One of the crucial challenges for the Manufacturing industry is to understand the supply and demand gaps. This impacts organizational profitability, resource efficiency, quality of products.

Some of the key rationales for choosing “Supply driven Demand Forecasting in Process Manufacturing industry project” capstone are:

• Helping manufacturers to streamline the production supply chain.
• Maximize the equipment and product manufacturing utilization.
• Improve workforce efficiency. Data Science offers primitive means to predict responses based on key predictors. There are multiple tools and technologies such as ARIMA, linear regression methods, SARIMA, long short-term memory (LSTM), Artificial Neural Network, and recently the Prophet Model. Based on the nature of the dataset and the application of algorithms, some of the methods can be utilized to build the forecasting model. Also, another critical requirement for building a forecasting model is to identify and leverage the subject matter expertise. SMEs can help in data cleansing, maintaining the quality of data, and avoid insignificant data points to influence results. The teamwork of SMEs and Data scientists will ensure the clean flow of data for modeling, consistent application, and steady evolution of the model. Key objectives achieved from this capstone are,
• Identify and learn techniques to build a forecasting model
• Help build an automated system to predict supply and demand in the Manufacturing industry.
• Identify the elements/parameters which can help businesses to predict & forecast demands.
• Optimize Manufacturing process to improve production line efficiency
• Maintaining optimal inventory can help improve sales profitability