Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
DESIGN AND EVALUATION OF A BUSINESS INFORMATION
VISUALIZATION SYSTEM FOR MANUFACTURING
PRODUCTION PLANNING SUPPORT
Ping Zhang
School of Information Studies
Syracuse University
pzhang@mailbox.syr.edu
Peng Si Ow
Consultant
Austin, TX
Andrew B. Whinston
Department of Management Science & Information Systems
The University of Texas at Austin
abw@uts.cc.utexas.edu
Abstract
In most management domains, problem-solving and
decision-making are overwhelming because of the high
volume of complicated data, the multiple complex
relationships among data, the negotiability of the
constraints, the changing environment, and time
pressure. Most existing computer systems, such as
expert systems, decision support systems, and
simulation systems, have built-in functionalities and
cannot reflect the changing environment and
possibilities for negotiation. Although they can generate
reports, they are very limited in providing superior
solutions for complex problems. Human beings are in
control in the process of problem-solving and decisionmaking. A useful decision support system should
incorporate human problem-solving processes, support
human information need during the processes, consider
human cognition and perception characteristics, and
provide insight into generated or collected data.
This paper describes our preliminary results on the
design and evaluation of an information visualization
system for the support of manufacturing production
planning. It introduces the domain problem, information
visualization research methodology from a usercentered perspective, and the design and implementation
of a prototype system based on a proposed visualization
model that emphasizes human-information interaction.
A lab experimental study shows that the visualization
system cooperated with planners’ problem-solving
strategies, provided the relevant information at the
relevant abstract levels that planners need during the
planning processes, and was easy to understand and use.
Compared with a traditional manufacturing production
planning support system, the visualization system
supported planners with more alternative solutions,
more efficient changes in raw planning data, more
confidence about domain tasks, and more satisfaction
with solutions.
(Visualization of high-volume, multi-dimensional,
non-geometric-based managerial data; Humaninformation interaction; Manufacturing production
planning; Decision-making support)
Introduction
Recent advances in technology have enabled the
development of sophisticated graphical user interfaces
for the visualization of large quantities of data [12, 15,
16] that are either gathered from the real world and
analyzed by a computer or produced through computer
simulations. The word "visualization" has helped clarify
what some people for years have called "computer
graphics." It means using computer-generated graphics
to help people understand and clarify visually the
relationships inherent in data [16]. The information
conveyed to a viewer by visualization undergoes a
qualitative change because it brings the eye-brain
system, with its great pattern-recognition capabilities,
into play in a way that is impossible with purely
numeric data [7].
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
Until now, most uses of this technology have
involved the visualization of systems that are naturally
represented as two- or three-dimensional objects.
Examples can be found in many scientific visualization
applications, such as visualizing biological molecules,
medical imaging, or tracking and imaging elementary
particles. However, many of the most exciting
potentialities of visualization technology involve the
visual representations of non-geometric-based data that
do not have physical-based interpretations or geometric
structures that lead directly to computer-supported
representations. This type of data can be easily found in
many business domains. Efforts have been made to
visualize business information: Shneiderman et al. [9,
17] developed TreeMaps to visualize huge amounts of
hierarchical and categorical information such as file
directories, budgets, sales data, and organizational
structure data. Chris Jones [10, 11] used attributed
graphs and graph-grammars for representing
management science models such as decision trees,
linear programming, and critical paths that involve
network-related
relationships.
However,
most
management domains have data with complicated
relationships that are difficult to interpret as hierarchical
or network data. Manufacturing production planning is
such a domain.
Manufacturing production planning involves large
amounts of detailed data and the need to manage a large
number of complicated relationships and flexible
constraints. Solutions provided by expert systems or
other computer systems are not adequate to deal with
such a dynamically changing environment and the
flexibility for negotiations. Visual interactive simulation
for modeling [2, 3, 8], although it “uses a simulation
model where the user can suspend execution of the
model, modify one or more parameters, including the
model structure, and resume model execution,” still
requires the model to capture a limited number of
parameters in advance and cannot reflect the changing
environment and negotiability. Human decision-making
in such a domain becomes overwhelmingly complex.
Even with some support of existing computer systems,
people still have to use their cognitive powers to figure
out the “stories” behind the computer-generated reports,
which could be as long as several hundred pages. An
effective solution to this situation is to shift some of the
user’s cognitive load to the human visual-perception
system: we let people see -- perceive visually -- what
they need to “see” -- perceive cognitively -- so that they
do not have to puzzle it out otherwise. The potential
benefit of visualization for providing planners with
insight into the complexities of non-geometric
information in the manufacturing planning process is
significant. However, it is far from obvious how to
visually represent the interaction of the many factors
that influence the production processes in a
manufacturing plant in a way that will enable
production planners to make better management
decisions.
This paper describes the preliminary results of our
research on the development of a visualization-based
decision support system for manufacturing production
planners. It introduces the domain problem,
visualization research methodology, the design and
implementation of a prototype system based on a
proposed visualization model, and evaluation results of
the prototype system using graduate students in a lab
experimental study. This research is part of our longterm project on discovering laws for human-information
interaction and developing information visualization
methodology and techniques for problem-solving and
decision-making in business domains [22].
Manufacturing Production Planning
Problems
In a collaborative effort with IBM Austin, Texas,
we studied the production planning problem at the IBM
PC Co.’s electronic card assembly and test plant
(ECAT). In production planning, specifically
manufacturing resource planning, a planner’s goal is to
maximize overall revenue from the production, subject
to resource constraints such as tools, manpower, and
component availability. ECAT has more than 200 types
of products and more than 2000 types of components.
Some of the components are shared by multiple
products. There are a total of six independent assembly
lines (also called production pull lines -- PPLs); each of
which has its own production capability but also
contributes to the entire factory. The production
planning is done monthly. The production plan rolls 12
months' horizon with varying levels of detail.
The decision-making environment at ECAT is very
dynamic. There is a severe component shortage
problem. Most of the shortfall components can be
obtained by negotiating with suppliers. The price market
for the end products -- personal computers -- is very
competitive. Sometimes the planner has to make a
decision between keeping market-share by paying high
prices for shortfall components, which may hurt profits,
or keeping high profits, in turn may hurt the marketshare. Also the planner has to decide if some products
will be produced outside (outsourcing) instead of by the
plant. In such a dynamic environment, the planner’s
understanding of the planning problem situation is
crucial.
During the planning period, the planner has many
possible actions to take in order to solve some of the
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
problems or sub-problems. For instance, s/he can move
products to different assembly lines, move products to
different time periods, change an assembly line’s
capacity, change the quantity of products to be made
(demand), change the quantity of components available
(scheduled receipts), and change the distribution of
components over products (production mix). Planners
usually have three kinds of difficulties when doing the
production planning or adjusting an existing plan in
response to changes in the environment: recognizing
potential conflicts, coordinating conflicts, and
evaluating adjustments [14].
There are existing planning tools available in
ECAT. One of them is ESIT, an optimization model
based on IBM's commercial product MPSX. Another
one is Copics: a batch-processing production planning
system based on MRP process that runs on an IBM
mainframe. It provides a texture interface, has a central
database, can generate a few reports, and handles
capacity and component planning separately. Due to the
unfriendly user interface of Copics, Sorcerer [1] has
been developed under the PS-2 environment. It provides
a spreadsheet-like interface for Copics, and allows the
planner to interactively change the data and do what-if
analysis.
The planners feel that the dynamic nature of the
decision-making environment makes it impossible to
use ESIT: the planning constraints and possible actions
cannot be represented easily in the algorithmic system.
Although Sorcerer has a friendly user interface and
simple one- or two-dimensional graphs such as line
charts, bar charts, pie charts, etc., the data it can provide
are basically in tabular format: they are either limited to
the size that a computer screen can handle, or printed on
a report that may be several hundred pages long. The
planners' primary frustration is trying to hold these vast
quantities of information while trying to develop a
solution. This presents an obvious opportunity for
enhancement of the planning process through computer
automation and visualization that can shift some of the
planners’ mental load to their perceptual system.
Research Methodology
Our approach to the visualization of planning data
is oriented around the planners' problem-solving process
modeled by Newell and Simon [13, 18]. A general
problem-solving process has the following stages:
identifying the problem, generating alternatives,
evaluating alternatives, and choosing solution(s).
Consciously or unconsciously, a planner may iterate
among the stages until the problem is solved. The
application of Newell-Simon’s model to solving a
problem results in a domain problem space. Figure 1
shows the production planning problem space. A 12month plan can be broken down into a first-12-weeks
sub-plan and a remaining-weeks sub-plan. Furthermore,
the first-12-weeks sub-plan can be considered from four
different perspectives or as four sub-problems: PPLbased is for one assembly line only; time-based for one
planning week; component-based for dealing with
component shortage problems; and capacity-based just
for capacity shortfall problems. Actions can be taken
when planners try to develop alternatives. As there are
different abstract levels involved in the problem space, a
planner needs different types of information support for
each level.
12 month plan
Evaluation
first 12 weeks
remaining weeks
Evaluation
PPL-Based
subproblem
Time-Based
subproblem
Component-Based
subproblem
Capacity-Based
subproblem
Evaluation
Action n
Action 2 Action 1
Actions
Final
Evaluation
Figure 1. Production Planning Problem Space
There are two challenges for visualizing production
planning data: how to handle massive data in both size
and dimensionality, and what should be the geometric
structures for the data and the relationships among the
data. A typical manufacturing plant can easily have
hundreds of products, thousands of components, several
assembly lines, and tens or hundreds of machines or
tools. It is impossible to show all data items on a
computer screen at once. Although detailed data items
might be needed at some point during the problemsolving process, most of the time they are not necessary.
We introduce “indicators” as aggregation results for
critical data objects. These indicators provide relevant
information for the planners at different stages during
the problem-solving process. Each of the indicators, and
some of the original data objects, must have a basic
geometric structure (called “abstract”) that should be
consistently used for the entire problem-solving process.
The geometric structures indicating relationships among
indicators and data also need to be carefully designed so
that they are meaningful to the planners and
representable on a computer screen. The procedure of
visualizing planning data for production planning
support is shown in Figure 2, where only one sub-
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
problem (one node) in the problem space is shown
(first-12-weeks). The entire process of visualizing
planning data involves applying the procedure in Figure
2 to each of the “nodes” in the problem space.
Problem Space Original Data Indicators Abstracts
PPLs
first 12 weeks
planning
weeks
demand
satisfaction
BOM
capacity
Evaluation
- revenue
- capacity usage
- demand satisfaction
- critical subproblem
products
a1
a2
components
availability
Visuals
a3
satisfaction
problems
components
inventory
demand
scheduled
receipts
commit
a4
capacity
availability
time
a5
PPLs
a6
Figure 2. Visualization Model for Production
Planning Support
VIZ_planner, the prototype of a decision support
system for production planners, has been developed
based on the above visualization model. It is able to
interactively construct visual representations at different
abstract levels. It also allows planners to do what-if
analysis for potential actions. By visualizing critical
planning data, it assists a planner in identifying
production planning problems, generating and
evaluating alternatives for solutions, and developing
superior solutions. To evaluate the effectiveness of the
visualization representations for improving planners’
problem-solving performance, a lab experiment was
conducted as part of the entire research methodology.
Visualizing Planning Data for
Decision-Making Support
VIZ_planner has two basic components: a
production planning management system (named
NORM_planner) that creates production plans based on
the MRP II process [5, 21], and an information
visualizer that aggregates and then visualizes critical
planning information for planners. As it is difficult to
collect real production data from a manufacturing plant
for software system tests and for the experimental study,
a raw data generator is developed as a pre-engine of
VIZ_planner to generate all raw planning data that
would otherwise be collected from a plant.
Data Aggregation
The purpose of data aggregation is to compress the
original data into representable sizes because of limited
display space (for instance, a computer screen). To
reduce the complexity of the research problem, we
consider components and capacity constraints only.
The raw data objects involved in production
planning are PPLs list, products list, unique components
list, common components list, bill of materials (BOM),
inventory, scheduled receipts of components, demand,
and PPL capacity. Some of the raw data, such as
Demand, are changeable by the planners during the
planning process. The production planning management
system NORM_planner calculates the time-phased plan
for each of the components based on the demands, bill
of materials, inventory, and scheduled receipt of
components. The time-phased plan has the information
about the available components list and the
corresponding component shortfall list. Based on the
production mix and time-phased plan, NORM_planner
then calculates the producible products based on
component availability. Capacity information is given to
each of the six assembly lines during the planning time
period. The final production plan of the products (called
commit) is determined by both the capacity and the
components' availability.
The data objects needed for the planner to evaluate
the final plan include: revenue profile, demand
satisfaction (by comparing demand and commit),
product satisfaction for each of the products based on
available components, critical component shortfall, and
the information about each product supported by each of
its components. All the data objects except the revenue
profile incorporate many detailed data items. These
detailed data items do not provide an overall
understanding of the planning problem. They are what
we want to aggregate into indicators. All the indicators
are taken as percentages so that relative comparisons are
possible.
Demand Satisfaction Indicators
The production demands of a plant can be measured
by revenues (in dollars) or other factors. We use the
ratio of achievable revenue to required revenue to
indicate how many of the production demands can be
satisfied based on tool capacity availability and
component availability. For example, if the plant is
planning to achieve $10,000 for some planning period,
and only $5,890 can be achieved according to the
available components and capacity, then the demand
satisfaction indicator is 58.9%. Each of the six PPLs
(Production Pull Lines) has a demand satisfaction
indicator for each of the planning weeks,
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
NL
∑f
DLk =
n•
Pkn
n =1
NL
∑f
• 100
n•
1≤ L ≤ 6
Dkn
n =1
where DLk is the demand indicator for PPL L at
week k, NL the number of different products produced
by PPL L, fn the profit (in dollars) of product n, Dkn the
required production quantity for product n at week k,
and Pkn the actual quantity of product n based on
available capacity and components.
Component Availability Indicators
ECAT has more than 2000 different types of
components. During the production planning period,
demands are allocated to products, and products are
allocated to PPLs. Some components are used by single
types of products, some are used by multiple products.
The proportion of the quantity of all available
components to the total quantity of required components
is the indicator for component availability:
ML
∑ (R
km
CLk =
- Skm)
m =1
• 100 1≤ L ≤ 6
ML
∑R
km
m =1
where CLk is the component indicator for PPL L at
week k, ML the number of different components after
decomposing NL products produced by PPL L, Rkm the
required quantity for component m at week k, and Skm
the shorted quantity of component m at week k.
Capacity Availability Indicators
In ECAT, each assembly line has production
capacity calculated in hours of tool usage. According to
the production demands of that assembly line, the
potential capacity utilization is the proportion of
required hours to available hours. We use the utilization
as the indicator for capacity availability,
NL
∑D
ULk =
kn •
Tn
n =1
HLk • 60
• 100
1≤ L ≤ 6
where ULk is the utilization for PPL L at week k,
NL the number of different products produced by PPL
L, Dkn is the required quantity for product n at week k,
Tn is the average minutes required for producing a
single unit of product n, HLk is the available hours for
PPL L at week k.
Information Visualizing
The next step is to map data and/or indicators into
visual images, which provides insight into planning
problems. In order to design the geometric structures for
most of the data objects and indicators, we studied the
cognitive processes and activities of the planners and
found that most of the cognitive activities involve many
comparisons. Since the final visual images should not be
too complicated for business managers to understand
and use, we extended the traditional bar charts and used
them as our theme for the entire visualization system.
Most of the values are represented by bars in a broader
sense (such as areas or lines) because bars are good for
comparisons [6] and are well understood by people,
including planners.
The layout of multiple data objects/indicators in one
visual image is determined by the dependency
relationships among the data objects/indicators. We say
data object A is dependent on B, or B determines A
(fully or partially), if A is a function of B: A = F(B). In
this case we say there is a dependency relationship
between A and B. In a virtual multi-dimensional space,
each data object/indicator has its own axis just as in an
ordinary one-dimensional space. The following rules, as
partially depicted in Figure 3, apply to data
objects/indicators to make them geometrically
connected.
Rule 1. If A is determined or partially determined
by B, then A and B construct a 2D plane by sharing the
same origin. For example, demand satisfaction (A) is
partially determined by components availability (B).
Rule 2. If A and B are time series data or location
data and determine other data objects/indicators at the
same time, then A and B construct a 2D plane by
sharing the same origin. For example, it is meaningful to
say the capacity availability (C) for assembly line 2 (B)
at planning week 3 (A).
Rule 3. If A and B have no dependency
relationship, but they both partially determine C, then A
and B could be in a parallel position sharing the same
origin. For instance, component availability (A) and
capacity availability (B) have no dependency
relationship between them, but they both determine the
demand satisfaction (C).
Rule 4. All elementary graphing techniques and
rules, when not conflicting with the above three rules,
apply to up to 3 data objects/indicators.
Rule 5. Combinations of the above rules will be
used to geometrically connect all the data objects/
indicators involved in one visual image.
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
C
C
A
A
B
Rule 1
B
A
(Time)
(Location)
Rule 2
B
Rule 3
Figure 3. Geometry Construction Rules
From the virtual multi-dimensional space to the
final 2D surface of a computer screen, typical
visualization or computer graphics techniques, such as
projections and perspectives, are used. Figure 4 through
Figure 7 are the final visual images at different abstract
levels that correspond to humans’ problem-solving
stages. They illustrate the design methods and rules
listed above. These visual images picture a scenario of a
planning process for six assembly lines over 12 weeks,
where 50 products and 812 components are considered.
The color coding is used for better human perception [4,
19, 20]1. Green is reserved for demand satisfaction,
orange and red for components' availability and
products satisfaction, blue for capacity availability, and
purple for no demand during the time period (see
Products Satisfaction By PPL 5 in Figure 6). The gray
background is for showing the values of bars. The color
coding is consistent over all visual representations.
Relationships among data objects are implied in the
visual images by the ways they are laid out according to
the rules.
Figure 4 shows the global problems and potential at
the highest abstract level. It has two visual images. The
left one is the Global View for Satisfaction & Potential
of the status of the planning results. The visual
representation is at a global and abstract level without
showing details. There are five data objects involved:
twelve planning weeks, six assembly lines, component
availability, capacity leftover, and demand satisfaction
(which is dependent on both components and capacity
availability). This visualization shows the relationships
among these data objects and the change trend of each
data object. It provides the planner a landscape view of
the planning problem from the satisfaction and potential
view. For example, demand satisfactions (the green
bars) are affected by both capacity leftover (blue bars)
and component availability (pink bars), while the latter
two seem to have no necessary relationship between
each other (parallel positions on the same plane).
Among all the PPLs, PPL4 (fourth from left) has
consistent capacity leftover. This implies that this PPL
can handle more demand than it does now. For PPL6 at
1
Color images are available from the first author upon request.
planning week 1 and week 2, the non-perfect demand
satisfaction is due to the components shortage, since
there is some capacity leftover for those two weeks.
During the planning process, the planners want to
know how severe the problem is. They want to look at
the planning results from problem’s view. The visual
image on the right in Figure 4 has the same layout and
shows the same planning results from the shortfall
aspect. In this visual image, we can see that PPLs 1 and
5 have severe capacity shortfall problems, where PPLs
2, 3, and 4 have severe component shortage problems.
This image shows the unbalanced production load to the
six assembly lines and implies a solution to the problem
at a high level without the need to look at the detailed
data items. By comparing the two visual images in
Figure 4, the planner can get a very thorough
understanding of the planning situation.
If the planner wants to focus on a specific PPL or a
specific planning week, s/he can “zoom in” from the
global view to get the perspective s/he wants. In Figure
5, the planner is focusing on PPL5 by zooming it in but
still considering it within the context of the whole
factory. The planner is also considering planning week
4.
Figure 6 has two views indicating the component
shortage problem that affects productions satisfaction.
The Global Products Satisfaction view shows the
satisfactory situation for each product in each PPL
throughout all planning weeks. For example, PPL5 has
eight products (eight lines) and they all have problems
in week 6; this leaves the corresponding green bar
(demand satisfaction) below one hundred percent. From
here the planner may want to further explore the
situation of each product in this PPL, from which comes
Products Satisfaction By PPL5, the right image in
Figure 6. This picture lists the product identification
numbers and shows the unsatisfied situation by detailed
numbers. For instance, only 37% of product P_26 can
be produced in week 3 due to the component shortage
problem.
Figure 7 gives more details for finding out what
causes 37% satisfaction of P_26. Product P_26
Supported by Components lists all the ID numbers of
the components required by P_26. The last three blueinked identifications are common components that are
shared by multiple products. In this picture, each bar
corresponds to one component for one week and
indicates the satisfaction the component can provide for
product P_26. Week 2 through week 8 show the
component shortage problems. As the complete sets of
components are needed for the production of P_26, the
picture shows which component is most critical. For
example, in week 3, both common components 40 and
44 are short. However, common component 44 (the
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
shortest bar) is more critical. In other words, C0_44
determines how many of P_26 can be produced for this
week. however in week 6, it is C0_40 that is more
critical. C0_44 is shared by another product P-41, while
C0_40 is needed by a total of four products. If the
planner is concerned that sharing of common
components may cause problems for P_26, or is
considering changing the production mix, then s/he can
view the Allocation of Common Components at Week
n, where all sharing products, allocation amounts to
products, and additional amounts needed are indicated.
Figure 7 shows the Allocation of C0_44 at Week 3 and
the Allocation of C0_40 at Week 3. Re-allocation of the
available amounts of common components may be an
alternative for solving or reducing a component
shortage problem for some products.
What-if analyses are supported so that the planner
can evaluate possible alternatives or actions. Visual
images are affected when the planner changes planning
data.
Evaluation
The prototype system has been informally
evaluated by real-world planners from different
manufacturers. Based on the encouraging comments and
suggestions from these planners, a lab experimental
study was conducted to evaluate the effectiveness of the
prototype system on problem-solving performance. A
total of 13 graduate students who have either taken
production planning courses or had real world
production planning experience participated in the
study. Two randomly assigned groups worked on the
same decision problems by using two different
computer systems respectively within a limited time
period: one was a traditional MRP II type system
(NORM_planner) with tabular format for data displays,
and the other was VIZ_planner with added visual
images. In order to pursue the experiment within
affordable mental effort by the subjects, the production
planning tasks (decision problems) were simplified with
only components constraints, one assembly line, and
limited products and components. Corresponding to this
simplification, only three visual representations were
used in VIZ_planner: Product Satisfaction (Figure 6),
Products Supported by Components (Figure 7), and
Allocation of Common Components at Weeks (Figure
7). The subjects were trained in basic production
planning concepts and decision problems, and in using
corresponding systems before the experiment. They
were also aware of the rewards for best performance.
Experimental data were collected by asking the subjects
to fill out questionnaires immediately after the
experiment, and by computer logs.
The results of the experiment indicate that (1) the
Viz group generated more alternatives for solutions than
the NORM group did; (2) the Viz group made more
efficient changes in the raw data in order to achieve
high revenues than the Norm group did; (3) the Viz
group was more confident about the decision tasks than
the Norm group was; and (4) the Viz group was more
satisfied with the outcomes than the Norm group was.
There was no difference found in the quality of the
solutions and perceived information overload, which
may be due to the reduced complexity of the decision
problems that decreases the advantages which
VIZ_planner has in dealing with multiple constraints
and huge data volume. All the Viz users felt that the
visualization system was cooperative with their
problem-solving strategies, while only half of the Norm
users felt that NORM_planner was cooperative with
their problem-solving strategies.
Another interesting finding from the experiment is
that the Viz group had only 15 minutes to watch a
demonstration of VIZ_planner, and 40 minutes for
practice in getting familiar with it after they knew how
to use NORM_planner. This means that the final visual
representations for manufacturing production planning
are very easy to understand and use. Also, 83.3% of Viz
members had intensive use of the visual representations
during their problem-solving process. One subject who
did not use visual representations much explained that
“I prefer to use exact data rather than percent
information.” The same subject, when asked why he
selected Allocation of Common Components at Weeks
(Figure 7) as his most-liked visual, said “because it
gives additional information that I cannot get easily
from the real figures.”
Conclusion
In this paper, we described the research results
of developing multi-dimensional interactive visual
representations of management data that are huge in
volume, have complicated relationships among data,
have no physical objects corresponding to them, and
have to be quickly understood and interpreted so that
effective interactions and decision-making can be
carried out by high-level managers. A visualization
system for manufacturing production planning decisionmaking support has been designed and implemented in
Sun Workstations based on the proposed visualization
model. A preliminary evaluation in a lab experiment has
shown that the visualization system can help a planner
to develop more alternatives for solutions, make more
efficient changes on the raw data, and feel more
confident about the tasks and more satisfied with the
outcomes. The evaluation has further supported the
Proceedings of INFORMS First Conference on Information Systems and Technology, May, 1996
advantages of the user-centered manufacturing
production planning visualization model. As
visualization of large data sets is a problem of concern
in many business domains and yet little research has
achieved any results in applying visualization to
business domains, this research has both theoretical and
practical values.
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