Feb 3

Failure Modes and Effects Analysis (FMEA)

  • Definition: Structured method for identifying possible failures in a product or process to assess their effects and find improvement opportunities.
    • Failure Modes: Asks "What can go wrong?"
    • Effects Analysis: Asks "What would be the consequences of failure for the customer?"

Example: FMEA for an ATM

  • Function: Dispense cash
  • Failure Modes:
    • Out of cash
    • Effects of Failure: Decreases customer satisfaction
    • Causes: Low cash alert not functioning
    • Recommended Action: Increase bill holder size
    • Responsibility: Designer
    • Jam issues
    • Effects of Failure: Bills stuck, decrease in profitability
    • Causes: Incorrect cash grouping
    • Recommended Action: Riffle cash

Steps in the FMEA Process

  1. Identify Functions: Define what the product/process does.
  2. Identify Failure Modes: Determine ways failures could occur for each function.
  3. Identify Effects of Failure: Assess the consequences of failure from the customer’s perspective.
  4. Determine Severity Rating (S): Scale from 1 (insignificant) to 10 (catastrophic).
  5. Identify Causes of Failure: Find reasons behind the failure modes.
  6. Determine Occurrence Rating (O): Scale from 1 (extremely unlikely) to 10 (inevitable).
  7. Identify Control Mechanisms: Existing processes to detect failures.
  8. Determine Detection Rating (D): Likelihood of detecting the failure before customer impact.
  9. Calculate Risk Priority Number (RPN): RPN = Severity (S) × Occurrence (O) × Detection (D).
  10. List RPNs: In descending order to prioritize issues.
  11. Identify Recommended Actions: Steps to reduce failure severity or improve detection.
  12. Update Ratings: As actions are implemented, update S, O, D, and calculate new RPNs.

Demand Forecasting

  • Definition: Estimate of expected demand for a specified time in the future, crucial for operations management.

Uses of Demand Forecasts

  1. Help design the system: Long-term planning.
  2. Plan medium-term system usage.
  3. Schedule short-term system usage.

Examples of Forecasts in Various Fields

  • Accounting: Estimates for new products, profits, cash management.
  • Finance: Equipment needs, funding requirements.
  • Human Resources: Hiring and layoff planning.
  • Marketing: Pricing, promotion, and strategy planning.
  • Operations: Scheduling, capacity, inventory planning.

Common Features of Forecasts

  • Based on the assumption that past causal systems will continue into the future.
  • Rarely perfect due to randomness.
  • More accurate when predicting groups rather than individuals.
  • Forecast accuracy diminishes as the time horizon extends.

Elements of a Good Forecast

  • Timeliness: Must be provided promptly.
  • Accuracy: Must quantify the degree of accuracy.
  • Reliability of method/software used.
  • Expressed in understandable units.
  • Must be documented in writing.
  • Simple to comprehend and use.
  • Cost-effective.

Steps in the Forecasting Process

  1. Determine the forecasting purpose.
  2. Establish a forecasting horizon.
  3. Gather and analyze historical data.
  4. Choose a forecasting technique.
  5. Prepare the forecast.
  6. Monitor the forecast.

Approaches to Forecasting

  • Judgmental Approaches: Based on subjective inputs, considering human factors.
  • Quantitative Approaches: Utilize numerical data to predict future values.

Judgmental Methods

  • Executive Opinions: Combining insights from high-level executives.
  • Sales Force Opinions: Insights based on customer interaction.
  • Consumer Surveys: Gathering feedback directly from consumers.
  • Historical Analogies: Using past demand data from similar products.
  • Expert Opinions: Techniques like the Delphi method for consensus.

Time Series Models

  • Sequence of observations collected at regular intervals.
Patterns in Time Series:
  1. Level: Stability, showing no significant trend.
  2. Trend: Directional movement over time.
  3. Seasonality: Fluctuations related to time periods.
  4. Cycles: Variations over longer durations due to economic factors.
  5. Irregular Variations: Anomalies not reflecting typical behavior.
  6. Random Variations: Noise after accounting for other patterns.

Time Series Techniques

  • Naive Methods: Simple, low-cost but low accuracy.
  • Averaging Methods: Include moving averages and exponential smoothing.
  • Trend Models: Account for linear or non-linear trends.

Naive Method Explained

  • Stable Series: Next point forecast equals the last data point.
  • Seasonal Variations: Forecast equal to the last seasonal value.
  • Trend Data: Forecast derived from the last value adjusted by trend difference.