Analysis and Forecasting of Industrial Production of Non-Durable Food Items

With the increasing growth in population, it is important to foster food requirements with ever-growing demand. Food items are perishable and can cause severe issues to mankind. In view of this, such food items need to be analyzed and insights from this analysis can be very important for food production industries. This study attempts to analyze and forecast the non-durable food items in the food industry using advanced machine learning techniques in order to provide valuable insights and practical implications for businesses in various sectors. For analysis and forecasting, a larger dataset comprising the samples from the years 1972 to 2020 is utilized which enabled a comprehensive analysis of long-term trends. Various forecasting models, including simple exponential smoothing, double exponential smoothing, triple exponential smoothing, ARIMA, SARIMA, MLP, LSTM, and CNN, were developed and evaluated using established evaluation parameters. Ensemble learning techniques were also employed to enhance forecast accuracy. The results obtained using the ensemble technique are comparable with existing techniques and contribute to the tailored analysis and development of a forecasting framework for non-durable food item industries. This study can help industries in production planning, inventory management, and meeting consumer demand in order to optimize operations and make informed decisions.