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Applied Engineering to Lean Manufacturing Production Systems

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A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (18 February 2020) | Viewed by 75900

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Prof. Dr. Jorge Luis García-Alcaraz

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Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juárez, Ciudad Juarez 32310, Chihuahua, Mexico
Interests: lean manufacturing; supply chain optimization; lean supply chain; sustainable supply chain; environmental impact
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cuauhtémoc Sánchez Ramírez

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División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba 94320, Veracruz, Mexico
Interests: supply chain management; supply chain simulation; system logistics and system dynamics modeling
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Special Issue Information

Dear Colleagues,

Lean Manufacturing (LM) is a philosophy applied to production systems that focuses on waste reduction (overproduction, waiting time, transportation, excess processing, inventory, movement and defects) in manufactured products [1,2]. However, LM relies on a series of tools and techniques to achieve its objective, which are based on concepts from different applied sciences.

Some authors declare that there are 25 common lean manufacturing tools; all of them focus on waste elimination and available resource optimization, where engineering techniques and basic science are applied [3]. For instance, some LM tools require the application of statistical techniques to perform sampling on a characteristic or attributes in a production line, debug information and to determine a quality situation in a production process, and then make proposals for improvement, which have a foundation in statistics [4]. Similarly, to offer product guarantees, companies perform reliability tests and accelerated life tests to determine a warranty period for their products, which are based on statistical inferences [5].

Likewise, a number of models are implemented for production process optimization to maximize or minimize characteristics or attributes that are based on integral and differential calculus, accelerated approach methods, among others. In addition, these applications are found in reorder points inventory management, in deterministic and stochastic operation research, where uncertainty and risk are integrated into the estimates. In other words, lean manufacturing tools apply a wide variety of engineering and applied science techniques.

Furthermore, this Special Issue is aimed to identify tools and methodologies, as well as applications that managers are using to improve their lean manufacturing production process, which allow them to generate a competitive advantage for their companies, as well as keep the company in the globalized market with low-cost products. Additionally, all the selected papers must report on examples or case studies that help to understand any lean manufacturing tool in the real world, where they illustrate how managers are focused on cost reduction, variability reduction, problem solving, and algorithms that seek to optimize resources in production process, among others. Additionally, the examples may come from some sectors such as automotive, aerospace, agricultural, healthcare, tourism, mining, forest, just to mention a few. In addition, the Special Issue is open to receive theoretical, case studies, and real-world contributions in different topics and aspects related to lean manufacturing applications.

References

Kumar, M.; Vaishya, R.; Parag. Real-time monitoring system to lean manufacturing. Procedia Manufacturing 2018, 20, 135-140.
Sartal, A.; Llach, J.; Vázquez, X.H.; de Castro, R. How much does lean manufacturing need environmental and information technologies? Journal of Manufacturing Systems 2017, 45, 260-272.
Karam, A.-A.; Liviu, M.; Cristina, V.; Radu, H. The contribution of lean manufacturing tools to changeover time decrease in the pharmaceutical industry. A smed project. Procedia Manufacturing 2018, 22, 886-892.
Kiran, D.R. Chapter 22 - kaizen and continuous improvement. In Total quality management, Kiran, D.R., Ed. Butterworth-Heinemann: 2017; pp 313-332.
Podolyakina, N. Estimation of the relationship between the products reliability, period of their warranty service and the value of the enterprise cost. Procedia Engineering 2017, 178, 558-568.

Prof. Dr. Jorge Luis García-Alcaraz
Prof. Dr. Cuauhtémoc Sanchez Ramírez
Guest Editors

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Keywords

5S
Andon
Bottleneck Analysis
Continuous Flow
Gemba (The Real Place)
Heijunka (Level Scheduling)
Hoshin Kanri (Policy Deployment)
Jidoka (Autonomation)
Just-In-Time (JIT)
Kaizen (Continuous Improvement)
Kanban (Pull System)
KPIs (Key Performance Indicators)
Muda (Waste)
Overall Equipment Effectiveness (OEE)
PDCA (Plan, Do, Check, Act)
Poka-Yoke (Error Proofing)
Root Cause Analysis
Single-Minute Exchange of Dies (SMED)
Six Big Losses
SMART Goals
Standardized Work
Takt Time
Total Productive Maintenance (TPM)
Value Stream Mapping and Visual Factory

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Published Papers (9 papers)

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Editorial

Jump to: Research

2 pages, 192 KiB

Open AccessEditorial

Special Issue on Applied Engineering to Lean Manufacturing Production Systems

by Jorge Luis García-Alcaraz and Cuauhtémoc Sánchez Ramírez

Appl. Sci. 2022, 12(17), 8609; https://doi.org/10.3390/app12178609 - 28 Aug 2022

Cited by 1 | Viewed by 1431

Abstract

In industrial production processes, different techniques, tools, philosophies, and methodologies are applied to facilitate their management and control [...] Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

Research

Jump to: Editorial

15 pages, 5080 KiB

Open AccessArticle

A System Dynamics Model to Evaluate the Impact of Production Process Disruption on Order Shipping

by Cuauhtémoc Sánchez-Ramírez, Rocío Ramos-Hernández, José Roberto Mendoza Fong, Giner Alor-Hernández and Jorge Luis García-Alcaraz

Appl. Sci. 2020, 10(1), 208; https://doi.org/10.3390/app10010208 - 26 Dec 2019

Cited by 9 | Viewed by 3508

Abstract

Multiple studies have analyzed the importance of preventive maintenance. Similarly, they have developed and/or evaluated various spare part inventory management policies that help companies reduce the duration of production disruptions resulting from sudden mechanical failures. All of these studies assess the impact of mechanical failures on local aspects, such as idle times or management policies. However, they do not holistically evaluate how machine failures affect key performance indicators. To address this gap, we proposed a System Dynamics (SD) model to systemically analyze the impact of machine part failures on order shipping, a key performance indicator. Similarly, we assessed the impact of machine part failures on the levels of the company’s finished goods inventory. The research was conducted in a glass bottle manufacturing company. To increase order shipping as a performance indicator, we identified the key variables of the production process and conducted a sensitivity analysis of the variables. Our results indicate that it is possible to reach the company’s 98–100% complete delivery policy. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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23 pages, 13017 KiB

Open AccessArticle

Implementation of Lean Manufacturing to Reduce the Delivery Time of a Replacement Part to Dealers: A Case Study

by Carlos Eleazar Pérez-Pucheta, Elias Olivares-Benitez, Hertwin Minor-Popocatl, Prudencio Fidel Pacheco-García and Marcos Fernando Pérez-Pucheta

Appl. Sci. 2019, 9(18), 3932; https://doi.org/10.3390/app9183932 - 19 Sep 2019

Cited by 22 | Viewed by 7334

Abstract

In today’s automotive industry, Lean production systems are used successfully to reduce delivery times. The current case study addresses a problem that affects an automotive company, which is the excessive delivery time of a spare part to its both national and international authorized dealers. In order to reduce the delivery time of this replacement part, the Lean Manufacturing methodology was used. For this purpose, the value stream mapping and the proposed A3 report are the tools used. With the use of these tools, activities that did not add any value are eliminated or modified; in addition, the logistical flow of the modules of the door-side trim panel delivery process is improved. As a result, added value is increased, the delivery time is reduced (for Mexico) and the number of product variants is reduced. Now, the painting process is done by the authorized dealers, and the number of pieces used for every spare part was estimated. The study demonstrates that the integration of value stream mapping administrative/productive in conjunction with the A3 report proposal allows to identify and eliminate waste in the delivery process. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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21 pages, 6551 KiB

Open AccessArticle

Contamination Improvement of Touch Panel and Color Filter Production Processes of Lean Six Sigma

by Chia-Nan Wang, Po-Chih Chiu, I-Fang Cheng and Ying-Fang Huang

Appl. Sci. 2019, 9(9), 1893; https://doi.org/10.3390/app9091893 - 8 May 2019

Cited by 5 | Viewed by 4818

Abstract

Color filter (CF) and touch panel (or touch sensor (TS)) are important components for optoelectronic materials and component manufacturing. Due to the cut-throat world of market in the manufacturing, the processes of color filters are similar to touch sensors. The case invested in the production of touch panels in 2009. After a long period of quality improvement, the problem of contamination pollution still accounts for ~30% of the total variation. In addition to the external problem, there is also the fail of communication caused by dirt or peeling. Therefore, the case was established to improve the dirt defect by setting up Lean Six Sigma project, and the project goal was to reduce the proportion to 0.18%. After three months of improvement and three months of continuous observation, the abnormal proportion of pollution decreased from 0.35% of the overall average defect loss to 0.13% (the improvement rate reached 63%). It is estimated that the entire product can generate 3 million (USD) of performance for the case in one year. It is also possible to increase the customer’s satisfaction and the company’s technical competitiveness by improving yield and achieving the continuous improvement of objectives. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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13 pages, 415 KiB

Open AccessFeature PaperArticle

Hesitant Fuzzy Linguistic Term and TOPSIS to Assess Lean Performance

by Luis Pérez-Domínguez, David Luviano-Cruz, Delia Valles-Rosales, Jésus Israel Hernández Hernández and Manuel Iván Rodríguez Borbón

Appl. Sci. 2019, 9(5), 873; https://doi.org/10.3390/app9050873 - 28 Feb 2019

Cited by 18 | Viewed by 3786

Abstract

Manufacturing companies usually expect strategic improvements to focus on reducing both waste and variability in processes, whereas markets demand greater flexibility and low product costs. To deal with this issue, lean manufacturing (LM) emerged as a solution; however, it is often challenging to evaluate its true effect on corporate performance. This challenge can be overcome, nonetheless, by treating it as a multi-criteria problem using the Hesitant Fuzzy linguistic and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. In fact, the hesitant fuzzy linguistic term sets (HFLTS) is vastly employed in decision-making problems. The main contribution of this work is a method to assess the performance of LM applications in the manufacturing industry using the hesitant fuzzy set and TOPSIS to deal with criteria and attitudes from decision makers regarding such LM applications. At the end of the paper, we present a reasonable study to analyze the obtained results. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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18 pages, 3060 KiB

Open AccessArticle

Integrating Simulation-Based Optimization for Lean Logistics: A Case Study

by Jorge González-Reséndiz, Karina Cecilia Arredondo-Soto, Arturo Realyvásquez-Vargas, Humberto Híjar-Rivera and Teresa Carrillo-Gutiérrez

Appl. Sci. 2018, 8(12), 2448; https://doi.org/10.3390/app8122448 - 1 Dec 2018

Cited by 15 | Viewed by 7522

Abstract

The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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17 pages, 4235 KiB

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Applying the Plan-Do-Check-Act (PDCA) Cycle to Reduce the Defects in the Manufacturing Industry. A Case Study

by Arturo Realyvásquez-Vargas, Karina Cecilia Arredondo-Soto, Teresa Carrillo-Gutiérrez and Gustavo Ravelo

Appl. Sci. 2018, 8(11), 2181; https://doi.org/10.3390/app8112181 - 7 Nov 2018

Cited by 92 | Viewed by 36249

Abstract

Defects are considered as one of the wastes in manufacturing systems that negatively affect the delivery times, cost and quality of products leading to manufacturing companies facing a critical situation with the customers and to not comply with the IPC-A-610E standard for the acceptability of electronic components. This is the case is a manufacturing company located in Tijuana, Mexico. Due to an increasing demand on the products manufactured by this company, several defects have been detected in the welding process of electronic boards, as well as in the components named Thru-Holes. It is for this reason that this paper presents a lean manufacturing application case study. The objective of this research is to reduce at least 20% the defects that are generated during the welding process. In addition, it is intended to increase 20% the capacity of three double production lines where electronic boards are processed. As method, the Plan-Do-Check-Act (PDCA) cycle, is applied. The Pareto charts and the flowchart are used as support tools. As results, defects decreased 65%, 79%, and 77% in three analyzed product models. As conclusion, the PDCA cycle, the Pareto charts, and the flowchart are excellent quality tools that help to decrease the number of defective components. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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16 pages, 6638 KiB

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Ore Composition’s Impact on Smelting Profitability: An Optimum Pricing Index Model for Long-Term Nickel Ore Feedstock Purchasing Agreements

by Ho-Hyun Jeong, Eul-Bum Lee and Douglas Alleman

Appl. Sci. 2018, 8(11), 2100; https://doi.org/10.3390/app8112100 - 1 Nov 2018

Cited by 4 | Viewed by 4776

Abstract

Global Nickel (Ni) smelters’ have been experiencing profit losses for nearly a decade due to the 2008 recession still impacting the industry, oversupply, and fluctuating ore quality. This paper proposes to aid the Ni smelters with the lattermost issue, presenting an optimum pricing index model for purchasing raw Ni ore materials. The model is developed using data from a major Ni smelter in operation in Korea, including parameters such as revenues, investment expenses, and ore purchasing costs and the impact Ni and Iron (Fe) content variation has on them. In contrast, existing published Ni price forecasting models are based on external variables (e.g., GDP) and are intended for profit forecasting versus contractual agreements. In executing a Monte-Carlo simulation of 1000 possible life-cycle costs analysis with and without the use of the proposed model, the model increased the likelihood of the smelter earning a profit by approximately 5% with an average approximate increase of $50 million. As such, the proposed index model provides new Ni and nonferrous metals smelters material quality fluctuation risk mitigation. Although this model is presented for the Ni smelting process, the findings could theoretically be applied to any long-term procurement activity with variable quality and market conditions. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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15 pages, 1190 KiB

Open AccessArticle

Development of a Preventive Maintenance Strategy for an Automatic Production Line Based on Group Maintenance Method

by Guofa Li, Yi Li, Xinge Zhang, Chao Hou, Jialong He, Binbin Xu and Jinghao Chen

Appl. Sci. 2018, 8(10), 1781; https://doi.org/10.3390/app8101781 - 30 Sep 2018

Cited by 14 | Viewed by 4560

Abstract

The high maintenance costs and low reliability of automatic production line are attributed to the complexity of maintenance management. In the present study, a preventive maintenance strategy for the automatic production line was developed based on the group maintenance method. The criticality of machines in the production line was evaluated, and then the machines were classified into three groups: the most critical machines, the secondary critical machines and the general machines. The general machines were performed on the breakdown maintenance. The preventive maintenance model of the most critical machines was established with the shortest shutdown time as decision objective on basis of the Delay-time theory. The maintenance model of the secondary critical machine was established based on the considering of reliability-maintenance cost. A case study on an automotive part automatic production line was carried out to verify the proposed preventive maintenance strategy based on the production line data, and the maintenance periods of the most and secondary critical machines were gained; meanwhile, the machines all satisfied the reliability requirements during the maintenance periods. Full article

(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing Production Systems)

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