Productivity and Quality in Smart Manufacturing Systems


Mulya Adi Kredo Tengtarto, Moses Laksono Singgih  and Nurhadi Siswanto

Extended Abstract

Purpose: The objective of this study was to design a cost optimization model that offers production improvement for SMEs.

Theoretical framework: Several studies related to production system disruption management have been conducted, with the majority focusing on large companies. However, small and medium enterprises (SMEs) have limitations compared to large companies. Repairability is considered for cost optimization.

Design/methodology/approach: This research designed a cost optimization model that offers production improvement with repairability process for SME.

Findings: There is a need for repairability given the disruption caused by defective products in SMEs. There is a clear difference in total profit between the current state without repairability and proposed conditions with repairability. SMEs suffer massive losses in the absence of repairs, assuming they do not consider repairing defective products with a production defect rate of approximately 15%. The current state produces many downgraded products. However, repairability still needs to be improved to increase profits.

Research, Practical & Social implications: The study implied that there is a need to consider repairability for product defects at SMEs, especially those with a 15% product defect rate. The use of the proposed model optimizes profit and is designed to increase production capacity based on product improvements. Repairability was considered in this research, considering that SMEs are more susceptible to disruptions compared to large companies.

Originality/value: The novelty of this paper is adding process repairability to the cost optimization model for SMEs in the textile sector, then considering the product downgrade under the conditions in SMEs.

Keywords: Disruption Management; Small Medium Enterprise; Repair Product


Competition in the industrial world in producing products and services is increasing. Every manufacturing industry makes various changes to achieve the best (Singgih et al., 2021). Competition exists naturally between companies, especially in the SME sector, so that each makes the most of the resources it has and, under normal circumstances, grows by making greater use of the resources it has (Sutrisno, Fachrunnisa, & Widodo, 2022). SMEs are crucial to achieving industrial and economic growth because they foster a certain level of competition that improves the kind and quality of goods and services offered, meets the needs of the local market for a variety of services, and lessens reliance on the external market for these goods and services, both in developed and developing nations (Saud, Neamah, & Sabbar, 2022). According to Gunasekaran et al. (2011), SMEs account for 45 percent of total employment opportunities and 33 percent of gross domestic product (GDP) in developing countries. It is often perceived as an engine that drives innovation, economic growth, employment, and social mobility (Ayyagari, Demirgüç-Kunt, & Maksimovic, 2011). The characteristics of SMEs to obtain maximum profit are quite complex because various problems related to complex optimization require the use of many complicated procedures (Prabowo, Singgih, Karningsih, & Widodo, 2020). However, SMEs have limited resources and capabilities due to the complexity of the company (Kamarudin, Aslan, & Rajiani, 2018).


Disruption management was first used in aviation companies, where flight disruptions frequently result in massive cost losses (Yu & Qi, 2004). The successful application of disruption management led to increased interest in applying disruption management to other fields. Yang et al. (2005) studied the problem of recovering production plans that are disrupted due to accidents such as power failure, market change, machine breakdown, supply shortage, worker no-show, and others. Researchers have studied the disruption management problem faced by single production plants. Lee & Yu (2008) studied the scheduling problem on parallel machines in a disruptive environment by minimizing the total of weighted completion times. Lin & Gong (2006) analyzed the machine damaged product with economic production quantity (EPQ) in a single stage production system. Chiu et al. (2007) used the EPQ model with Poisson distribution of machines to determine the optimal production time. They developed the total inventory cost using EPQ with and without details in a single-stage production system. Schmitt & Snyder (2012) developed an inventory model that considers unreliable suppliers and reliable but expensive suppliers.


 Solutions were made and later compared with the results obtained from the case study, shown in Table 2. In addition, the information in Table 2 was obtained from the company before the equations for the proposed approach were utilized. This is actual data for qi, qk1, qk2, and qj, where qi is the quantity of a good product, qk1 is a defective product that has been repaired and has the same quality as a good product, qk2 is a defective product that has been repaired, however, there has been a decline in its quality, and qj is the rejected product. In October, companies experienced an increase in demand; therefore, their products were higher than those manufactured in September and November. Total profit (TP) was obtained using Equation (10) in Tables 1 and 2. However, in October, it was less because several companies received downgraded products.


 Small and medium enterprises have several differences compared to large companies regarding costs, human resources, government policies, the environment, etc. These differences influence the willingness of SMEs to develop, and several have limited capital to develop. This research contribute to optimize the cost of disruption management in small and medium enterprises. A model was designed to increase production capacity based on product improvements. Repairability was considered in this research, considering that SMEs are more susceptible to disruptions due to certain limitations compared to large companies. Mathematical and conceptual models are developed to assess product repair with a trade-off repair cost and an additional inspection. The case study was a mass-production SME glove company. This research model needs to be adopted by companies that experience product defects, especially those with a 15% product defect rate. The use of the proposed model optimizes profit. This model needs to be implemented in mass-production companies with a similar production pattern. Further research is needed to consider the differences between small and medium-sized industries other than gloves because not all regions and SME industries are glove-centered.


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