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Adaptive Exception Monitoring Agents for Management by Exceptions
Rey-Long Liu劉瑞瓏
中華大學資訊管理系所
2
Outline• Introduction
– Exception monitoring agents for management by exceptions
• AAMBE– Adaptive Agents for supporting
Management By Exceptions • Experiments on financial management• Evaluation• Conclusion
3
Introduction
• Management by Exceptions (MBE)– The manager defines a set of critical success
factors (CSF).– He/she should focus on other tasks, while
simultaneously control the CSF.– Exceptions of the CSF should be handled in a
prompt and complete manner.=> Exception monitoring is a key to MBE
4
• Exception monitoring (EM)– Passive EM
• The monitor is notified (by the server) of updates, and accordingly checks for exceptions
• Example technique: Dynamic Data Exchange (DDE)• Drawback: Not all servers support passive IM
– Active EM• The monitor queries the server for possible updates,
and accordingly checks for exceptions.• Example technique: Periodical monitoring• Drawback: Difficult to estimate timing of updates
=> Active EM is necessary
5
ReplyQuery
Request for update Request for update
Maintain Maintain
ReplyQuery
Information Item 1 …
Exception Monitor
… Information Server N
Information Updating Systems
Information Item NInformation Item 2
Maintain
Information Server 1 Information Server 2
Query Reply
Set Report exceptions
Manager
Active Exception Monitoring for MBE
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• Challenges of active EM– Each item may be updated at any time.– It’s impossible to know the exact
happening time of an update.– Each item may have its own update
behavior, which may even evolve over time.
– Uncontrolled monitoring may make the related networks and servers exhausted.
=> Adaptability of active EM is a must
7
Criteria for evaluating exception monitoring systems
Timeliness(A) Time Delay = Σ {Time of being detected - Time of happening}, for all exceptions(B) Average Time Delay = Time Delay / E, where E = total number of detected exceptions(C) Timeliness = 1 / Average Time Delay
Weighted Timeliness(A) Average Weighted Time Delay = Time Delay / WE, where
WE =Σ(Ik * times of being detected ), for all detected exceptions (B) Weighted Timeliness = 1 / Average Weighted Time delay
Effectiveness(A) Total Resource Consumed = Σ (N k) for all CSFk, where
N k= Times of checking CSFk by querying the information server(B) Effectiveness = Number of exceptions detected / Total Resource Consumed
Weighted Effectiveness =(Σ (Ik* D k), for all CSFk) / Total Resource Consumed, where D k = Number of exceptions detected for CSFk
Exception Detection Ratio =Number of exceptions detected / Total number of exceptions that happened
Weighted Exception Detection Ratio =(Σ (Ik* D k), for all CSFk) / (Σ (Ik* H k), for all CSFk), whereH k = Number of the happening exceptions of CSFk
8
AAMBE
• AAMBE: Adaptive Agents for MBE.– For the manager
• Timeliness and Weighted Timeliness• Exception detection ratio and Weighted EDR• Maximum tolerable delay of exception detection
– For the system administrator• Effectiveness and Weighted Effectiveness
– The methodology• An adaptive agent for monitoring a CSF
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Adaptiveagent coordinator
requestgrant
grant
request
request
grant
requestgrant
requestgrant
grant
request
request
grant
Agent1
Agentt
Agent2
Agentt+1
Agentt+2
AgentN
Agentt-1AgentN-1
monitor monitor
monitor monitor
monitor monitor
monitor
Itemt+1
monitor request
grant
Adaptive Exception Monitor
Itemt+2
ItemN-1
ItemN
Item1
Item2
Itemt-1
Itemt
AAMBE: Adaptive Agents for MBE
10
Behavior of each exception monitoring agentEstimated lower bound of the time periods in which the agent might find an update (LDU) = Unknown;Estimated upper bound of the time periods in which the agent might find an update (UDU) = Unknown;Estimated time difference between the two exceptions (TDE) = Unknown;MAE = The agent’s maximum tolerable time delay of detecting an exception;Repeat
PNG = The agent's time period of not being granted;If (PNG ≥ MAE) Issue a Type-1 request;Else if (PNG ≥ UDU) and (PNG ≥ TDE ) Issue a Type-2 request;Else if (both LDU and UDU are Unknown) Issue a Type-3 request;Else if (UDU is Unknown) Issue a Type-4 request;Else if (LDU is Unknown) Issue a Type-5 request;Else if (TDE ≥ PNG ≥ UDU) Issue a Type-6 request;Else if (PNG ≥ LDU) Issue a Type-7 request;Else if (PNG > η * LDU) Issue a Type-8 request;If (the agent's request is granted)Check and validate whether an exception occurs, and if so, handle the exception;If (an update is detected now)
UDU = Time difference between the two successful detection actions;AmountToException = Current value – standard value of the item being monitored;UpdateAmount = Current value - last value of the item being monitored;TDE = (AmountToException / UpdateAmount) * UDU;
ElseLDU = Time difference between current time and the time when previous update was detected;
Until the agent is terminated
11
Behavior of adaptive agent coordinator
RepeatIf the coordinator is ready to grant a request now (by the frequency translated from µ)
If (there are requests from the agents)If (there are requests of Type-1) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-2) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-3) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-4) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-5) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-6) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-7) Grant a request from the agent with the highest Ik;Else If (there are requests of Type-8) Grant a request from the agent with the highest Ik;
Until the system is terminated
12
Experiments
• Performance investigation in the context of financial management– Simulating 181 days of financial operations of
merchandising trades (e.g. retailers)– Some of the criteria (e.g. timeliness and exception
detection ratio) can be measured only in a simulated environment
• Baseline EM systems– Random monitor– Periodical monitor
13
Merchandise priceOrdered quantity (OQ)Shipped quantity (SQ)
Sales return
Revenue Cycle
Inventory levelPurchase quantity (PQ)Received quantity (RQ)
Purchase priceReorder point
Purchase return
Expenditure Cycle Finance Cycle
Accounts receivable (A/R)Notes receivable (N/R)Accounts payable (A/P)
Notes payable (N/P)Exchange rate
Cash amount (CA)
Main cycles of financial management in merchandising trades
14
Purchasereturn
State transitions in simulated financial operations
S12PQ ↑
A/P or N/P↓CA ↓
Inventory ↓A/P or N/P↓RQ ↓
Inventory ↑A/P or N/P↑RQ ↑
S9
S8S7
S10S11
Payment to supplier
Purchase Start
(a) SalesFSM
(b) PurchaseFSM
TriggerPurchaseFSM
A/R or N/R↓CA ↑
OQ ↑
Inventory ↑A/R or N/R↓SQ ↓
Inventory ↓A/R or N/R ↑SQ ↑
S2
Merchandise shipped
S6
S3
S5S1Order received
Start
S4
Inventory ≤reorder point
Sales returnInventory > reorder point
No sales return
Ask for payment
Payment received
Merchandise received
Asked for payment
No purchasereturn
15
Generation of the events that trigger financial operations
(1) Timing of sale: Each item is associated with three sales frequencies: λ1 (for slack season), λ2 (for normal season), and λ3 (for boom season). In each time period, the probability density function of selling an item follows the exponential distribution (i.e. given λ, the probability of selling the item in time interval α is P(X≦α) = 1-e-λ α).(2) Amount of sale: Each item is also associated with three pairs of upper bounds and lower bounds of its sales amount. The three pairs correspond to the three sales seasons of the item respectively. In each order from the customer, the ordered quantity is uniformly distributed between the pair of sales bounds for the current season.(3) Safety stock: Each item is also associated with three safety stocks. The three pairs are for the three sales seasons of the item.(4) Reorder point: Each item is also associated with a lead time (for purchasing the item). Thus its three reorder points are estimated based on the lead time and its safety stock in different sale seasons.(5) Exchange rate between currencies: The exchange rate between NT dollars and US dollars is randomly generated between 28 to 32 for every hour.(6) Purchase price: In every 72 hours, randomly determine when to change purchase prices. At that time, an item is randomly selected. Its purchase price is randomly changed between -2% and 2%.(7) Sales return: In every 24 hours, randomly determine when a sales return may happen. At that time, an item is randomly selected. Its amount of sales return is randomly generated between 0% to 8%.(8) Purchase return: In every 48 hours, randomly determine when a purchase return may happen. At that time, an item is randomly selected. Its amount of purchase return is randomly generated between 0% to 15%.(9) Accounts payable (AP) and notes payable (NP): One half of the purchases are paid by cash, and the others are by notes (due dates are set to be 15 days after the issue date).
16
Main categories of the exceptions to be monitored Exception category Validation rule Impact to the business
(1) Inventory level Inventory level ≤ Reorder point Customer satisfaction, inventory control
(2) Supplier's delivery ratio
Delivery ratio ≤ 0.95 Market planning, customer satisfaction,purchase policy
(3) Purchase price Purchase price ≥ Sales price * 0.85 Business profitability, customer satisfaction
(4) Due date of notes payables (NP)
Due date ≤ Current date + 3 Short-term financing, business credit
(5) Sales return ratio Amount of sales return / Sales amount ≥ 0.1 Customer satisfaction, business credit, quality control
(6) Purchase return ratio
Amount of purchase return / Purchase amount ≥ 0.1
Business partner cooperation,purchase policy, quality control
(7) Exchange rate Current exchange rate ≥ (Exchange rate when purchasing ) * 1.05 OR Current exchange rate ≤ (Exchange rate when purchasing) * 0.95
Business profitability, risk managing
(8) Cash amount Cash amount ≤ 4800000 Cash management, liquidity
17
0
0.005
0.01
0.015
0.02
0.025
0.03
101
108
115
122
129
205
212
219
226
305
312
319
326
402
409
416
423
430
507
514
521
528
604
611
618
625
Date
Tim
elin
ess
AAMBE
PeriodicalExM
RandomExM
0.7
0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
101
108
115
122
129
205
212
219
226
305
312
319
326
402
409
416
423
430
507
514
521
528
604
611
618
625
Date
Effec
tiven
ess AAMBE
PeriodicalExM
RandomExM
0.3
0
0.05
0.1
0.15
0.2
0.25
101
109
117
125
202
210
218
226
306
314
322
330
407
415
423
501
509
517
525
602
610
618
626
Date
Exc
epti
on d
etec
tion
rati
o
AAMBE
PeriodicalExM
RandomExM
Timeliness, effectiveness, and exception detection ratio
18
0
0.2
0.4
0.6
0.8
1
1.2
101
107
113
119
125
131
206
212
218
224
302
308
314
320
326
401
407
413
419
425
501
507
513
519
525
531
606
612
618
624
630
Date
Wei
ghte
d tim
elin
ess
AAMBE
PeriodicalExM
RandomExM
0
5
10
15
20
25
30
101
107
113
119
125
131
206
212
218
224
302
308
314
320
326
401
407
413
419
425
501
507
513
519
525
531
606
612
618
624
630
Date
Wei
ghte
d eff
ectiv
enes
s
AAMBE
PeriodicalExM
RandomExM
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
101
10811512212
9205212219
226305312319326 402
409416423430507514521 52860461161862
5
Date
Wei
ghte
d ex
cept
ion
dete
ctio
n ra
tio
AAMBE
PeriodicalExM
RandomExM
Weighted timeliness, effectiveness, and exception detection ratio
19
Improvement on all evaluation criteria
Criterion PeriodicalExM RandomExM AAMBE ImprovementTimeliness 0.00211 0.00235 0.00363 63%Weighted timeliness 0.05633 0.06091 0.14123 141%Effectiveness 0.39643 0.22443 0.53655 73%Weighted effectiveness 10.61932 5.69595 20.61809 153%Exception detection ratio 0.07751 0.04500 0.12104 98%Weighted exception detectionratio
0.06584 0.03512 0.15401 205%
20
Evaluation
• Information agents• Information monitoring• Reactive & deliberative agents• Coordination & negotiation among agents• Learning to coordinate agents• Future research directions
– Quality of Services (QoS)– Monitoring textual exceptions
21
Conclusion
• MBE: an effective administration strategy– Active EM: a key to MBE– Adaptive EM: a must for active EM
• Agent-based adaptive EM:– A scalable and customizable methodology– Adapting to
• Preference of the manager• Update behavior of the information items about CSF