The document discusses enterprise resource planning (ERP) systems and demand forecasting. It provides an overview of ERP systems, including their integrated nature, major developers like SAP, and implementation challenges. SAP's modules for financials, human resources, operations, and corporate services are described. The document also covers demand management, types of forecasts, and forecasting techniques like time series analysis, regression, moving averages, exponential smoothing, and decomposition. Implementation issues and reasons for adopting ERP systems are also summarized.

1. McGraw-Hill/Irwin Copyright © 2011 The McGraw-Hill Companies, All Rights Reserved
Chapter 14
Enterprise Resource Planning Systems

2. 14-2
Learning Objectives
1. Understand the scope of enterprise
resource planning (ERP) systems.
2. Recognize the client/server structure
of ERP systems.
3. Relate the value of integrated
information in a complex firm.
4. Identify the challenges associated with
implementing ERP systems.

3. 14-3
Enterprise Resource Planning
(ERP)
• Enterprise resource planning (ERP)
system: computer system that links all
the areas of the business
– Manufacturing knows about new orders as
soon as they are entered
– Sales knows the exact status of a
customer order
– Purchasing knows what manufacturing
needs to the minute
– The accounting system is updated as all
the relevant transactions occur
LO 1

4. 14-4
Enterprise Resource Planning
(ERP) Continued
• The potential benefits are huge
– Savings in redundant postings
– Redundant jobs can be eliminated
– Other jobs can be done faster
– Creates new ways to do business
• Benefits are not without cost
– ERP systems are complex and expensive
– May require major changes in processes
LO 1

5. 14-5
Major Developers of Enterprise
Resource Planning Software
LO 1

6. 14-6
SAP
• SAP AG is the world leader in providing ERP
software
– Flagship product is R/3
• Software designed to operate in a three-tier
client/server configuration
• Cost is a high-speed network of database
servers
• Application consists of software modules
• Applications are fully integrated and share
data
• Firms wishing to use SAP may need to
change their practices to those implemented
by SAP
LO 1

7. 14-7
Three-Tier Client/Server
Configuration
LO 2

8. 14-8
SAP Application Modules
• Software is built around a set of
application modules:
– Financials
– Human capital management
– Operations
– Corporate services
– Duet–Microsoft Office integration
• Can be used either alone or in
combination
LO 2

9. 14-9
Financials
• Financial application provides functionality to
run the financial accounts for the company
• The application is divided into three areas:
1. Financial and management accounting
module: includes general ledger, accounts
payable, accounts receivable, capital investments
and closing the books
2. Corporate governance: internal control and
audit functions needed to adhere to corporate
governance standards, documentation of internal
controls, and audits that comply with current
requirements
3. Financial supply chain management: handles
the money flow related to supply chain activities
LO 3

10. 14-10
Human Capital Management
• This application contain the full set of
capabilities needed to manage, schedule,
pay, and hire the people who make a
company run
• It includes:
– Payroll
– Benefits administration
– Application data administration
– Personnel development planning
– Workforce planning
– Schedule and shift planning
– Time management
– Travel expense accounting
LO 3

11. 14-11
Human Capital Management Continued
• The “talent management” functions are
designed to help align employee goals with
corporate goals by maximizing the impact of
training
• “Workforce deployment” applications are to
aid in deploying the right people with the right
skills to positions in the firm
• Managing project teams, monitoring the
progress of projects, and tracking time are
supported
LO 3

12. 14-12
Operations
• Materials management covers all tasks within
the supply chain
– It also includes inventory and warehouse
management
• Plant maintenance supports the activities
associated with planning and performing
repairs and preventive maintenance
• Quality management plans and implements
procedures for inspection and quality
assurance and is built around the ISO 9001
specifications.
LO 3

13. 14-13
Operations Continued
• Production planning and control
supports both discrete and process
manufacturing processes
• Sales order management, configuration
management, distribution export
control, and shipping and transportation
management are handled as well
LO 3

14. 14-14
Corporate Services
• The corporate services applications are
designed to manage both centralized and
decentralized services
• This includes managing the firm’s real estate
portfolio, including:
– Property acquisition and disposal
– Property management
– Building operations and maintenance
– Investment reporting
• Another major aspect of the corporate
services is travel management
LO 3

15. 14-15
mySAP.COM and SAP NetWeaver–
Integrated E-Business Applications
• All SAP functions can be implemented using
an Internet interface
– mySAP versions work over the Internet
– Can run SAP without deploying any special
software
• Cloud computing: delivering hosted
services over the Internet
• Three distinct characteristics:
1. Sold on demand
2. It is elastic
3. The service is fully managed by the provider
LO 3

16. 14-16
NetWeaver
• NetWeaver allows users to build their
own application on top of SAP
applications
• Users and third-party vendors can offer
an unlimited variety of specialized
applications
LO 3

17. 14-17
Implementing ERP Systems
• SAP has strong competition
– Oracle
– i2 Technologies
– PeopleSoft
• Implementation of ERP is costly
– Cost of the software typically one-third or
less of the total cost
• ERP implementation does not always
work out
LO 4

18. 14-18
Major Problems with ERP
Implementation
1. ERP technology could not support
their businesses
2. Their organizations could not make
changes needed to benefit from ERP
3. ERP implementation might actually
damage their businesses
LO 4

19. 14-19
Reasons to Implement ERP
• Desire to standardize and improve
processes
• To improve systems integration
• Improve information quality
LO 4

20. McGraw-Hill/Irwin Copyright © 2011 The McGraw-Hill Companies, All Rights Reserved
Chapter 15
Demand Management and Forecasting

21. 14-21
Learning Objectives
1. Understand the role of forecasting as a basis for
supply chain planning.
2. Compare the differences between independent and
dependent demand.
3. Identify the basic components of independent
demand: average, trend, seasonal, and random
variation.
4. Describe the common qualitative forecasting
techniques such as the Delphi method and
Collaborative Forecasting.
5. Show how to make a time series forecast using
regression, moving averages, and exponential
smoothing.
6. Use decomposition to forecast when trend and
seasonality is present.

22. 14-22
Demand Management
• Strategic forecasts: forecasts used to help
set the strategy of how demand will be met
• Tactical forecasts: forecasted needed for
how a firm operates processes on a day-to-
day basis
• The purpose of demand management is to
coordinate and control all sources of demand
• Two basic sources of demand
– Dependent demand: the demand for a product or
service caused by the demand for other products or
services
– Independent demand: the demand for a product
or service that cannot be derived directly from that
of other products
LO 2

23. 14-23
Demand Management Continued
• Not much a firm can do about
dependent demand
– It is demand that must be met
• There is a lot a firm can do about
independent demand
1. Take an active role to influence demand
2. Take a passive role and respond to
demand
LO 1

24. 14-24
Types of Forecasts
• Four basic types
1. Qualitative
2. Time series analysis
3. Causal relationships
4. Simulation
• Time series analysis is based on the
idea that data relating to past demand
can be used to predict future demand
– Primary focus of this chapter
LO 1

25. 14-25
Components of Demand
1. Average demand for a period of time
2. Trend
3. Seasonal element
4. Cyclical elements
5. Random variation
6. Autocorrelation
LO 3

26. 14-26
Common Types of Trends
LO 3

27. 14-27
Time Series Analysis
• Short term: forecast under three
months
– Tactical decisions
• Medium term: three months to two
years
– Capturing seasonal effects
• Long term: forecast longer than two
years
– Detecting general trends
– Identifying major turning points
LO 5

28. 14-28
A Guide to Selecting an
Appropriate Forecasting Method
LO 5

29. 14-29
Pick Forecasting Model Based On
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified personnel
LO 5

30. 14-30
Linear Regression Analysis
• Regression: functional relationship between
two or more correlated variables
• It is used to predict one variable given the
other
• Y = a + bX
– Y is the value of the dependent variable
– a is the Y intercept
– b is the slope
– X is the independent variable
• Assumes data falls in a straight line
LO 5

31. 14-31
Example 15.1: The Data and Least
Squares Regression Line
LO 5

32. 14-32
Example 15.1: Equations and
Calculating Totals
LO 5

33. 14-33
Example 15.1: Calculating the
Forecast
 
 
 
  2
.
195
,
6
17
6
.
359
6
.
441
6
.
835
,
5
16
6
.
359
6
.
441
0
.
476
,
5
15
6
.
359
6
.
441
4
.
116
,
5
14
6
.
359
6
.
441
16
15
14
13














Y
Y
Y
Y
bx
a
Y
LO 5

34. 14-34
Decomposition of a Time Series
• Time series: chronologically ordered
data that may contain one or more
components of demand
• Decomposition: identifying and
separating the time series data into
these components
• Seasonal variation
– Additive: the seasonal amount is constant
– Multiplicative: the seasonal variation is a
percentage of demand
LO 6

35. 14-35
Additive and Multiplicative Seasonal
Variation Superimposed on Changing
Trend
LO 6

36. 14-36
Example 15.3: The Data and Hand
Fitting
x
bx
a
Y
55
170



LO 6

37. 14-37
Example 15.3: Computing
Seasonal Factors and Computing
Forecast
LO 5

38. 14-38
Decomposition Using Least
Squares Regression
1. Determine the seasonal factor
2. Deseasonalize the original data
3. Develop a least squares regression
line for the deseasonalized data
4. Project the regression line through the
period of the forecast
5. Create the final forecast by adjusting
the regression line by the seasonal
factor
LO 6

39. 14-39
Steps 1-3 Deseasonalized Demand
LO 6

40. 14-40
Steps 4 – 5
LO 6

41. 14-41
Simple Moving Average
• Useful when demand is neither growing
nor declining rapidly and does not have
seasonal characteristics
• Moving averages can be centered or
used to predict the following period
• Important to select the best period
– Longer gives more smoothing
– Shorter reacts quicker to trends
LO 5

42. 14-42
Simple Moving Average Formula
ago
periods
n
to
up
on
so
and
ago,
periods
three
ago,
periods
two
s
occurrence
Actual
and
,
period
past
in the
occurrence
Actual
averaged
be
to
periods
of
Number
n
period
coming
for the
Forecast
3
2
1
3
2
1

















n
t
t
t
t
t
n
t
t
t
t
t
A
A
A
A
F
n
A
A
A
A
F

LO 5

43. 14-43
Forecast Demand Based on a
Three- and a Nine-Week Simple
Moving Average
067
,
1
3
000
,
1
400
,
1
800



300
,
1
3
500
,
1
000
,
1
400
,
1



333
,
1
3
500
,
1
500
,
1
000
,
1



LO 5

44. 14-44
Weighted Moving Average
• The moving average formula implies an
equal weight being placed on each
value that is being averaged
• The weighted moving average permits
an unequal weighting on prior time
periods
– All the weights must sum to one
F = w A + w A + w A +...+w A
t 1 t-1 2 t-2 3 t-3 n t-n
LO 5

45. 14-45
Choosing Weights
• Experience and trial-and-error are the
simplest ways
• Generally, the most recent past is the
best indicator
• When data are seasonal, weights
should be established accordingly
LO 5

46. 14-46
Exponential Smoothing
• Most used of all forecasting techniques
• Integral part of all computerized forecasting
programs
• Widely used in retail and service
• Widely accepted because…
1. Exponential models are surprisingly accurate
2. Formulating an exponential model is relatively
easy
3. The user can understand how the model works
4. Little computation is required to use the model
5. Computer storage requirements are small
6. Tests for accuracy are easy to compute
LO 5

47. 14-47
Exponential Smoothing Model
 
constant
smoothing
Alpha
period
e
past t tim
in the
occurance
Actual
A
period
past time
1
in
alue
Forecast v
F
period
t time
coming
for the
lue
Forcast va
F
:
Where
F
1
-
t
1
-
t
t
1
1
1
t






 



 t
t
t F
A
F
LO 5

48. 14-48
Week Demand 0.2
1 820 820.00
2 775 820.00
3 680 811.00
4 655 784.80
5 750 758.84
6 802 757.07
7 798 766.06
8 689 772.45
9 775 755.76
10 759.61
Exponential Smoothing Example
(=0.20)
 
  820
0
2
.
0
820
820
820
2
.
0
820





 
 
811
0
.
9
820
45
2
.
0
820
820
775
2
.
0
820








 
 
8
.
784
2
.
26
811
131
2
.
811
811
680
2
.
811








LO 5

49. 14-49
Trend Effects in Exponential
Smoothing
• An trend in data causes the exponential
forecast to always lag the actual data
• Can be corrected somewhat by adding
in a trend adjustment
• To correct the trend, we need two
smoothing constants
– Smoothing constant alpha ()
– Trend smoothing constant delta (δ)
LO 5

50. 14-50
Exponential Forecasts versus Actual
Demand over Time Showing the Forecast
Lag
LO 5

51. 14-51
Trend Effects Equations
 
 
constant
Smoothing
constant
Smoothing
period
prior
for the
demand
actual
The
A
period
prior
for the
made
trend
including
forecast
The
FIT
t
period
for
trend
including
forecast
The
FIT
t
period
for
trend
smoothed
lly
exponentia
The
T
t
period
for
forecast
smoothed
lly
exponentia
The
F
FIT
1
-
t
1
-
t
t
t
t
1
1
1
1
1
t
























t
t
t
t
t
t
t
t
t
t
FIT
F
T
T
FIT
A
FIT
F
T
F
LO 5

52. 14-52
Forecast Error
• Bias errors: when a consistent mistake
is made
• Random errors: errors that cannot be
explained by the forecast model being
used
• Measures of error
– Mean absolute deviation (MAD)
– Mean absolute percent error (MAPE)
– Tracking signal
LO 5

53. 14-53
The MAD Statistic to Determine
Forecasting Error
• The ideal MAD is zero which would
mean there is no forecasting error
• The larger the MAD, the less the
accurate the resulting model
MAD =
A - F
n
t t
t=1
n

LO 5

54. 14-54
Tracking Signal
• The tracking signal (TS) is a measure that
indicates whether the forecast average is
keeping pace with any genuine upward or
downward changes in demand
• Depending on the number of MAD’s selected,
the TS can be used like a quality control chart
indicating when the model is generating too
much error in its forecasts
TS =
RSFE
MAD
=
Running sum of forecast errors
Mean absolute deviation
LO 5

55. 14-55
Computing the MAD, the RSFE,
and the TS from Forecast and
Actual Data
LO 5

56. 14-56
Causal Relationship Forecasting
• Causal relationship forecasting:
using independent variables other than
time to predict future demand
– The independent variable must be a
leading indicator
• Must find those occurrences that are
really the causes
LO 5

57. 14-57
Qualitative Techniques in
Forecasting
• Qualitative forecasting techniques take
advantage of the knowledge of experts
• Most useful when the product is new or there
is little experience with selling into a new
region
• The following are samples of qualitative
forecasting techniques
– Market research
– Panel consensus
– Historical analogy
– Delphi method
LO 4

58. 14-58
Web-Based Forecasting: (CPFR)
• Collaborative planning, forecasting, and
replenishment (CPFR): a Web-based tool
used to coordinate demand forecasting,
production and purchase planning, and
inventory replenishment between supply
chain trading partners
• Used to integrate the multi-tier or n-Tier
supply chain
• Objective is to exchange selected internal
information to provide for a reliable, longer
term future views of demand
• CPFR uses a cyclic and iterative approach to
derive consensus forecasts
LO 5

59. 14-59
Web-Based Forecasting: Steps in
CPFR
1. Creation of a front-end partnership
agreement
2. Joint business planning
3. Development of demand forecasts
4. Sharing forecasts
5. Inventory replenishment
LO 5