Adaptive Exception Monitoring Agents for Management by Exceptions

Rey-Long Liu劉瑞瓏

中華大學資訊管理系所

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Outline• Introduction

– Exception monitoring agents for management by exceptions

• AAMBE– Adaptive Agents for supporting

Management By Exceptions • Experiments on financial management• Evaluation• Conclusion

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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

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• 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

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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

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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

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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

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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

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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

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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

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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

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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

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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).

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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

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AAMBE

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PeriodicalExM

RandomExM

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PeriodicalExM

RandomExM

Timeliness, effectiveness, and exception detection ratio

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RandomExM

Weighted timeliness, effectiveness, and exception detection ratio

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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%

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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

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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