Module 8

• All production of new or existing products face various risks hence, careful planning is very necessary
• Steps in planning the operation of a production system

  1. Determining an accurate forecast of the demand for items to be produced

  2. Using the forecast as a basis to specify the control policies for the inventory system

  3. Loading the machines

  4. Determining the machinery and materials handling requirements

  5. Determining the work-force level during the production period
• Uncertainty


  Existence of more than one possibility
• Risk


  State of uncertainty or exposure to the consequences of uncertainty from nature, economic system, & human nature where some of the possibilities involve a loss, catastrophe, or other undesirable outcome
• Risk is a concept denoting potential negative impact to any characteristic of value that may arise from a future event
• General Risks


  • Risks associated with product failure
• Specific Risks


  • Physical Property
  • Administration
  • Labor force
  • Materials
  • Technology
  • Market
• Challenges to Management include anticipating and minimizing risks
• Challenges to Management include determining if the product is well conceived and if there is demand for the products
• Steps in determining demand

  1. Conduct a preliminary survey to determine potential demand

  2. Do intensive market study which is the basis for production plans
• Challenges to Management include preventing reduction of efficiency, costly damage, draining of profits, & business failure
• Different methods can be used in forecasting the future
• Forecasting


  Process of predicting future trends, events, or conditions from known facts. It is very important in planning and preparing for expected changes in business and is an underlying basis of all business decisions
• Types of Forecasting based on Time Horizon

  • Short-range Forecast (up to 1 year, usually less than 3 months)
  • Medium-range Forecast (3 months to 3 years)
  • Long-range Forecast (3+ years)
• Types of Forecasting according to Use, Function, or Application

  • Economical Forecasts
  • Resource Forecasts
  • Technological Forecasts
  • Demand/Supply/Revenue (D/S/R) Forecasts
• Steps in Forecasting


  1. Determine the use of the forecast

  2. Select the items to be forecasted

  3. Determine the time horizon of the forecast

  4. Select the forecasting model(s)

  5. Gather the data

  6. Make the forecast

  7. Validate and implement results
• Forecasts are always wrong
• Aggregated forecasts and shorter horizon forecasts are more accurate
• There is a very nil chance of achieving a set of perfect forecasts
• Assumption is that there is some underlying stability in the system
• Both product family and aggregated product forecasts are more accurate than individual product forecasts
• Different forecasting techniques and approaches should be used when trying to understand the behavior of a product in the market, depending on the part of the Product Life Cycle being considered
• Forecasting Approaches


  • Qualitative Methods
  • Quantitative Methods
  • Simulation
  • Focus Forecasting
  • Web Based Forecasting
  • Collaborative Planning, Forecasting, and Replenishment (CPFR)
• Qualitative Forecasting Methods


  Used when the situation is vague & little data exists (i.e. new products, or new technologies). It is subjective and judgmental; it involves intuition, experience, estimates, and opinions
• Qualitative Forecasting Methods


  • Historical Analogy
  • Sales Force Composite (Grass Roots)
  • Market Research
  • Panel Consensus
  • Jury of Executive Opinion
  • Delphi Method
• Quantitative Forecasting Methods


  Used when the situation is 'stable' & historical data exists (i.e. existing products, current technology). It involves mathematical techniques that rely on historical data (time series) and/or causal variables (causal/associative)
• Quantitative Forecasting Methods


  • Time-Series Models
  • Associative Models
• Time Series Components


  • Trend Component
  • Seasonal Component
  • Cyclical Component
  • Random Component
• Simulation - Dynamic model, usually computer-based, that allows the forecaster to make assumptions about the internal variables & external environment
• Focus Forecasting - Uses several rules that seem logical and easy to understand to project past data into the future.
• Web Based Forecasting -Applicable to any industry but are more used on food, apparel, and general merchandise industries.
• Collaborative Planning, Forecasting, and Replenishment (CPFR) - This is

  used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners.
• Historical Analogy - Specific to new products; ties what is being forecast to similar items. Important in planning new products where a forecast may be derived by using the history of similar products
• Sales Force Composite (Grass Roots) - Estimates from individual salespersons are reviewed for reasonableness, then aggregated. Combination of predictions or opinions are drawn from experienced sales personnel and used to forecast sales for a designated time period. Each salesperson projects his or her sales, reviewed & combined at district & national levels.
• Market Research - Asks the customer using surveys; seeks input from customers or potential customers through use of surveys, interviews, etc. regarding the purchasing plan and compares it to the hypothesis about the market.
• Panel Consensus - Free open exchange at meetings; participants may be executives, salespeople, or customers; assumes that discussion by a group produces better forecasts than any one individual.
• Jury of Executive Opinion - Pool opinions of high-level executives, sometimes augment by statistical models; involves small groups of high-level managers; group estimates demand by working together; combines managerial experience with statistical models; relatively quick.
• Delphi Method. Panel of experts, queried iteratively; Developed by Olaf Helmer and Norman Dalkey of the Rand Corporation, and Nicholas Rescher in the 1950’s to assess the impact of an atomic bomb attack on the US.
• Time-Series Models - Analysis based on the idea that the history of occurrences over time can be used to predict the future.
• Associative Models - Tries to understand the system underlying and surrounding the item being forecasted