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In this chapter, the following content will be discussed: What is forecasting? forecasting time horizons, the strategic importance of forecasting, forecasting approaches, associative forecasting methods: regression and correlation analysis, monitoring and controlling forecasts, focus forecasting, forecasting in the service sector.
LECTURE LSM733-PRODUCTION OPERATIONS MANAGEMENT By: OSMAN BIN SAIF Summary of last Session • Case Study– Hard Rock Cafe • Projects Why? • Characteristics and Activities • Work Break down structure • Project Scheduling techniques • Cost time trade offs • Project Control reports Agenda for this Session • What Is Forecasting? • Forecasting Time Horizons • • • The Strategic Importance of Forecasting Forecasting Approaches – Qualitative Methods – Quantitative Methods Associative Forecasting Methods: Regression and Correlation Analysis What is Forecasting? u u Process of predicting a future event Underlying basis of all business decisions u u u u ?? Production Inventory Personnel Facilities The Realities! u u u Forecasts are seldom perfect Most techniques assume an underlying stability in the system Product family and aggregated forecasts are more accurate than individual product forecasts Forecasting Time Horizons u u u Short-range forecast u Up to year, generally less than months u Purchasing, job scheduling, workforce levels, job assignments, production levels Medium-range forecast u months to years u Sales and production planning, budgeting Long-range forecast u 3+ years u New product planning, facility location, research and development Influence of Product Life Cycle Introduction – Growth – Maturity – Decline u u Introduction and growth require longer forecasts than maturity and decline As product passes through life cycle, forecasts are useful in projecting u Staffing levels u Inventory levels u Factory capacity Product Life Cycle Introduction OM Strategy/Issues Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Growth Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focus Enhance distribution Maturity Standardization Fewer product changes, more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Decline Little product differentiation Cost minimization Overcapacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Figure 2.5 Forecasting Approaches Qualitative Methods u Used when little data exist u u u New products New technology Involves intuition, experience u e.g., forecasting sales on Internet Qualitative Methods Jury of executive opinion (Pool opinions of highlevel experts, sometimes augment by statistical models) Delphi method (Panel of experts, queried iteratively until consensus is reached) Sales force composite (Estimates from individual salespersons are reviewed for reasonableness, then aggregated) Consumer Market Survey (Ask the customer) 10 y = 1.75 + 25x If payroll next year is estimated to be $6 billion, then: Sales = 1.75 + 25(6) Sales = $3,250,000 Sales = 1.75 + 25(payroll) 4.0 – 3.25 Nodel’s sales ^ Associative Forecasting Example 3.0 – 2.0 – 1.0 – | | | | | Area payroll 39 | | Correlation u u u How strong is the linear relationship between the variables? Correlation does not necessarily imply causality! Coefficient of correlation, r, measures degree of association u Values range from -1 to +1 40 Correlation Coefficient nΣ ξψ − Σ ξ Σ ψ r= [ν Σ ξ − (Σ ξ )2][ν Σ ψ − ( Σ ψ ) 2] 41 Correlation Coefficient y y (a) nΣ ξψ − Σ ξ Σ ψ r= [ν Σ ξ 2x − (Σ ξ )2][ν Σ ψ(b)2 − Positive ( Σ ψ ) 2] Perfect positive correlation: r = +1 correlation: 0