Designing A Leaf-Based Performance Metric Model for Organisational Effectiveness (PMMOE) with Special Reference to Small and Medium Enterprises (SMEs)
Abstract
Organizations are always on the move to improve their performance to stay in business for a longer period. Measuring performance has been one of the prominent activities in all types of business. Hence, organizations predominantly engage in tracking the performance against goals and also in identifying areas where improvement is needed. It is seen in the literature that systematic performance measurement models (Hudson, Lean, Smart, 2001) were implemented only in large companies. And later those measurements were tried in small and medium enterprises also. During the early ’90s, SMEs mainly implemented financial performance measures used in large companies such as ROI, ROE, ROCE, and their derivatives. However, the operations carried out in large industries and SMEs are not the same. So this forms a major problem for those SMEs, as there is no specific performance measurement model available for them. Therefore, an effort has been made in this study to design a performance metric model specifically for SMEs. The model which, if designed is specific to their nature of operations will help them to monitor the performance of various factors leading to achieving organizational effectiveness and meeting the competition in the global market.
SMEs form the largest employment generator sector in India, which is next to the agriculture sector. It earns a significant share in export revenue of 40% of the total exports of the country. Extensive workforce i.e. about 106 million (40%) of India’s total workforce is involved in these SMEs for production and operational activities. An effective performance measurement model if available would be very useful for this segment to keep track of their activities and move towards organizational effectiveness.
The Pump Industry was selected for the study because of its high importance and benefits to the growth of the country. Indian Pump industry is mounting at a compound annual growth rate of about 10 percent and shares more than 20 percent of the largest net assets of the mechanical industry. In addition, the Indian Pumps domestic market is rising at a healthy annual rate of 16 to 18 percent.
The present study is descriptive and conceptual in nature. The study was conducted during 2015- 2019 in Coimbatore, Tamil Nadu. Coimbatore is the hub of various small and medium manufacturing units. Data was collected from stakeholders (owners, managers, supervisors, employees) of the pump manufacturing SMEs. The study adopts various stages which are interconnected with each other. As per the requirement of the particular stages, appropriate sample size, sampling technique, and methodology for data collection were used. The study adopts both qualitative and quantitative data analysis techniques.
Initially, the researchers concentrated on developing a conceptual process model for designing a performance metric model. Then in the next stages, the research concentrated on testing each stage of the conceptual process model like identifying the critical success factors and key performance indicators with specific to small and medium pump manufacturing units in Coimbatore city. Finally, a performance metric model was designed and presented.
The study identified twenty-five critical success factors for the pump industry out of which, the top ten critical success factors were considered for further stages of the process model. In the next stages, there were forty-three strategic objectives formulated, and corresponding KPIs and performance metrics were also identified. Thus a leaf-based performance metric model (PMMOE) was designed with these forty-three KPIs and metrics. This metric model helps the owners and managers to make relevant decisions as quickly as possible to sustain in this competitive environment. The model also supports sharing updates on organizational performance with stakeholders like employees, customers, bankers, and suppliers. There is quite a lot of scope for trying this model in other small and medium manufacturing clusters and making it more generalized. Also validating the model by testing it with actual data will make the model more applicable.
