U.S. patent application number 10/843451 was filed with the patent office on 2005-11-17 for method for managing and controlling stability in business activity monitoring and management systems.
Invention is credited to Chen, Li, Ramachandran, Bala.
Application Number | 20050256752 10/843451 |
Document ID | / |
Family ID | 35310509 |
Filed Date | 2005-11-17 |
United States Patent
Application |
20050256752 |
Kind Code |
A1 |
Ramachandran, Bala ; et
al. |
November 17, 2005 |
Method for managing and controlling stability in business activity
monitoring and management systems
Abstract
A stabilization methodology and system component in Business
Activity Monitoring and Management systems. This enables firms to
use Business Activity Management (BAM) systems to manage business
activity by only responding to monitored data when the overall
business performance can be improved. This enables firms to
identify appropriate tradeoffs between potentially conflicting
objectives while meeting business objectives. Information from BAM
systems are analyzed based on models of the business process and
different information filter criteria are assessed for their impact
on business performance indicators. Based on this, a filter
criterion is chosen which is executed by an information filter. The
outputs from the information filter are used as the basis for
deciding the inputs for business process execution.
Inventors: |
Ramachandran, Bala;
(Harrison, NY) ; Chen, Li; (Palo Alto,
CA) |
Correspondence
Address: |
WHITHAM, CURTIS & CHRISTOFFERSON, P.C.
11491 SUNSET HILLS ROAD
SUITE 340
RESTON
VA
20190
US
|
Family ID: |
35310509 |
Appl. No.: |
10/843451 |
Filed: |
May 12, 2004 |
Current U.S.
Class: |
705/7.37 ;
705/7.39 |
Current CPC
Class: |
G06Q 10/063 20130101;
G06Q 10/06 20130101; G06Q 10/06375 20130101; G06Q 10/06393
20130101; G06Q 10/0635 20130101 |
Class at
Publication: |
705/007 |
International
Class: |
G06F 017/60 |
Claims
Having thus described our invention, what we claim as new and
desire to secure by Letters Patent is as follows:
1. A method for analyzing monitored data from Business Activity
Management systems comprising a business process execution
receiving inputs and outputting events and a feedback loop
including a business process monitoring and management system that
depicts business process metrics, said feedback loop receiving
events from the business process execution and identifying and
modifying inputs for business process execution, said feedback loop
further including a stabilizer component executing the method
comprising the steps of: creating and updating a predictive model
of the business process; creating and updating filter policies to
manage monitored information of the business process; and
implementing filtering policies on the monitored information.
2. The method according to claim 1, wherein the predictive model is
used to determine an impact of changing business process execution
inputs on overall business performance indicators.
3. The method according to claim 2, wherein the business impact
determination is used to determine whether or not to create an
exception for the normal timing of activities in a business process
cycle.
4. The method according to claim 1, wherein the step of
implementing filter policies is manifested in an information filter
that allows some monitored data to pass through to elicit some
business response and other data to be rejected, so as not to
elicit any business response.
5. The method according to claim 4, wherein the filter is
incorporated in a computer system or any other suitable electronic
device.
6. The method according to claim 3, wherein monitored data that is
not responded to is used to evaluate potential benefits to value
chain partners.
7. The method according to claim 6, wherein estimates of potential
benefits to value chain partners are used in business negotiations
and contract formulations, to share the resulting benefits among
value chain partners.
8. The method according to claim 1, wherein business impact
determination is used to perform trade-off analysis between
conflicting business objectives.
9. The method according to claim 8, wherein business impact
estimates are used to manage supply chains.
10. The method according to claim 1, wherein the models used for
business impact determination are used to determine the optimal
response frequency for business responses to monitored
information.
11. An apparatus for analyzing monitored data from Business
Activity Management systems comprising: a business process
execution receiving inputs and outputting events; and a feedback
loop including a business process monitoring and management system
that depicts business process metrics, said feedback loop receiving
events from the business process execution and identifying and
modifying inputs for business process execution, said feedback loop
further including a stabilizer component which creates and updates
a predictive model of the business process, creates and updates
filter policies to manage monitored information of the business
process, and implements filtering policies on the monitored
information.
12. The apparatus according to claim 11, wherein the predictive
model is used to determine an impact of changing business process
execution inputs on overall business performance indicators.
13. The apparatus according to claim 12, wherein the business
impact determination is used to determine whether or not to create
an exception for the normal timing of activities in a business
process cycle.
14. The apparatus according to claim 11, wherein the step of
implementing filter policies is manifested in an information filter
that allows some monitored data to pass through to elicit some
business response and other data to be rejected, so as not to
elicit any business response.
15. The apparatus according to claim 14, wherein the filter is
incorporated in a computer system or any other suitable electronic
device.
16. The apparatus according to claim 13, wherein monitored data
that is not responded to is used to evaluate potential benefits to
value chain partners.
17. The apparatus according to claim 16, wherein estimates of
potential benefits to value chain partners are used in business
negotiations and contract formulations, to share the resulting
benefits among value chain partners.
18. The apparatus according to claim 11, wherein business impact
determination is used to perform trade-off analysis between
conflicting business objectives.
19. The apparatus according to claim 18, wherein business impact
estimates are used to manage supply chains.
20. The apparatus according to claim 11, wherein the models used
for business impact determination are used to determine the optimal
response frequency for business responses to monitored information.
Description
DESCRIPTION
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention generally relates to a methodology and
apparatus for managing and controlling stability in Business
Activity Management (BAM) systems.
[0003] 2. Background Description
[0004] Business Activity Monitoring and Management is a technology
enabling the visibility and monitoring of real-time business
information. Examples are (i) Sense & Respond (see G. Lin et
al. "The Sense & Respond Enterprise", OMRS Today, April 2002,
p. 34) and (ii) Supply Chain Event Management (see M. Bittner,
"E-Business Requires Supply Chain Event Management", AMR Research
Report, November 2000). The underlying value proposition of this
technology is that it enables the use of real-time information to
update operational policies and manage execution accordingly.
[0005] In the typical usage of Business Activity Monitoring and
Management systems, enterprise information is monitored in
real-time or near real-time and converted to business performance
indicators which can be displayed on dashboards or other visual
form to different business role players. Also, different criteria
can be specified to detect situations of interest to the business
role players, triggering alerts in different forms that can prompt
business responses. These alerts can take multiple forms, such as
pop-up messages on a computer screen, an e-mail, a mobile phone
call, and the like. The persons receiving this message makes a
business judgement on the severity of the alert and potential
business consequences and takes appropriate steps to modify the
inputs that drive business process execution.
[0006] Although updating based on real-time information can be
beneficial for operational management, it need not always be. In
some cases, it can result in local operational improvement, while
deteriorating system-wide performance. For example, the phenomenon
of demand variablility amplification in a multi-echelon supply
chain (also known as The Bullwhip Effect) has been recognized in
many diverse industries (see H. L. Lee, V. Padmanabhan and S.
Whang, "Information Distortion In a Supply Chain: The Bullwhip
Effect", Management Science, Vol. 43, No. 4, p. 546). Uncoordinated
frequent actions, taken in response to changes in demand/supply
information at the downstream sites in a supply chain, can cause
excessively higher demand variability to the upstream sites, which,
in turn, results in excessive inventories as one moves up the
chain. A forecast-driven inventory control policy involving
frequent updates is one of the key drivers of the demand
variability amplification phenomenon. This raises the question of
how to respond to real-time or near real-time information that is
enabled by Business Activity Monitoring and Management systems in
an optimal way without triggering any undesired effect on business
performance.
[0007] The background described above indicates a need for
stabilization mechanisms in Business Activity Monitoring and
Management systems that enable the appropriate usage of monitored
information, i.e., to improve business performance and not to have
unintended consequences in business performance deterioration. This
requires the usage of monitored information in a way, that all the
instability factors, such as information distortion in the bullwhip
effect case, are kept under control.
SUMMARY OF THE INVENTION
[0008] It is therefore an object of the present invention to
provide a methodology that enables the analysis of information
coming from Business Activity Monitoring and Management systems to
determine the potential impact that responding to such information
would have on the overall business performance indicators. By doing
so, it ensures that the monitored data is used to improve business
performance while all the instability factors are kept under
control, thus stabilizing the BAM system.
[0009] This invention introduces a Stabilizer component in Business
Activity Monitoring and Management. This Stabilizer component
analyzes monitored data and suitably modifies the data and uses the
processed data in determining the business process execution
inputs. The Stabilizer component comprises the following
sub-components:
[0010] 1. A model for predicting the outputs of business process
execution.
[0011] 2. A method for evaluating policies that specify the filter
characteristics and choosing a policy for implementation. The
filter policies specify schemes for modification of the monitored
data, which are subsequently used for determining the business
process execution inputs.
[0012] 3. A filter to process the monitored data according to the
chosen filter policy.
[0013] The invention contemplates other ways of using the
Stabilizer component in the business process execution feedback
loop, including filtering and stabilizing the alerts seen on a
dashboard or alerts received using other electronic medium.
[0014] This invention can help firms realize the full benefits from
the visibility of real-time or near real-time business performance
indicators. In sharp contrast to prior art techniques, this method
enables the usage of monitored information to update business
process execution inputs only when such an update can result in
potential improvement in business process performance.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The foregoing and other objects, aspects and advantages will
be better understood from the following detailed description of a
preferred embodiment of the invention with reference to the
drawings, in which:
[0016] FIG. 1 is a block diagram showing a representative process
for using Business Activity Monitoring and Management systems;
[0017] FIG. 2 is a block diagram showing the process for using
Business Activity Monitoring and Management systems with a
Stabilizer according to the present invention;
[0018] FIG. 3 is a block diagram showing the essential components
of the Stabilizer shown in FIG. 2;
[0019] FIG. 4 is a block diagram, similar to FIG. 2, showing an
alternative process of using Business Activity Monitoring and
Management systems with a Stabilizer;
[0020] FIG. 5 is a flow diagram showing a high level description of
the procedures implemented in the Stabilizer;
[0021] FIG. 6 is a pictorial representation showing a two-stage
supply chain that is used as an example to the illustrate the
Stabilizer according to the present invention;
[0022] FIG. 7 is a flow diagram showing how the Stabilizer works in
the Supply Chain Monitoring and Management scenario shown in FIG.
6; and
[0023] FIG. 8 is a graph showing simulation results confirming
existence of an optimal response frequency for a two echelon supply
chain model.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION
[0024] In the following description, we assume the existence of a
Business Process Monitoring and Management system that probes
different enterprise events and monitors different enterprise
performance indicators. The performance indicators could include
metrics both at business and information technology (IT) levels.
This invention is not limited by the specific details of a
particular Business Process Monitoring and Management system. We
assume the existence of one or more mechanisms for accessing the
monitored information and alerts, including, but not limited to
dashboard portals, e-mail, personal digital assistants (PDAs), cell
phones, and the like. We also assume the existence of processes or
mechanisms that use the monitored information to identify and
modify inputs that drive Business Process execution. This invention
is not limited by the specific details of Business Process
execution, including use of workflow engines.
[0025] Referring now to the drawings, and more particularly to FIG.
1, there is shown a representative process for using Business
Activity Monitoring and Management systems. The Business Process
(BP) execution 10 receives inputs 12 which are modified for BP
execution by a feedback loop. The output events from the BP
execution 10 is fed back for modifying inputs to Business Process
execution 12, by a Business Process Monitoring and Management
system 14 that depicts Business Process Metrics 16 and produces
alerts. More particularly, enterprise information is monitored in
real-time or near real-time and converted to business performance
indicators which are displayed to different business role players.
A person receiving a message makes a business judgment and takes
appropriate steps to modify the inputs 12 that drive the BP
execution 10. The problem is that updating the inputs on real-time
information can potentially result in an amplification effect,
making the feedback loop unstable.
[0026] A novel element of this invention is the intelligent use of
monitored data to drive changes in input data 12 for Business
Process (BP) execution 10. FIG. 2 shows the process of FIG. 1 with
the added component of a Stabilizer 18 in the feedback loop between
the business process monitoring and management system 14 and the
method for modifying business process execution inputs 12. The
Stabilizer 18 analyzes the monitored data and suitably processes
the data which is used in determining the business process
execution inputs 12. The implementation of the Stabilizer 18
requires the following components: (a) predictive models for the
business process, (b) filter policies, and (c) an information
filter. This invention does not require that all the above
components be implemented as a single computer program or that it
run on a single computation device.
[0027] As shown in FIG. 3, the Stabilizer 18 comprises a model 180
for predicting the outputs of the BP execution 10. This model can
be updated using historical data on business process inputs and
outputs. Business process events are used along with User
Guidelines by a method 182 to evaluate policies that specify filter
characteristics. The output of the method 182 is a filter policy
for processing of the monitored data in order to be used for
determining the business process execution inputs 12. The filter
policy is selected from a number of filter policies that specify
schemes for processing of monitored data. A filter 184 implements
the selected filter policy and processes the monitored data based
on a chosen filter policy.
[0028] It will be appreciated that the Stabilizer 18 can be located
in the feedback loop shown in FIG. 2 in a different location. For
example, as shown in FIG. 4, the Stabilizer 18 is shown between the
Business Process Monitoring and Management system 14 and the
depiction of business process Metrics and alerts 16.
[0029] FIG. 5 describes the essential functions performed in the
Stabilizer 18. First, business process events are cleansed to
obtain the process data. The input data that drives business
process execution is used to predict process performance
indicators, using models of the business process. Different
elements of the business process may be captured together in one
model or in multiple models. This invention is not limited by the
specific details of a model of the business process. This can take
several forms that include models of the business process that can
be simulated to predict performance indicators and statistical
process models that correlate business process inputs with outputs
and domain-specific models. The predictive model can also be used
to determine the impact of changing business process execution
inputs on overall business performance indicators. Such prediction
can be used to determine whether or not to create an exception for
the normal timing of activities in a business process cycle. Based
on the predicted data and the measured actuals, the model error is
estimated. If the model error is larger than a user specified
tolerance, the model is re-estimated. This may involve tuning the
parameters of the current model or identifying a new model
structure, followed by estimation of model parameters. Business
process experts may be involved in the decision of when a model
needs to be re-estimated, in the choice of new model structures and
in deciding the parameters for the process model. The "model
update" step 51 is performed at a pre-specified frequency.
Alternatively, this can be triggered automatically based on certain
business rules or can also be triggered manually by participants
performing certain business process roles.
[0030] The "determine filter criteria" step 52 can be executed in
many ways, including manual input of filter criteria. We describe
one way below. Past monitored data that was filtered to determine
input data for business process execution is compared with actual
data from business process measurements using the business process
model to estimate the "ideal" filtered data that should have been
used to determine business process execution. The actual filtered
data that was used in the past is compared with the "ideal"
filtered data to estimate the filter error. If the filter error is
larger than a user specified tolerance, the filter policies need to
be re- estimated. This is done following these steps:
[0031] Identify a set of filter policies. This invention is not
limited by the specific details of a filter policy. This can take
several forms including business rules and statistical
algorithms.
[0032] Run the predictive model for each model for each of these
filter policies and determine the business process outputs.
[0033] Examine the predicted outputs to identify the most
appropriate filter policy.
[0034] The "determine filter criteria" step 52 could optionally
include an optimization procedure that is used to optimize the
filter criteria based on specified business objectives and
constraints. The "determine filter criteria" step 52 is performed
at a pre-specified frequency. Alternatively, this can be triggered
automatically based on certain business rules or can also be
triggered manually by participants performing certain business
process roles. The filter criteria may further be reviewed and
revised by business process experts.
[0035] The "filter event" step 53 filters the events and/or
monitored data by executing the filter policies determined in the
"determine filter criteria" step 52 described above. This can be
manifested in an information filter that allows some monitored data
to pass through to elicit some business response and other data to
be rejected, so as not to elicit any business response.
[0036] As a specific example, we consider a simple two-level supply
chain that consists of one manufacturer and one supplier, as
generally depicted in FIG. 6. The manufacturer 61 makes and sells
one product, the raw materials for which are obtained from the
supplier 62. The manufacturer 61 forecasts demands 63 for a
specific time horizon, which forms the basis for the manufacturer's
production planning process. The production plan is used to drive
the Materials Requirements Planning process to generate supplier
requirements. The manufacturer 61 shares supplier requirements,
along with demand forecasts and production plans, with the supplier
62. This information acts as the basis for the supplier to plan
production.
[0037] These data inputs to the supplier 62 undergo constant churn
in response to changes in supply-demand balance at the manufacturer
61. For example, the manufacturer production unit might suffer an
unplanned outage or there can be a sudden shift in the demand. This
triggers changes in the supplier data inputs very frequently. At
the manufacturer's end, demand is constantly changing, as customers
can place new orders or cancel existing orders. Supply commitments
also change based on changes in the suppliers plan. Responding to
every event from business process execution might result in
repeated changes, sometimes more than once. On the other hand, not
processing some events might lead to unintended and potentially
undesirable consequences in business performance and in fact,
defeats the whole purpose of Business Activity Monitoring. As
visibility to these business process information is enabled by
Business Activity Monitoring and Management systems, how should the
consumers of this information respond? This invention provides the
capability to Business Activity Monitoring systems to enable users
to intelligently respond to real-time or near real-time changes in
monitored information.
[0038] We describe here a specific method for monitoring changes in
demand, developing a demand model, observing and predicting supply
chain performance and choosing a particular filter policy. This
invention is by no means limited by the details of this specific
method.
[0039] External demand for the single item occurs at the
manufacturer. The underlying demand process for the item is modeled
as an independent normal variable with unknown trend, t, as defined
below:
D.sub.t=.mu..sub.t+.epsilon..sub.t
[0040] Here D.sub.t is the observed demand at time period t, .mu.,
is the underlying demand trend and .epsilon..sub.t is the random
shock on demand. One way to model random demand shocks is to model
et as an independent and identically distributed random variable
(otherwise referred to as an i.i.d. variable), with mean 0 and
variance .sigma..sub..epsilon..sup.2. The statistics of
.epsilon..sub.t can alternatively be estimated based on historical
demand information. Each site reviews its inventory level and
replenishes its inventory from an upstream site every period. The
replenishment lead times from the supplier's supplier to the
supplier, and from the supplier to the manufacturer, are in
constant periods and denoted by K and L, respectively.
[0041] First, the timing of events for the manufacturer's ordering
process is the following: (1) at the beginning of period t, the
manufacturer places an order, O.sub.t, to the supplier; (2) Next,
the goods ordered L periods ago arrive. (3) Finally, demand is
realized, and the available inventory is used to meet the demand.
Excess demand is backlogged, and a penalty cost is charged on
shortfall demand if stock-out occurs. Let h, p denote the unit
inventory holding cost and unit stock-out penalty cost accounted at
the manufacturer, respectively.
[0042] Next, the supplier handles his ordering process as follows:
(1) before the beginning of period t, the goods ordered K periods
ago arrive. (2) At the beginning of period t, the supplier receives
and ships the required order quantity O.sub.t to the manufacturer.
If the supplier does not have enough stock to fill this order, then
we assume that the supplier will meet the shortfall by obtaining
some units from an "alternative" source, with additional cost
representing the penalty cost to this shortfall. Thus, the
inventory system at the supplier resembles a system with back
orders, and the supplier guarantees supply to the manufacturer. (3)
Supplier reviews his inventory level and places an order, R.sub.t,
to his external supplier. Let H, P denote the unit inventory
holding cost and unit stock-out penalty cost assessed at the
supplier site, respectively. The manufacturer adopts the m-period
modified order-up-to policy and the supplier uses the base case
order-up-to policy (forecasts fully updated every period). The
supplier's external supplier is perfectly reliable. This invention
is by no means limited to the details of this particular business
process.
[0043] When there is no information sharing, the supplier 62
receives only information about the retailer's order quantity
O.sub.t. Therefore, the supplier 62 treats the order quantity
O.sub.t from the manufacturer 61 as an independent normal random
variable. Also, the supplier 62 has his own forecast for the
underlying trend of orders from the manufacturer. We can show
that
E(O.sub.t)=.mu..sub.t+L
[0044] Let G.sub.t,s be the supplier's forecast at period t for the
unknown trend of orders that the manufacturer will place at period
s, with t.ltoreq.s, we assume the supplier's forecast process
evolves as follows:
G.sub.t,s=.mu..sub.s+L+.eta..sub.t,s, for t.ltoreq.s
[0045] where .eta..sub.t,s is an i.i.d. normal variable with mean 0
and variance .tau..sub.s-t.sup.2. We assume .eta..sub.t,s is
independent of actual order quantity O.sub.s.
[0046] Therefore, the optimal order-up-to level (T.sub.t) for
supplier is: 1 T t = s = t + 1 t + K G t , s + Z . e where , e = K
. Var ( O t ) + i = 1 K i 2 Z = - 1 ( P / ( P + H ) )
[0047] where .PHI.(.) is the cumulative standard normal
distribution and Var(O.sub.1) is defined as:
Var(O.sub.1)=E{Var(O.sub.1.vertline.I.sub.1)}+Var{E(O.sub.1.vertline.I.sub-
.1)}
[0048] These variances can be calculated based on different demand
models.
[0049] Under this model framework, we have the manufacturer's long
run average cost is, 2 C M = ( h + p ) ( z ) 1 m i = 0 m - 1 e ,
i
[0050] where .phi.(.) is the standard normal density function and
z, and .sigma..sub.e.i are defined as: 3 z = - 1 ( p / ( p + h ) )
e , i 2 = ( L + 1 ) 2 i = i L k 2 + i . L 2
[0051] And the supplier's long run average cost is,
C.sub.s=(H+P)..phi.(Z)..tau..sub.e
[0052] where, Z and e are defined as above.
[0053] It is easy to show that CM is increasing in m, and CS is
decreasing in m. Therefore, there exists an optimal m*,
1<m*<.infin., such that the total supply chain cost is
minimized. Therefore, when there is no information sharing between
the supply chain members, the total supply chain cost performance
will improve as the downstream member updates his inventory target
level less frequently. And there exists an optimal updating
frequency to minimize the total supply chain cost performance.
[0054] FIG. 7 shows the procedures implemented by Stabilizer 18 of
the Business Activity Monitoring and Management system for the
Manufacturer and Supplier in this specific example. In the "model
update" step 71, the parameters of the demand model and supply
model are updated. The frequency of this update can either be
specified by the user or the update can be triggered based on
business rules for the events. Typically, this frequency has to be
much larger than the typical planning time scales. In the
"determine filter criteria" step 72, the performance of the updated
model is evaluated for different inventory update frequencies. The
performance evaluation is based on overall supply chain costs
estimated by the model. The filter execution in this simple case is
basically to update inventory policy based on the frequency
determined in the "model update" step 71.
[0055] FIG. 8 shows simulation results that confirm the existence
of an optimal response frequency (m=5) for this two echelon supply
chain model.
[0056] While the invention has been described in terms of a single
preferred embodiment, those skilled in the art will recognize that
the invention can be practiced with modification within the spirit
and scope of the appended claims.
* * * * *