Special Issue "Applied Engineering to Lean Manufacturing and Production Systems 2020"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 15 December 2021.

Special Issue Editors

Prof. Dr. Jorge Luis García-Alcaraz
E-Mail Website
Guest Editor
Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juárez (Chihuahua), Ciudad Juárez, Mexico
Interests: supply chain performance; just in time (JIT); Kanban and inventory management; Kaizen and continuous improvement; lean manufacturing performance; multicriteria decision making in manufacturing
Special Issues and Collections in MDPI journals
Prof. Dr. Cuauhtémoc Sánchez Ramírez
E-Mail Website
Guest Editor
Tecnológico Nacional de México/ I.T. Orizaba (Veracruz), Mexico
Interests: supply chain management; supply chain simulation; system logistics and system dynamics modeling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In a productive system are converging a lot of techniques, tools, methodologies and philosophies applied to industrial production, which come from different sciences, such as engineering and management. However, all them are focus on generating products that must satisfy a need in customers and improve the financial, operational and social performance of the company. One of the most complete production philosophies is Lean Manufacturing (LM), since it integrates several tools, which in turn rely on other techniques. Usually, LM is focused on waste reduction (overproduction, waiting time, transportation, excess processing, inventory, movement and defects) in manufactured products [1], that allow to reduce cost and offer a competitive advantage.

There is no consensus regarding how many LM tools exist or are applied to a productive system. However, all of them are focus on waste elimination and resource optimization, where engineering techniques and basic science are applied [2]. 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 statistical data analysis [3]. Similarly, to offer product guarantees, companies perform tests and accelerated life tests to determine a warranty period for their products, which are based on statistics inferences [4].

Likewise, some models are implemented to production process for attributes optimization (maximize or minimize) and they are based on integral and differential calculus, accelerated approach methods, among others.  In addition, these applications are found in inventory management, in deterministic and stochastic operation research where uncertainty and risk are integrated into the estimates, among others. 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 and engineers 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.

[1] R. Henao, W. Sarache, I. Gómez, Lean manufacturing and sustainable performance: Trends and future challenges, Journal of Cleaner Production, 208 (2019) 99-116. https://doi.org/10.1016/j.jclepro.2018.10.116.

[2] V. Munteanu, A. Ştefănigă, Lean Manufacturing in SMEs in Romania, Procedia - Social and Behavioral Sciences, 238 (2018) 492-500. https://doi.org/10.1016/j.sbspro.2018.04.028.

[3] a. Kenneth W. Green, a. R. Anthony Inman, a. Victor E. Sower, a. Pamela J. Zelbst, Impact of JIT, TQM and green supply chain practices on environmental sustainability, Journal of Manufacturing Technology Management, (2019) 26. 10.1108/JMTM-01-2018-0015.

[4] F. Wang, H. Li, A practical non-parametric copula algorithm for system reliability with correlations, Applied Mathematical Modelling, 74 (2019) 641-657. https://doi.org/10.1016/j.apm.2019.05.011.

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

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (6 papers)

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Research

Article
System Dynamics and Lean Approach: Development of a Technological Solution in a Regional Product Packaging Company
Appl. Sci. 2021, 11(17), 7938; https://doi.org/10.3390/app11177938 - 27 Aug 2021
Viewed by 394
Abstract
This study was performed in a regional product marketing company located in Ciudad Obregón, Sonora, México, where a problem was detected in empirical decision-making due to their recent incorporation into the market. Thus, the objective of this study is the shelf-product production link, [...] Read more.
This study was performed in a regional product marketing company located in Ciudad Obregón, Sonora, México, where a problem was detected in empirical decision-making due to their recent incorporation into the market. Thus, the objective of this study is the shelf-product production link, where the interest is in knowing the behavior of the main variables that influence the system. System dynamics methodology follows six steps: (1) Map the process under study with the value stream map (VSM); (2) Create a causal diagram; (3) Elaborate the Forrester diagram and equations; (4) Validate the current model; (5) Simulate scenarios; (6) Create the graphical user interface. The main results were the design of the scenarios starting from a robust system dynamics model, three scenarios, and the graphical interface. For this purpose, Stella Architect Software was used as it has special attributes to create a graphical user interface. Furthermore, all the elements of the VSM were added under the Lean Startup approach. Significantly, the inadequate management of the materials was detected, which is why the recommendation was to separate the packaging of dry and cold products to care for food innocuousness and the cold chain. Likewise, processing time decreased, reducing material transfer, which was detected by applying a future VSM based on the Lean Startup methodology. The technological solution in this study is a contribution based on social sciences and mathematics (nonlinear equations) using dynamics simulation to observe the complexity of system behavior. Full article
(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing and Production Systems 2020)
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Article
PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design
Appl. Sci. 2021, 11(4), 1406; https://doi.org/10.3390/app11041406 - 04 Feb 2021
Viewed by 598
Abstract
Product Design (PD) currently faces challenges in new product development, since the industry is in a rush to introduce new products into the market, with customers demanding products that are faster, cheaper, and free from failure. In addition, global companies are trying to [...] Read more.
Product Design (PD) currently faces challenges in new product development, since the industry is in a rush to introduce new products into the market, with customers demanding products that are faster, cheaper, and free from failure. In addition, global companies are trying to improve their product design risk assessment process to gain advantages over competitors, using proven tools like Failure Mode and Effect Analysis (FMEA) and mixing risk assessment methods. However, with current risks assessment tools and a combination of other methods, there is the opportunity to improve risk analysis. This document aims to reveal a novel integrated method, where FMEA, Pythagorean Fuzzy Sets (PFS), and Dimensional Analysis (DA) are cohesive in one model. The proposed method provides an effective technique to identify risks and remove uncertainty and vagueness of human intervention during risk assessment using the Failure Mode and Effect Analysis method. A real-life problem was carried out to illustrate the proposed method. Finally, the study was substantiated by using a correlation and sensitivity analysis, demonstrating the presented integrated method’s usefulness in decision-making and problem-solving. Full article
(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing and Production Systems 2020)
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Article
Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly
by , and
Appl. Sci. 2021, 11(2), 749; https://doi.org/10.3390/app11020749 - 14 Jan 2021
Cited by 2 | Viewed by 769
Abstract
Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and [...] Read more.
Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors. Full article
(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing and Production Systems 2020)
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Article
Research on Improved OEE Measurement Method Based on the Multiproduct Production System
Appl. Sci. 2021, 11(2), 490; https://doi.org/10.3390/app11020490 - 06 Jan 2021
Viewed by 695
Abstract
The multiproduct production system has been applied extensively in factories worldwide due to the diverse consumption habits of consumers. However, current Overall Equipment Effectiveness (OEE) measurement methods are not suitable for it properly. With the prevailing of multiproduct production system, it is essential [...] Read more.
The multiproduct production system has been applied extensively in factories worldwide due to the diverse consumption habits of consumers. However, current Overall Equipment Effectiveness (OEE) measurement methods are not suitable for it properly. With the prevailing of multiproduct production system, it is essential to measure the effectiveness accurately in this kind of production system. In order to fill this gap, based on analyzing former OEE models, we propose the multiproduct production system effectiveness (MPSE), including the calculating steps and application framework, in this paper using the heuristic method. This MPSE is verified by a case study. The principal results show that the proposed MPSE can significantly enhance overall production effectiveness and improve the measurement of indicators in the multiproduct production system, which enriches the theory of OEE at the theoretical level and proposes a novel way to measure and improve the effectiveness of the multiproduct production system effectively at the practical level. Full article
(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing and Production Systems 2020)
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Article
Linear System Identification and Vibration Control of End-Effector for Industrial Robots
Appl. Sci. 2020, 10(23), 8537; https://doi.org/10.3390/app10238537 - 29 Nov 2020
Cited by 1 | Viewed by 468
Abstract
This paper presents the discrete state space mathematical model of the end-effector in industrial robots and designs the linear-quadratic-Gaussian controller, called LQG controller for short, to solve the low frequency vibration problem. Though simplifying the end-effector as the cantilever beam, this paper uses [...] Read more.
This paper presents the discrete state space mathematical model of the end-effector in industrial robots and designs the linear-quadratic-Gaussian controller, called LQG controller for short, to solve the low frequency vibration problem. Though simplifying the end-effector as the cantilever beam, this paper uses the subspace identification method to determine the output dynamic response data and establishes the state space model. Experimentally comparing the influences of different input excitation signals, Chirp sequences from 0 Hz to 100 Hz are used as the final estimation signal and the excitation signal. The LQG controller is designed and simulated to achieve the low frequency vibration suppression of the structure. The results show that the suppression system can effectively suppress the fundamental natural frequency and lower vibration of end-effector. The vibration suppression percentage is 95%, and the vibration amplitude is successfully reduced from ±20 μm to ±1 μm. The present work provides an effective method to suppress the low frequency vibration of the end-effector for industrial robots. Full article
(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing and Production Systems 2020)
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Article
Dynamic Model and Graphical User Interface: A Solution for the Distribution Process of Regional Products
Appl. Sci. 2020, 10(13), 4481; https://doi.org/10.3390/app10134481 - 28 Jun 2020
Cited by 4 | Viewed by 769
Abstract
Organizations in the agroindustry sector face shorter delivery schedules; therefore, they are seeking ways to conduct more effective and less costly product distribution. Supply chain management efforts have focused on improving the flow of both products and information. Thus, the aim of this [...] Read more.
Organizations in the agroindustry sector face shorter delivery schedules; therefore, they are seeking ways to conduct more effective and less costly product distribution. Supply chain management efforts have focused on improving the flow of both products and information. Thus, the aim of this case study was to build a graphical user interface to enable decision-making based on quantitative information for a food distribution process. The problem to be solved was associated with the development of a technological solution to reduce and control variations in transportation times, delivery costs and capacities in cold and dry food distribution. An eight-step system for a dynamics methodology was used: (1) distribution process analysis, (2) route description, (3) variable and parameter description, (4) causal loop diagram creation, (5) current model simulation, (6) validation, (7) quantitative scenario construction based on key performance indicators, and (8) graphical user interface development. The main findings of this research were that the graphical user interface and simulation showed information that represented on average 56.49% of the total distribution costs regarding fuel and that maintenance and tire wearing costs had less of an impact on total costs, representing 9.21% and 3.66% of the total costs, respectively. Additionally, the technological solution—created for the supply chain in the distribution process against the background of changes in policies—makes it possible to improve decision-making based on different scenarios supported by a graphical interface according to key performance indicators. This solution could be used by different organizations who aim to reduce logistics and transportation costs. The main implications of this research were the available and organized information and the restructuring of the distribution process. Full article
(This article belongs to the Special Issue Applied Engineering to Lean Manufacturing and Production Systems 2020)
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