Purpose Technological innovation is one of the strongest driving forces in the survival and growth of any organization, including textile and apparel industries. However, technological innovation inherits a wide array of risks due to the uncertainty involved in it. In-depth research reveals the existence of a significant relationship between innovation failures and the approach used to innovate, that is, the organization’s innovator type. However, quantitative evidence supporting this concern is still lacking. Hence, the purpose of this paper is to bridge the existing gap in the literature on effective management of technological innovation risk factors and the innovator type of textile and apparel industries. Design/methodology/approach The risk factors related to technological innovations are identified under different innovator types. Analytic network process (ANP) has been used to evaluate the contribution of risk factors according to the innovator type of the organization. Data was gathered through the literature review and structured and semi structured interviews with textile and apparel industry experts. The contribution of risk factors was determined through priorities, derived according to the ANP using Super Decision software. Findings Contribution of risk factors takes different values according to innovator type. This provides comprehensive knowledge on developing a risk management strategy according to the innovator type of the organization. Furthermore, this provides insight into the fact that a generalized risk management strategy will not be effective and sensible for all innovator types. Originality/value The findings provide a thorough understanding of developing a customized risk management strategy by determining the “most to least” criticality of risks based on the innovator type of the organization. Furthermore, findings can be used to adopt the most appropriate innovator type based on the organization’s key competencies. Moreover, this guides the organization in making the best use of internal resources during risk management. Furthermore, this provides insight into the risk factors that must be addressed prior to embarking on new innovative approaches.

Purpose – High Work In Progress (WIP) and its fluctuation are found to be common in garment manufacturing lines in Sri Lanka. The purpose of this paper is to demonstrate a lean cellular manufacturing model as a solution. Design/Methodology/Approach – The application of group technology/ lean cellular manufacturing techniques and its benefits are examined. The significance of the WIP and its fluctuation is investigated through the data obtained from several garment manufacturing lines. Root cause analysis on the problem reveals the major factors contributing to the problem. The sewing line is identified in few (more commonly four) clusters and each cluster behaves as a separate manufacturing cell termed as a ‘sub cell’. Findings – The hypothesis testing demonstrates that WIP fluctuation is significant in garment manufacturing lines. Poor line balancing is one of the major reasons for WIP fluctuation. Practical Implication - The proposed sub cell concept leads to reduce the WIP level and its fluctuation significantly and delivers many advantages. The validation of the model is tested by implementing the concept into a garment manufacturing company with 20 production lines. The results are promising with a 12% increase in the production efficiency, equivalent to US$ Million 1.23 annually. Originality/ Value –The existing cellular systems are dedicated to work on parts of few products. In garment manufacturing the lines are temporarily dedicated to manufacture only one product. The existing cellular manufacturing systems do not suit for manufacturing garments. Presently each garment manufacturing line behaves as one entity, where empowerment, team work are difficult to be promoted. The new sub-cell concept changes the organizational culture and makes the production lines more flexible through motivated, cohesive team. The operators are motivated with higher earning through higher productivity and dignity.

This paper presents a mathematical programming model for dynamic cell formation to minimize changeover-related costs (i.e., machine relocation costs and machine setup cost) and inter-cell material handling cost to cope with the volatile production environments in apparel manufacturing industry. The model is formulated through findings of a comprehensive literature review. Developed model is validated based on data collected from three different factories in apparel industry, manufacturing fast fashion products. A program code is developed using Lingo 16.0 software package to generate optimal cells for developed model and to determine the possible cost-saving percentage when the existing layouts used in three factories are replaced by generated optimal cells. The optimal cells generated by developed mathematical model result in significant cost saving when compared with existing product layouts used in production/assembly department of selected factories in apparel industry. The developed model can be considered as effective in minimizing the considered cost terms in dynamic production environment of fast fashion apparel manufacturing industry. Findings of this paper can be used for further researches on minimizing the changeover-related costs in fast fashion apparel production stage.

No abstract

No abstract

Purpose Technological innovation has become a significant part of textile and apparel industries. To become distinguished from competitors, it is essential to harness the potentials of the organizations in achieving higher speeds, faster delivery times, more precise research and development processes followed by modern manufacturing techniques, lower inventory and lower costs. This paper aims to present the risk factors, root causes and their impact related to technological innovation. This uncovers deeper problems inherent to the technological innovation process while providing insight to develop a more robust risk management strategy in marking the survival and growth of the organizations in the textile and apparel trade. Design/methodology/approach Literature review and structured interviews with industry experts were conducted to identify the risk factors and root causes associated with technological innovations related to textile and apparel industries. The impact of root causes to the risk factors was determined through priorities, derived according to analytic network process using Super Decision software. Findings Impact of the root causes on risk factors take different priorities, highlighting the most alarming root causes, which contribute more to the outcome. This provides insight for a more precise decision-making on the order of prioritization of root causes in managing risks involved. Originality/value Findings of this research provide insight on the most influential competencies of any textile and apparel organization for a thriving innovation. Profound knowledge on risk factors, root causes and their contribution to the outcome enhances complex decision-making. This ultimately leads to “credible performances’’ of the technological innovation and optimizing the resources available, which bridges the gap that exists in the current literature.

Typical layout of a garment manufacturing line includes on average about 30 machines arranged in a straight line configuration. Normally one skilled operator works on one machine and semi skilled operators are employed at some machines to help the skilled operator. This arrangement hinders work sharing and team work, as no communication is possible between operators in this long line. Work sharing in manufacturing cells and teamwork concepts are indispensible in lean manufacturing environments. Further, balancing of lines for one-piece-flow or near one-piece-flow is only possible with a large balancing loss or impossible due to large variations between cycle times of operations. The new layout proposed in this research facilitates work sharing and team working concepts and proved dramatic improvement when implemented. An algorithm to balance the cell based on the garment type and the skill matrix of operators too is proposed.

No abstract