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APICS CPIM - Basics of SC Mgmt > BSCM - 2 - Demand Management > Flashcards

BSCM - 2 - Demand Management Flashcards

1
Q

Name three important business processes that relate to DEMAND MANAGEMENT.

A
  • Marketing Management
  • CRM
  • Demand Planning
    • Forecasting
    • Customer Orders
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2
Q

Type of forecast error where there are forecasts errors month to month but they cancel themselves out. Harder to plan around when the demand pattern is erratic.

A

RANDOM VARIATION

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3
Q

Must be forecasted.

A

INDEPENDENT DEMAND

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4
Q

Calculated based on demand for another product.

A

DEPENDENT DEMAND

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5
Q

Name the 5 Sources of Demand

A
  1. Forecast
  2. Customer Orders
  3. Replenishment Orders from DCs
  4. Interplant transfers / Intercompany orders
  5. Other sources of demand (samples, marketing, etc.)
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6
Q

Type of forecast error. Exists when the cumulative variation of actual demand from the cumulative forecast is not zero.

A

BIAS

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7
Q

Actions and Alternatives to Investigate when dealing with forecast bias

A
  • Investigate and take into account the cause of the error (sales promotions, shutdowns, etc.).
  • If necessary, adjust the demand history.
  • Change the monthly average forecast.
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8
Q

Recognition of customer requirements through:

  • forecasts
  • management of orders from:
    • internal customers
    • external customers
A

DEMAND PLANNING

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9
Q

Name the 4 Major Principles of Forecasting

A
  1. Forecasts are rarely 100% accurate over time.
  2. Forecasts should include an estimate of error.
  3. Forecasts are more accurate for product groups and families.
  4. Forecasts are more accurate for nearer periods of time.
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10
Q

Explain the difference between STABLE and DYNAMIC DEMAND.

A

STABLE DEMAND retains the same general shape over time.

DYNAMIC DEMAND is erratic and unpredictable.

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11
Q

Why track the forecast?

A
  • to determine why demand differs from the forecast
    • has demand changed? has a trend developed?
  • to plan around error in the future
  • to improve forecasting methods
  • to adjust safety stock levels if necessary
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12
Q

Explain the major demand patterns.

A
  • TRENDS - increasing, decreasing, or level
  • SEASONAL - predictable pattern
  • RANDOM - no explanation
  • CYCLICAL - wave-like fluctuations over LONG time spans (years)
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13
Q

Name 3 planning levels that are supported by demand planning and explain how forecasting supports them.

A
  1. Business Planning
    • Forecasts sales volume ($); new market and supply chain initiatives (2 to 10 years)
  2. Sales & Operations Planning (S&OP)
    • Forecasts physical units of production at the product family level (1 to 3 years).
  3. Master Scheduling
    • Forecasts physical unit of production at the end item level (3-18 months)
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14
Q

Name the three principles of data collection and preparation?

A
  • Record data in terms needed for the forecast.
  • Record circumstances relating to the data.
  • Record demand separately or different customer groups.
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15
Q

Forecasting technique based on intuition and informed opinion. Tends to be subjective and based on one’s judgment and “gut feel”. Most often used for business planning and forecasting for new productions; also often used for medium-to-long term forecasting.

A

QUALITATIVE FORECASTING TECHNIQUES

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16
Q

Forecasting technique based on several assumptions:
-The past helps you understand the future.
-Time series are available.
-The past pattern of demand predicts the future pattern of demand.
EXAMPLES: moving averages, exponential smoothing

A

QUANTITATIVE FORECASTING TECHNIQUE: INTRINSIC

17
Q

Forecasting technique based on correlation and causality. It relies on external indicators and is most useful in forecasting total company demand or demand for families of productions.
It has two types of leading indicators:
-Economic
-Demographic

A

QUANTITATIVE FORECASTING TECHNIQUE: EXTRINSIC

18
Q

(x-1)+(x-2)+(x-3)/3

A

3 MONTH MOVING AVERAGE FORMULA

19
Q

PERFORMANCE OBJECTIVES - Order Winning and Order Qualifying Characteristics

A
  • Quality
  • Dependability
  • Cost
  • Speed
  • Flexibility
20
Q

CRM plays a major role in operations efficiency and customer service through:

  • fast and accurate order entry and tracking
  • meeting promised delivery dates and quantities
  • handling customer inquiries and service complaints, returns, and repairs
  • accurate and timely shipping documentation, invoicing, and recording of sales history.
A

ORDER MANAGEMENT

21
Q

Competitive characteristics that a firm’s products & services MUST exhibit in order for the firm to be a viable competitor in the marketplace.

A

ORDER QUALIFIERS

22
Q

Competitive characteristics that cause customers to prefer a firm’s products and services over those of its competitors.

A

ORDER WINNERS

23
Q

MOVING AVERAGES: LESSONS LEARNED

A
  • The moving average will make the development of a rising or falling trend lag.
  • The farther back the moving average forecast reaches for data, the greater the lag.
  • The three-month moving-average forecast may have overreacted if the demand surge had abated.
  • The moving-average forecast works best when demand is stable with random variation–IT WILL FILTER OUT RANDOM VARIATION.
24
Q
  • Deal with demand uncertainty through process improvements.
  • Decrease reliance on long-term forecasts and increase ability to react quickly to demand.
  • Collaborate with customers and suppliers, especially in sharing demand information.
  • Increase manufacturing flexibility internally and operations integration externally with customer and suppliers.
A

SUPPLY CHAIN MANAGEMENT IMPLICATIONS

Forecast Measurement

25
Q
  • Identifying changes and trends in demand.
  • Identifying and adjusting for forecast error that results from random events.
  • Adjusting the period forecast as close to the true forecast average demand as possible to eliminate or minimize bias in future.
  • Making decisions on safety stock and service levels based on the degree of random variation, or forecast error.

****TO GUARANTEE A HIGH SERVICE LEVEL, YOU NEED SAFETY AND FAITH IN THE FORECAST!!!!

A

USES OF FORECAST MANAGEMENT

26
Q

SEASONAL FORECAST FORMULA

A

Expected Quarter Demand = Seasonal Index x Deseasonalized Forecast Deman

27
Q

Describe the 3 Steps of the Seasonal Forecast Process.

A

Step 1: Create a seasonal index of demand for each period (quarter?) to account for seasonality in historical demand.

Step 2: Develop deseasonlized demand by developing the total forecast for a year and divide it by the number of periods. This takes out seasonality and results in average demand across all seasons.

Step 3: Develop a seasonal forecast for each quarter by multiplying the seasonalized demand by each quarter’s seasonal index.

28
Q

What is the formula for the SEASONAL INDEX?

A

AVERAGE DEMAND FOR ALL PERIODS

29
Q

Explain the process to develop the DEASONALIZED FORECAST.

A
  • make the forecast for the next year.
  • Deseasonalize the forecast (distribute it evenly across all periods).
DEASONALIZED DEMAND = ANNUAL FORECAST
                                                -------------------------
                                                # OF PERIODS
30
Q

Explain the logic behind an exponential smoothing forecast.

A
  • Take the old forecast and the actual demand for the latest or most current period.
  • Assign a weighting factor or smoothing constant (alpha) to the latest period’s demand vs. old forecast.
  • Calculate the weighted average of the old forecast and the latest demand.
31
Q

Explain the calculations involved with EXPONENTIAL SMOOTHING.

A

NEW FORECAST = (alpha)(latest demand) + (1-alpha)(old forecast)
where alpha = smoothing constant.

  • A low alpha gives more weight to the old forecast. ONLY APPROPRIATE WHEN DEMAND IS STABLE.
  • If the forecast is lagging, adjust alpha up.
32
Q

What is the formula for MEAN ABSOLUTE DEVIATION (MAD)?

A

MAD = SIGMA |A-F|
—————
n

MAD = SIGMA absolute errors
————————-
number of periods

-INDICATES THE RELATIVE COST OF DIFFERENT LEVELS OF CUSTOMER SERVICE.

33
Q
  • center= central tendency or average
  • forecast errors (not shown) are randomly dispersed on both sides of the central tendency.
  • Statistically, in a normal distribution:
    • 60% of the forecast error will fall within +/- 1 MAD
    • 90% of the forecast error will fall within +/- 2 MAD
    • 98% of the forecast error will fall within +/- 3 MAD
  • **A NARROW CURVE INDICATES HIGH FORECAST ACCURACY.
A

MAD NORMAL DISTRIBUTION CURVE

APICS CPIM - Basics of SC Mgmt (17 decks)

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