1 information filtering converting data into relevant information for decision making
TRANSCRIPT
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Information Filtering
Converting Data into Relevant Converting Data into Relevant Information for Decision MakingInformation for Decision Making
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Information as Decision Support
• Accountants add value by helping turn data into useful information
• Students asked to analyze quantitative data often have trouble extracting and presenting information
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The Problem
• Masses of data and minimal structure
• Non routine task
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The “Solution”
• Exposure to examples of how data can be turned into information
• Practice in analyzing or filtering data to provide decision relevant information
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Creating Information Using Data
• Ways information can be turned into information:– Examining the data for relationships (data
mining or analysis)– Categorizing the data in ways that clarify
alternatives (summarizing using various criteria)
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A Class on Information Filtering
• Introduction: examples of information filtering in businesses
• An example from sales
• An example from production
• Conclusions
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National Highway Safety Administration
• Ascential Software works with large data sets (an average of 10 million to 20 million data records) to extract information for customers
• The firm has worked on projects for Lockheed Martin, the U.S. Navy, and the National Highway National Highway Safety AdministrationSafety Administration.
• The NHSA project involved matching police matching police records with ambulance reports to justify the records with ambulance reports to justify the need for seat beltsneed for seat belts.
Signal (February 1999)
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Decision Relevant Information Through Data Filtering
• One grocerygrocery chainchain segmented customers by recency, frequency and spending and found that 30% of their customers produced 70% of their sales. And now they know which ones.
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Decision Relevant Information Through Data Filtering
• Wal-MartWal-Mart uses data to decide where to display items (they put bananas near the cereal because people typically buy them together, tissues near cold remedies, and measuring spoons near baking items).
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Decision Relevant Information Through Data Filtering
• The Veterans Health Administration The Veterans Health Administration used a new computerized system to determine the relationship between pneumonia vaccination and death or serious illness
• The results were used to support a decision to push vaccination, resulting in rates of about 84% versus national rates of about 50%
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Decision Relevant Information Through Data Filtering
• Blue Cross Blue Cross used an analysis of heart patient outcomes by hospital to determine which hospitals to include as reimbursable under their program
Burton, T. M. 1999. Bed Check: HMO Rates Hospitals. Wall Burton, T. M. 1999. Bed Check: HMO Rates Hospitals. Wall Street Journal April 22: A1.Street Journal April 22: A1.
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Creating Decision Relevant Information Through Data Filtering
• One telephone order business has a computer feed the order taker information about related products the customer might also buy given what they just entered on the order
Public Utilities Fortnightly Winter Supplement (1999)Public Utilities Fortnightly Winter Supplement (1999)
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Consider an Order Processing Group
• Look the following slideLook the following slide representing information on orders processed– This is the raw data for a small work group (imagine
the data for a large one)
• Is it currently "decision relevant Is it currently "decision relevant information" or does it need further information" or does it need further processing?processing?
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Daily Orders Processed by Person
0
10
20
30
40
50
60
70
80
90
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Day
Number of
Orders
Joe Mary
Fred Suzy
Marle
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Order Processing Information
• Consider the next slide.• It presents information for the same group that has
been summarized.• What can you tell about the processing group from
this slide?
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Daily Orders Processed
0
50
100
150
200
250
300
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Day
Number of Orders
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Applying an Information Filter
• How can we filter filter the information to remove “noise”?remove “noise”? S
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Applying an Information Filter
• Consider the following slide.
• This is filtered information.• It represents number of order processing workers
who were were below their 20 day average number of orders processed by day.
• What can you tell about the process from this?
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Number of Processors Below 20 Day Averages
0
1
2
3
4
5
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49
Day
Number of Processors Below
Their Average
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Applying an Information Filter
• The underlying “reality” that produced the preceding numbers:– One person quit (Day 25) and was replaced by
a more effective employee (Day 26).– Two people were sick, one on Day 30 and one
on Day 33.– The system went down on Days 15 and 21.– ALL the rest of the variation was due to ALL the rest of the variation was due to
random “noise.”random “noise.”
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What Information Is Needed on Processes?
• Now consider a production process with 6 products and 4 production departments.
• The firm wants to reduce cycle timereduce cycle time to better meet customer needs.
• The following slide represents firm current performance and performance by a firm with similar production processes.
• What information does this provide?What information does this provide?
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Product Cycle Times: Current and Desired
0
10
20
30
40
50
60
A B C D E F
Product
Cyc
le T
ime
Cycle Time
BM for CT
Our firmOur firm Benchmark firmBenchmark firm
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Product Cycle Times: Current and Desired
0
10
20
30
40
50
60
A B C D E F
Product
Cyc
le T
ime
Cycle Time
BM for CT
Now that they know improvement is possible, where should they start?
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What Information Is Needed?
• Assume all you have on the benchmark is what is shown.
• What “slicing” would you like next on your own processes to help you decide where to start the improvement process?
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Where Should They Start? S
Product Cycle Time Categories
0
10
20
30
40
50
60
A B C D E F
Product
Da
ys
Wait Time
Order Processing
Transit
Setup
Production
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Where Should They Start?
Product Cycle Time Categories
0
10
20
30
40
50
60
A B C D E F
Product
Da
ys
Wait Time
Order Processing
Transit
Setup
Production
The areas that are the largest and the most amenable to change: which areas qualify?
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Where Should
They Start?
Setup Times for Production Departments by Product
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
P1 P2 P3 P4
Production Department
Se
tup
Tim
es
A
B
C
D
E
F
Wait Times: Products A through F
0
1
2
3
4
5
6
W-P1 W-P2 W-P3 W-P4
Production Department
Wa
it T
ime
in
Da
ys A
B
C
D
E
F
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Where Should
They Start?
Setup Times for Production Departments by Product
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
P1 P2 P3 P4
Production Department
Se
tup
Tim
es
A
B
C
D
E
F
Wait Times: Products A through F
0
1
2
3
4
5
6
W-P1 W-P2 W-P3 W-P4
Production Department
Wa
it T
ime
in
Da
ys A
B
C
D
E
F
Is there a “baseline” for what you could reasonably expect to achieve in the “problem” areas?
Outliers
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What Changes Might Be Reasonable?
• You can simulatesimulate results.
• What would the results be ifif we could reduce all wait times to two days and all wait times to two days and all setups to onesetups to one dayday? NS
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Improved Cycle Times
0
10
20
30
40
A B C D E F
Product
Cyc
le T
ime
in D
ays
New CT
BM CT
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Filtering Information
• Look for relationships:– Accidents and seat belt use– Sales of products that move together (Kleenex
and cold remedies, catalog sales)– Medical treatments and outcomes– Sales orders processed versus “normal”
• Categorize:– Customers with high sales levels– Various types of costs (production activities)