JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III PRODUCTION PLANNING AND CONTROL Previous Question Paper (February/March - 2022 - R18)

Production Planning and Control (PPC) functions are essential to ensure the smooth flow of production. The primary functions include:

• Planning: This involves determining the production objectives, selecting the manufacturing processes, and deciding on the sequence of operations. It includes capacity planning and material requirements planning.
• Routing: This determines the exact path or sequence of operations that a product must follow through the various machines and workstations to be completed.
• Scheduling: This function fixes the starting and finishing times for each operation or task, ensuring that production is completed within the stipulated deadlines.
• Dispatching: This is the execution phase where actual production orders are released to the shop floor, providing the necessary instructions, drawings, and materials to operators.
• Expediting/Follow-up: This involves monitoring the progress of work, identifying bottlenecks or delays, and taking corrective actions to ensure production stays on schedule.
• Inspection: This ensures that the produced goods meet the defined quality standards and specifications throughout the manufacturing process.
• Evaluation: This involves analyzing the performance of the production system against the planned targets to identify areas for future improvement.

Forecasting is the process of predicting future demand for products or services based on historical data, market trends, and expert judgment. It serves as the foundation for all production planning activities.

Forecasting techniques are broadly classified into two categories:

• Qualitative Techniques: These are subjective and based on human judgment, experience, and intuition. Examples include the Delphi method (expert consensus), market research surveys, and the jury of executive opinion. These are useful when historical data is scarce or unreliable.
• Quantitative Techniques: These rely on mathematical and statistical models using historical demand data. Key methods include:
  • Time Series Analysis: Uses past data patterns to predict future values, including moving averages, weighted moving averages, and exponential smoothing.
  • Causal Models: These assume a cause-and-effect relationship between variables, such as regression analysis, where demand is predicted based on external factors like advertising spend or economic indicators.