Productivity improvement in furniture industry using lean tools and process simulation

The objective of the study is to improve productivity of a furniture manufacturing company. Demand forecasts play a crucial role in productivity, improvement and production planning analysing the problems associated with it and there by tackling over production and shortage. Demand forecasting was analysed using three (simple moving average method, weighted moving average method, seasonal regression method) different methods in which seasonal regression was found to be more accurate for the current scenario. This method reduces the deviation of forecast by 17.37% which will result in better visibility thus by improvement in productivity. Preliminary survey showed that there were some processes that lead to overall increase in production lead time and industry is not keen in operating the second shift particularly in painting department, which leads to under-utilisation of resources. This research work opted for an exploratory study using the time study analysis and value stream mapping (VSM) to identify bottleneck processes. Brainstorming session was conducted to plot cause and effect and there by priorities the causes to tackle. Simulation analysis was performed to understand the utilisation of man-machine using the current and alternative method. The proposed methods improve the operator utilisation to 54% from 29% and output by 50%.