing practices (LMPs) in small and medium enterprises (SMEs) of Northern India and their relationship with firm performance. Kartika and Setiawan (2020) investigated the lean production implementation in coffee beans businesses in Surabaya to achieve a competitive advantage in the production process to compete with other competitors. Kharub et al (2021) used lean six sigma to improve the production rate and reduce the waste of a south Indian food processing company.…”

Section: Introductionmentioning

confidence: 99%

How Does the Value Stream Mapping Method Identify Waste and Improve the Coffee Bean Production Process of a Café?

Wurjaningrum

Shafak

2022

JMTT

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Objective: This study aims to identify waste and its causes in the production process and provide suggestions for improvement. Design/Methods/Approach: The author uses a qualitative research method approach to obtain research data. The author identifies the waste that occurs using Value Stream Mapping (VSM). Identify waste in the coffee bean production process using structured question weighting and direct interviews with several parties related to the production process, including the café owner, production manager, head barista, and two baristas in the production section. Findings: The waste causes that occur in the identified coffee production process are waiting time due to the first stage sorting process still using hands manually, the absence of tools, defects caused by the inevitable side effects of the roasting process, and unnecessary motion. After knowing the cause of the waste that occurred, the company was given a suggestion for improvement using 5W-1H tools, such as using a coffee sieve of a certain size for the first stage sorting process, making a new product in the form of ground coffee, processing defect coffee beans that always exist from the roasting results, and adding a fan and exhaust in the roasting room to reduce unnecessary movement during the process. Originality: The paper contributes to the limited literature on lean production in the coffee processing business and disseminates this information to improve production efficiency.

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Lean Production Implementation on Coffee Beans Business

Cited by 3 publications

References 6 publications

Lean Supply Chain System in Indonesian Banking Industry

Lean Supply Chain System in Indonesian Banking Industry

Prediction measuring local coffee production and marketing relationships coffee with big data analysis support

How Does the Value Stream Mapping Method Identify Waste and Improve the Coffee Bean Production Process of a Café?

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