Implementation of Autonomation and Value Stream Mapping for Productivity Improvement
摘要
In the present scenario, every industry is trying to achieve higher productivity due to competitiveness and rigorous regulations by governing bodies toward sustainable development. In a nutshell, productivity is defined as the ratio of output to input. It is possible to achieve by improving the correct things and cultivating them into a habitual routine. Productivity can be improved by regular review of the existing practices, evaluating, and adopting better methods such as lean manufacturing. There are multiple ways by which non-value-added expenditures will be involved in typical manufacturing industries which reduces productivity. The lean manufacturing method mainly helps in identifying such wastes, thereby improving productivity. Kanban, 5S, Kaizen, just-in-time production are all some of well-known lean tools in competitive industry work culture. Similarly, there are two more recent techniques value stream mapping an autonomation gaining popularity due to cost effectiveness and maximum throughput. In the present work, authors have presented the practical approach to implement hybrid value stream mapping and autonomation concept in batch-type of production system at ABC Cranes Pvt. Ltd. India. The study is conducted on a production line dedicated for machining differential housing. The work consists of developing a current state map to understand the existing opportunities to improve the productivity and based on findings two solutions are developed. As per SOP of value stream mapping, implementing future state map has reduced the inventory by 19.2% and change over time by 44.4%. And while analyzing future state map it is observed that quality inspection process is consuming a cycle time of 14.3 min. To reduce this non-value-added time, a novel technique of implementing autonomation concept into future state map is adopted. The tangible benefits received are reduction in cycle time by 34.4%, lead time by 25.9% and change over time by 66.7%.