ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Demand Management
Activities involved in planning for and handling all types of demand of an enterprise.
Sources of Demand:
1. Customers (Domestic and Foreign)
2. Other Plants (same corporation)
3. Replenishment (branch warehouses in other locations)
4. Consigned stocks in customers’ locations
Planning & Handling Activities:
1. Forecasting Demand
2. Order Entry
(2,3, 4 Operational Keys for Competitive Companies)
3. Delivery Promises
4. Input to Master Planning Schedule (MPS = steering wheel for mfg.)
Steps:
Order Entry >> Scheduling >> Order Promise
Demands include all classes of materials:
1 Finished Products
2. WIP
3. Maintenance Parts
1
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Forecast Time Period Classification:
Long Range (2 to 5 or more years)
Intermediate Range (1 to 2 years)
Short Range (3 to 6 months)
Immediate Future (daily or weekly)
Factors that influence demand:
External:
> Business conditions
> Nation’s Economy
> Competitive forces
Internal:
> Advertising
> Promotions / Pricing
> Selling efforts
> Quality Improvements
> On-time deliveries
2
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
A forecast is a set of numbers to work from, not to.
Two problems with forecasting:
1. Making a more accurate forecast
2. Making better use of inexact forecasts
General Characteristics of Forecasts:
1. Forecasts are wrong.
2. Forecasts are most useful with an estimate of error.
3. Forecasts are more accurate for larger groups of items. Figure 4-3 Forecast Error for Items
4. Forecasts are more accurate for shorter periods of time. Figure 4-4 Forecast Error over Horiz
5. Forecasts are not a substitute for calculated demand.
3
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Essential steps in making a Forecast:
1. Define the purpose
2. Prepare the data
3. Select the techniques
4. Making the forecasts (and the estimates of forecast error)
5. Tracking the forecast
Give each user a forecast suitable for his or her needs.
(Avoid interpretation or changing of data not reflective of forecast).
Possible Forecast Issues:
1. Period of time (weekly, monthly, annually)
2. Frequency of review (daily, weekly)
3. Unit of measure ($, pieces, pairs, hours, gallons, dozens)
4. Time bucket (short, medium, long)
Two Types of Forecasting Techniques:
1. Objective (mathematical or statistical in nature)
2. Subjective (Based on human judgment)
4
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Judgmental Forecast Methods
Opinions
Internal or external
Markets Surveys
1. Expensive
2. Reliability of data?
Statistical Forecast Methods
Separate Components:
5
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Intrinsic Nature
1. Persistence (simple, of if no variation is present)
The same value which occurred last period will occur next period.
2. Trajectory >> Least Squares
The trend of the past data is fitted to a mathematical curve by means of least squares
or other techniques. The trends may be short or long range.
Estimating Parameters a & b:
a = (n ∑ xiyi – ∑ xi ∑ yi) / ( (n ∑(xi2)) – (∑xi)2 )
b = y_bar – a x_bar
3. Cyclical (similar to trajectory)
Business cycles or seasonal cycles are analyzed for repetition. The values for
future periods are correlated to values in a previous cycle. After the forecast for the
average is updated, the new forecast is then seasonalized by multiplying it by the
index. Figure 4 – 16.
4. Moving Average (running)
The arithmetic average of past data calculated over any number of periods gives
the estimate for the next period. Updating simply develops a new average each
period by adding the latest actual demand and subtracting the oldest.
F = D1 + D2 + …+ D12 / n
5. Weighted Average (Exponential Smoothing)
A weighted average of all or part of past data gives the estimate.
First order smoothing rearranged New F = old forecast + alpha (sales – old)
Second order smoothing for trends
Figure 4 – 15 (Next page)
6
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Second-order smoothing when trends exist: Employs two parts A and B:
Anew = old forecast + α (sales – old forecast)
Bnew = Bold + α (Anew – Bold )
New Forecast = 2Anew – Bnew
6. Random No way to forecast randomness, use range or max/min values and expected
values. A probability distribution is obtained from past data or assumed by
subjective means.
Extrinsic Nature
1. Multiple Correlation
The characteristics of some other factors may be such that it can be used to
estimate the value under study. These techniques are especially valuable when
no historical data is available. Figure 4-11.
2. Leading Time Series
A time series of an index or some factor may be affected by the same components
which affect the value being studied. A mathematical equation relates the
estimate to past values of leading time series. Figure 4-10.
7
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
8
ISE 312 Chapter 4 (Plossl)
Spring Semester
Demand Management
Four steps in using Forecasts:
1. Select the proper technique
2. Manage the updating carefully
3. Track actual versus forecast sales closely
4. React promptly to correct significant deviations
Forecast only what you must; calculate whatever you can.
Track forecasts regularly and do it often; find and fix the bad ones.
Agree is advance when forecasts will be revised; don’t play games with moving goal lines.
Six Reasons Why Forecasts Fail
1. Wasn’t a team effort.
2. Disillusionment occurs.
3. We’ll do it ourselves attitude.
4. People have different needs.
5. No one’s keeping track.
6. Unnecessary items are involved.
Don’t commit stocked items to any specific location until the last possible moment.
9