Applied Engineering to Lean Manufacturing and Production Systems 2020

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.

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• 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