This paper proposes a new Lean Six Sigma (LSS) methodology to improve process for clothing small- and medium-sized enterprise SME. The methodology is based on combination of two approaches which are the PDCA (Plan, Do, Check, and Act) and the DMAIC (Define, Measure, Analyze, Improve, and Control). The combination technique consists in applying the PDCA to continuously improve and control every DMAIC steps. The DMAIC approach has included Lean Six Sigma tools and techniques, as well as the success factors obtained from a survey, to improve its efficiency. The proposed approach is applied to improve the performances indicators such as Z sigma, Cp, cycle time, and lead time for the case of clothing SME in Tunisia. As an example, the Z-sigma has increased from the sigma level was improved from 1.45 to 3.85. The process capability Cp from 0.5 to 1.3 and the lead time was decreased from 39.47 days to 30.23 days. Finally, the study is concluded by sorting out the effects of the type of produced articles and the presence or not of the quality certification on the application of the proposed approach. The effectives from using PDCADMAIC technique are better when it’s applied with certified company, than non-certified one.

Biosynthesis of metal-oxide nanoparticles using plant extracts has been attracting increasing interest. In this study, we focused on the green synthesis of zinc oxide (ZnO) nanomaterials using zinc acetate as a precursor and mulberry fruit extract as a green reducing agent and determined the antioxidant activity. Powder X-ray diffraction and UV-Vis and Fourier Transform Infra-Red (FT-IR) spectroscopy were used for structure elucidation and to determine the crystallinity of the synthesized product. The morphology of samples was determined using Scanning Electron Microscopy (SEM). Our results indicated the successful synthesis of ZnO nanoparticles. SEM findings revealed the nanoparticles to be spherical; they were found to agglomerate and showed a narrow space between particles, which could be indicative of improved activity. The antioxidant activity of ZnO nanoparticles was determined using a 2,2-Diphenyl-1-Picryl-Hydrazyl (DPPH) free-radical scavenging assay taking into account time and concentration. Our results indicated that ZnO nanoparticles with mulberry fruit extract that were synthesized using green chemistry could effectively scavenge the free DPPH radicals, thereby confirming their superior antioxidant activity.

This study was carried out in a clothing company to determine the parameters that influence the decline in production. The results showed that the parameter “Machine failures” had a 34.6% effect on production, and “lack of versatility” 19.7%, while the parameter “heavy launch” had an effect of 17.1%, “lack of supplies” 9%, touch-ups 3%, and “lack of cut parts” 2%. Indeed, the application of lean and six sigma tools and especially the D.M.A.I.C and TPM methods are two tools that improve efficiency and productivity in a production workshop. Thus, a grid was developed to monitor and maintain the effectiveness of improvement actions and for the continuity of the spirit of improvement within the production department without forgetting that teamwork makes it possible to improve the result.

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To improve quality, production, and service delivery, clothing industries look toward continuous improvement approaches such as lean manufacturing, Six Sigma, and Lean Six Sigma (LSS). Simulation is one of the effective methods which aim to examine different solution scenarios. This study explores how LSS and simulation can be integrated based on the Sim-Lean approach, using a process improvement effort in clothing small–medium enterprises (SMEs). A structured framework integrating these research methodologies is developed, which might benefit a variety of future clothing process improvement efforts, and could inform quality improvement efforts in other industries. The aim is to allow a successful implementation of the approach in the clothing industry to improve the lead time, the daily output, the average staying times (min) of jobs waiting in queues, and the resource utilization.