Applied Engineering to Lean Manufacturing Production Systems

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

1. Kumar, M.; Vaishya, R.; Parag. Real-time monitoring system to lean manufacturing. Procedia Manufacturing 2018, 20, 135-140.
2. 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.
3. 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.
4. Kiran, D.R. Chapter 22 - kaizen and continuous improvement. In Total quality management, Kiran, D.R., Ed. Butterworth-Heinemann: 2017; pp 313-332.
5. 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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