U.S. patent application number 11/117405 was filed with the patent office on 2006-11-02 for method and apparatus combining control theory and business performance management.
Invention is credited to Lianjun An, Bala Ramachandran, Karthik Sourirajan.
Application Number | 20060247939 11/117405 |
Document ID | / |
Family ID | 37235578 |
Filed Date | 2006-11-02 |
United States Patent
Application |
20060247939 |
Kind Code |
A1 |
An; Lianjun ; et
al. |
November 2, 2006 |
Method and apparatus combining control theory and business
performance management
Abstract
A control methodology and component in Business Performance
Management (BPM) Systems. This enables firms to exploit control
theoretic techniques for Business Performance Management.
Information from BPM systems is used to calibrate models of the
business process. This model is then used to assess and optimize
control actions to manage business performance, on the basis of
which a control action is selected for business process
execution.
Inventors: |
An; Lianjun; (Yorktown
Heights, NY) ; Ramachandran; Bala; (Harrison, NY)
; Sourirajan; Karthik; (West Lafayette, IN) |
Correspondence
Address: |
WHITHAM, CURTIS & CHRISTOFFERSON, P.C.
11491 SUNSET HILLS ROAD, SUITE 340
RESTON
VA
20190
US
|
Family ID: |
37235578 |
Appl. No.: |
11/117405 |
Filed: |
April 29, 2005 |
Current U.S.
Class: |
705/7.38 ;
705/7.37 |
Current CPC
Class: |
G06Q 10/06375 20130101;
G06Q 99/00 20130101; G06Q 10/06315 20130101; G06Q 10/0639
20130101 |
Class at
Publication: |
705/001 ;
705/009; 705/010 |
International
Class: |
G06Q 99/00 20060101
G06Q099/00; G06F 15/02 20060101 G06F015/02; G07G 1/00 20060101
G07G001/00; G06F 9/46 20060101 G06F009/46 |
Claims
1. A method for analyzing data from Business Performance management
systems and determining an action to manage Business Process
Performance comprising the steps of: developing, updating and
calibrating business performance models based on business process
data, defining business objectives based on desired business
performance measures, analyzing control policies by using the
business performance model to predict future business process
performance for different control policies, selecting an optimal
control policy based on business objective, and deploying actions
based on the optimal control policy.
2. A method according to claim 1, wherein the control policy is
chosen adaptively based on the current business environment and the
desired business performance metrics.
3. A method according to claim 1, wherein the control policy is
chosen based on tradeoff analysis done using business guidelines on
different performance objectives.
4. A computer implemented method for analyzing data from Business
Performance Management systems and determining an action to manage
Business Process Performance comprising the steps of: developing,
updating and calibrating business performance models based on
business process data, defining business objectives based on
desired business performance measures, analyzing control policies
by using the business performance model to predict future business
process performance for different control policies, selecting an
optimal control policy, and deploying actions based on the optimal
control policy.
5. A signal-bearing medium tangibly embodying a program of machine
readable instructions executable by a digital processing apparatus
to perform a method for analyzing data from Business Performance
Management systems and determining an action to manage Business
Process Performance comprising the steps of: developing, updating
and calibrating business performance models based on business
process data, defining business objectives based on desired
business performance measures, analyzing control policies by using
the business performance model to predict future business process
performance for different control policies, selecting an optimal
control policy, and deploying actions based on the optimal control
policy.
6. A method according to claim 1, wherein data from Supply Chain
Management systems are analyzed and an action to manage Supply
Chain Performance is determined further comprising the steps of:
developing, updating and calibrating supply chain models based on
supply chain data, defining business objectives based on desired
business performance measures, analyzing control policies for
placing replenishment orders by using the supply chain model to
predict future supply chain performance for different control
policies, selecting an optimal control policy, and placing supply
chain orders based on the optimal control policy.
7. A computer-implemented method according to claim 6, wherein data
from Supply Chain Management systems is analyzed and an action to
manage Supply Chain Performance is determined, further comprising
the steps of: developing, updating and calibrating supply chain
models based on supply chain data, defining business objectives
based on desired business performance measures, analyzing control
policies for placing replenishment orders by using the supply chain
model to predict future supply chain performance for different
control policies, selecting an optimal control policy, and placing
supply chain orders based on the optimal control policy.
8. The method according to claim 4, including a step of measuring a
difference between a desired performance value and a current
monitored value, a cumulated value of all such differences along a
given timeline, and a transient changing rate of the differences
for the business performance metrics in order to choose a proper
combination of adjustments on three types of errors to realize
optimal feedback control.
9. The method according to claim 8, wherein the combination is
formulated as a business objective to be controlled and managed in
the Business Performance Management system, further comprising the
step of adjusting a chosen combination based on the changing
objective.
10. The method according to claim 4, including a step to monitor
business environment and constantly update optimal policy,
comprising the steps of: identifying exogenous variables in the
model corresponding to the business environment, including a
pricing model change, and updating an objective definition
corresponding to the change.
11. A method according to claim 4, wherein the steps are
implemented in a controller as a component in a business
performance management system.
12. A system for control and management of business performance
comprising: (a) an open loop component for business performance
management consisting of at least one of specifying business
performance objectives and constraints and specifying business
stability requirements, (b) a closed loop component which closes
around the said open loop component that consists of identifying
control variables for managing business performance, estimating
business performance using state information from business
performance management systems, identifying best control action
based on the business state information, selected at least from the
class of control algorithms including proportional control,
proportional integral control, proportional integral derivative
control, adaptive control, model predictive control, general
control algorithms, and implementing the control action using the
business performance management system.
13. A computer based system for analyzing data from Business
Performance management systems and determining an action to manage
Business Process Performance comprising: input means for receiving
one or more set points for metrics and outputs generated by a
Business Performance Management (BPM) system based on the events
and metrics that are processed by the BPM system and producing a
differential metric output; a controller receiving said
differential metric output and a business objective based on
desired business performance measures and developing, updating and
calibrating business performance models based on business process
data, said controller analyzing control policies by using the
business performance model to predict future business process
performance for different control policies and selecting an optimal
control policy based on business objective; a business process
execution means deploying actions selected by said controller based
on the optimal control policy; and means measuring events and
metrics generated as a result of deploying actions by the business
process execution means, which measured events and metrics are
processed by the BPM system to generate feedback to the input
means.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The instant application is related to copending U.S. patent
application entitled "Method for Managing and Controlling Stability
in Business Activity Monitoring and Management Systems", Ser. No.
10/843,451 filed May 12, 2004, by B. Ramachandran et al.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention generally relates to management of
business performance and, more particularly, to a methodology and
apparatus for combining control theory with Business Performance
Management.
[0004] 2. Background Description
[0005] Business Performance Management is a key emerging technology
positioned to enable optimization of business operations and
information technology (IT) infrastructure, so as to achieve
dynamic business performance targets. This is done by continually
monitoring and optimizing business processes, not just during
business process design, but also after the process has been
deployed. Hence, there is a need for developing capabilities that
enable the control and dynamic management of business process
performance. These capabilities should be adaptable to changing
conditions in the business process environment and to uncertainties
in the various business process attributes.
SUMMARY OF THE INVENTION
[0006] It is therefore an object of the present invention to
provide a method and apparatus to achieve optimal business process
performance, by utilizing control theoretic principles and
algorithms that adaptively determine the attributes of the actions
taken to manage the business process.
[0007] Business Performance Management aims at creating a culture
of continuous performance improvement by modeling, deploying,
monitoring and managing business solutions. This invention enables
that by the use of control theory based algorithms to optimize the
business actions. It uses the notion of business process targets
and business process levers. Further, it determines the optimal
setting for the business process levers to meet business process
targets and dynamically manage the process performance.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] 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:
[0009] FIG. 1 is a block diagram showing a representative process
for using Business Performance Management systems.
[0010] FIG. 2 is a block diagram showing a representative process
for using Business Performance Management systems combined with
control theory, according to the present invention.
[0011] FIG. 3 is a block diagram showing some key components of
Business Performance Management systems shown in FIG. 2.
[0012] FIG. 4 is a block diagram showing the essential components
involved in combining control theory with Business Performance
Management systems.
[0013] FIG. 5 is a flow diagram showing a high level description of
the procedures that are implemented when combining control theory
with Business Performance Management systems.
[0014] FIG. 6 is a pictorial representation showing a two-stage
supply chain that is used as a scenario in the embodiment to
illustrate the combination of control theory with Business
Performance Management systems according to the present
invention.
[0015] FIG. 7 is a block diagram showing the essential components
involved in combining control theory with Business Performance
Management systems for the Supply Chain Management scenario.
[0016] FIG. 8 is a flow diagram showing a high level description of
the procedures that are implemented when combining control theory
with Business Performance Management systems for the Supply Chain
Management scenario.
[0017] FIG. 9 is a graph showing inventory-backlog difference
profile for a bad choice of the Control parameter for the Supply
Chain Monitoring & Management scenario.
[0018] FIG. 10 is a graph showing inventory-backlog difference
profile for a good choice of the Control parameter for the Supply
Chain Monitoring & Management scenario.
[0019] FIG. 11 is a histogram illustrating potential improvements
from adaptively choosing control policies for the Supply Chain
scenario.
[0020] FIG. 12 is a block diagram of the environment and
configuration of a computer system for implementing the present
invention.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION
[0021] In the following description, we assume the existence of a
Business Performance Management system that probes different
enterprise events, monitors different enterprise performance
indicators and assists in the management of Business Performance.
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
Performance 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. This
invention is not limited by the specific details of Business
Process execution, including use of workflow engines. Further, this
invention is not limited to the type of business process, business
process targets, business process levers and business process
inputs.
[0022] Referring now to the drawings, and more particularly to FIG.
1, there is shown a representative process for using Business
Performance Management systems. The Business Process Execution 110
is managed by a Business Performance Management (BPM) system 120.
Based on the events and metrics that are processed by the BPM
system, the Business process inputs are modified 130 for BP
execution. We alternatively refer to Business Process inputs as
Business Process levers, as they can be changed to modify and
manage the Business Process performance. This feedback loop is
fundamental to the value proposition of BPM systems. Typically a
process involving people determines the Business process inputs.
Although people can apply judgment in determining the business
process inputs, this typically results in a sub-optimal
performance.
[0023] A novel element of this invention is in the combination of
control theory with Business Performance Management systems to
determine the inputs for Business Process Execution, as shown in
FIG. 2. We use the notion of Business Process targets to define a
set point for Business Performance metrics. A Business Performance
Controller 235 adaptively analyzes different control algorithms and
recommends a control action to modify Business Process inputs for
BP execution, either in real-time or on an ongoing basis, as is
appropriate in the context of a specific business process. The
control action may be either taken manually or automatically. This
is a known concept in control theory and has been applied in
several practical situations, such as chemical process control
(see, for example, Process Control by Coughanowr and Koppel,
McGraw-Hill Publishers). This invention proposes the use of a
controller system component in Business Performance Management
systems.
[0024] Control theory is a well-developed field used in prior art
in several practical situations, such as chemical process control.
This invention proposes the use of a controller system component in
Business Performance Management (BPM) systems. BPM systems refer to
a broad range of systems that are designed to help manage business
performance. In order to further clarify the scope of this
invention from prior art, the important components of BPM systems
are illustrated in FIG. 3. Business processes 310 generate events
that are sent to a Common Event Infrastructure 320. These events
are then correlated at 330 to identify situations of interest (both
business and IT situations). The event information is stored in a
data warehouse 340 that may be queried by different types of users
through portals to monitor and manage business performance 350. It
is assumed that users will take appropriate actions based on the
queried information to better manage business performance. This
invention enables the use of control algorithms on the business
performance information to identify appropriate modification of
inputs for business process execution.
[0025] As shown in FIG. 4, there are several steps involved in
using control algorithms for Business Performance Management. These
steps are further explained in the flow diagram in FIG. 5. First,
the underlying business performance models are created and updated
(410). The model is based on historical data on business
performance metrics and control actions, using which the
performance can be predicted. This prediction can be compared with
the observed business performance metrics to estimate the model
error. If the model error exceeds a user specified threshold, the
model is recalibrated. The optimal control action is then
determined 430 by analyzing the impact of different alternative
control actions 420. This is done in two steps--first by selecting
a control policy (such as Proportional-Integral Control) and then
by selecting the parameters governing the control policy. Business
guidelines on different business performance objectives are used to
guide the selection of an appropriate control action. This
invention is not limited by the choice of business metrics or the
specifics of a control policy or algorithm.
[0026] We describe here a specific embodiment of the invention
combining control theory and Business Performance Management using
a simple example of business performance management in supply
chains. A schematic of the supply chain scenario, consisting of a
simple two-level supply chain that consists of one manufacturer and
one supplier, is depicted in FIG. 6. The manufacturer 620 makes and
sells one product, the raw materials for which are obtained from
the supplier 630. The manufacturer 620 forecasts demands 610 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 620 places orders to the supplier
630 in each period. This order information acts as the basis for
the supplier to plan production.
[0027] These data inputs to the supplier 630 undergo constant chum
in response to changes in supply-demand balance at the manufacturer
620. 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 modify/cancel existing orders. Supply
commitments also change based on changes in the suppliers plan. In
this example, the business performance metrics are inventory costs
and customer service levels (as measured by the backorder costs).
We assume that the manufacturer uses a Business Performance system
to manage the performance of the supply chain. This can be
optionally linked to business domain specific applications, i.e.
supply chain applications in this case. In this embodiment, we
describe how supply chain ordering policies can be determined based
on adaptive use of control theoretic policies to optimize business
performance metrics under changing forecast scenarios.
[0028] FIG. 7 shows a block diagram that illustrates how control
theory can be combined with Business Performance Management for
this scenario. The supply chain execution 710 is managed by BPM
systems 720 tailored for supply chain management. The observed
inventory and service levels are compared against specified targets
to estimate deviations in business performance. Different
replenishment policies are analyzed based on their overall business
performance impact by the controller 735. On this basis, a
replenishment policy is selected and orders are placed for supply
chain execution 730. There are several research papers exploring
specific control policies for replenishment orders for a given
scenario (see, for example, Dejonckheere J, Disney S M, Lambrecht M
R, Towill D R, "Measuring and avoiding the bullwhip effect: a
control theoretic approach", European Journal of Operational
Research, vol. 147, no. 3, 2003). FIG. 8 provides a flow diagram
that details this further. First, the underlying models predicting
inventory and service levels are updated, if appropriate. Then, the
optimal control policy is determined, by using the model to predict
future business performance for different control policies and
different settings of parameters that govern the control policy.
The control policy thus determined is then executed; this may
involve placing replenishment orders to the suppliers using the
appropriate delivery channels.
[0029] In order to analyze different control methods further in the
context of this scenario, we make some assumptions. This invention
is by no means limited by these assumptions, rather, these allow us
to formulate a specific model and perform analyses of different
control policies. We assume that the demand forecasts (FD) are
determined using an exponential smoothing method, governed by the
parameter T.sub.a. Let us now put down some notations for further
analysis.
[0030] T.sub.p=Lead Time between placing orders and receiving
them
[0031] T.sub.n=Proportional control parameter
[0032] T.sub.d=Derivative control parameter
[0033] T.sub.i=Integral control parameter
[0034] D=Demand
[0035] O=Orders
[0036] NS=Net Stock=(Excess Inventory On-hand-Backlogs)
[0037] DNS=Desired Net Stock=Safety Stock=.alpha..times.FD, .alpha.
positive
[0038] ENS=Net Stock Error=(DNS-NS)
[0039] WIP=Pipeline Orders
[0040] DWIP=Desired Pipeline=Lead Time Demand=T.sub.p.times.FD
[0041] EWIP=Pipeline Error=(DWIP-WIP)
[0042] The aim of a control policy is, given a deviation from
desired state at time t, e(t), it determines the adjustment, u(t),
that needs to be made to the business process levers in order to
bring the system to the desired state. At the same time we want to
optimize a defined objective (such as total cost evaluated as the
sum of inventory and ordering costs) that captures the desired
business metrics. Some examples of common control policies are
Proportional Control, Proportional Integral Control, Proportional
Derivative Control and Proportional Integral Derivative Control
(see any textbook on control theory for a detailed discussion of
this and other control policies--for example, K. Ogata, Modern
Control Engineering, Prentice Hall, 2001.)
[0043] We will now define the objective function and the control
policies used in the preferred embodiment. The methodology below
can be extended to any desired combination of business metrics and
control policies. [0044] Objective Function for capturing trade-off
between responsiveness and volatility in the system
Min{c.sub.h*max(NS,0)+c.sub.bmax(-NS,0)+p*dev(O)} where c.sub.h,
c.sub.b and p are the holding cost, the backlog cost and penalty
for deviation of orders respectively. While the inventory and
backlog costs measure the responsiveness of the system to the
customer's demands, costing the deviation of the orders helps
measure the volatility in the system or the Bullwhip effect (see
Lee et al., "Information Distortion in a Supply Chain: the Bullwhip
effect", Management Science, Vol. 43, No. 4, 1997). [0045] Control
Policies [0046] Proportional Control (P-Control): (T.sub.n=1
implies Order Up To Base stock policy that is commonly used in
inventory management literature). u .function. ( t ) = e .function.
( t ) T n ##EQU1## [0047] Proportional Integral Control
(PI-Control): u .function. ( t ) = 1 T n [ e .function. ( t ) + i
.times. e .function. ( t ) T i ] ##EQU2## [0048] Proportional
Derivative Control (PD-Control): u .function. ( t ) = 1 T n
.function. [ e .function. ( t ) + T d .function. ( e .function. ( t
) - e .function. ( t - 1 ) ) ] ##EQU3## [0049] Proportional
Integral Derivative Control (PID-Control): u .function. ( t ) = 1 T
n [ e .function. ( t ) + i .times. e .function. ( t ) T i + T d
.function. ( e .function. ( t ) - e .function. ( t - 1 ) ) ]
##EQU4## Now, we can define the governing equations for PID Control
using z-transforms: FD = zD z + T a .function. ( - 1 + z ) ##EQU5##
NS = z - 1 + z .function. [ O z T p + 1 - D ] ##EQU5.2## WIP = [ O
- 1 + z - O z T p .function. ( - 1 + z ) ] ##EQU5.3## O = [ FD ] +
[ ( 1 T n + z T n .times. T i .function. ( - 1 + z ) + T d
.function. ( - 1 + z ) T n .times. z ) .times. ( ENS + EWIP ) ]
##EQU5.4##
[0050] Given these above equations for PID control, the various
other control policies can be obtained by setting the control
policy parameters accordingly. [0051] T.sub.n=1, T.sub.i=.infin.,
and T.sub.d=0 implies Order-Up-To Policy [0052] T.sub.i=.infin. and
T.sub.d=0 implies P-Control [0053] T.sub.d0=implies PI-Control
[0054] T.sub.i=.infin. implies PD-Control [0055] All non-zero and
less than infinity implies PID-Control
[0056] The transfer function for the orders as a function of the
demand for PID-Control is given below. O D = z .function. ( Az 3 +
Bz 2 + Cz + D ) ( X + Yz ) .times. ( Pz 3 + Qz 2 + Rz + S )
##EQU6##
[0057] where:
A=(1+T.sub.i+T.sub.dT.sub.i)(T.sub.p+T.sub.a+1+.alpha.)+T.sub.nT.sub.i
B=-[(T.sub.i+2T.sub.dT.sub.i)(T.sub.p+T.sub.a+1+.alpha.)+(1+T.sub.i+T.sub-
.dT.sub.i)(T.sub.p+T.sub.a+.alpha.)+2T.sub.nT.sub.i]
C=T.sub.dT.sub.i(T.sub.p+T.sub.a+1+.alpha.)+(T.sub.i+2T.sub.dT.sub.i)(T.s-
ub.a+T.sub.p+.alpha.)+T.sub.nT.sub.i
D=-T.sub.dT.sub.i(T.sub.p+T.sub.a+1) X=-T.sub.a Y=1+T.sub.a
P=T.sub.nT.sub.i Q=1+T.sub.i+T.sub.iT.sub.d-2T.sub.nT.sub.i
R=-[T.sub.i+2T.sub.iT.sub.d-T.sub.nT.sub.i] S=T.sub.dT.sub.i
[0058] We know from control theory literature that the roots of the
characteristic equation should lie within the unit circle in the
complex plane for the control system to be stable. The denominator
of the transfer function gives the roots for the characteristic
equation for the control system. It is important to note that such
stability from a control theoretic perspective is a minimum
requirement. However, it does not tell us anything about the
volatility arising from Bullwhip effect, which is captured by the
objective function defined earlier. We will now discuss the control
theoretic stability properties of the various control policies.
[0059] For P-Control, we find that the roots of the equation are
(T.sub.a/(T.sub.a+1)) and (-1/2). Both these roots lie within the
unit circle and hence the system is stable from a control theory
perspective. We can see that this results in the OUT policy also
being stable from a control theory perspective (as OUT policy can
be obtained by setting T.sub.n=1). But we know that OUT policy
results in bullwhip. So, we are interested in both the system being
stable from a control theory perspective and also one that has the
least bullwhip. [0060] For PD-Control, we find that the roots of
the equation are (T.sub.a/(T.sub.a+1)) and ( 1 2 - 1 + T d 2
.times. T n ) .+-. ( 1 2 .times. ( 1 - 1 + T d T n ) 2 + 4 .times.
T d T n ) ##EQU7## [0061] By setting T.sub.n=3, T.sub.d=0.2, we get
the roots to be (T.sub.a/(T.sub.a+1)), 0.728 and -0.228, which
means that the system is stable for such a parameter choice. [0062]
For PI-Control, we find that the roots of the equation are
(T.sub.a/(T.sub.a+1)) and ( 1 - 1 + T i 2 .times. T i .times. T n )
.+-. ( 1 2 .times. ( 2 - 1 + T i T i .times. T n ) 2 - 4 .times. (
1 - 1 T n ) ) ##EQU8## [0063] By setting T.sub.n=3, T.sub.i=10, we
get the roots to be (T.sub.a/(T.sub.a+1)), 0.833 and 0.8, which
means that the system is stable for such a parameter choice.
[0064] Thus, we can attain stability from a control theory
perspective by carefully setting the control policy parameters. As
an example, FIG. 9 illustrates instability with a PI controller
when the integral control parameter is chosen wrongly. We can see
from FIG. 9 that the system inventory oscillates and diverges
leading to instability. FIG. 10 illustrates how the control
parameter can be tuned to produce stable behavior. It can be noted
from FIG. 10 that the inventory is always within desirable limits.
(Note that the scale for FIG. 10 is much smaller than FIG. 9.) The
above examples show that we can select the control policy
parameters such that we can get demand smoothing and also have
stability from a control system perspective. A similar result
applies for PID-Control as well. We can use simulation to examine
the cost implications and measure the trade-off between
responsiveness to demand and system volatility. The overall
objective is to select the optimal control policy.
[0065] It was found that Proportional Control smoothens the
ordering process and the flow across the system. This type of
control reaps benefit by reducing the bullwhip, but increases the
inventory and backorder costs. We find that the volatility in such
a system is lesser than that obtained by combining information
sharing with traditional Order Up To (OUT) policies but, the
responsiveness (as determined by the inventory and backorder costs
and hence the service levels) is worse. We need to choose the
P-Control parameter, T.sub.n, to find the appropriate trade-off
between responsiveness and volatility. The usefulness and parameter
choice for P-Control depends on both the forecast error and bias.
Our simulation results indicate that a high T.sub.n value results
in better business performance for cases of high forecast errors.
In the case of forecast bias, we need to choose low T.sub.n values,
but still the performance is not good enough in the presence of
bias.
[0066] Derivative control adds prediction by looking at the change
in the error values. We get better response than using just
P-Control as the derivative control predicts error changes earlier
and better. However, the volatility in the system is increased
since derivative control is highly sensitive when it comes to
reaction to noise in the system. The usefulness and choice of the
derivative control parameter, T.sub.d, depends on the forecast
error. From our simulation results, we find that when the forecast
error is low/medium, derivative control gives a good result in
terms of maximizing the gain from the trade-off between
responsiveness and volatility.
[0067] Integral Control reacts more to demand trends than
proportional control. The usefulness and parameter choice for
integral control depends on the forecast bias. Integral control is
highly effective when the bias is high and the demand trends are
not captured. This is analogous to integral control being used to
remove the steady state offset in traditional process control.
Thus, integral control can be used to counter the effect of
forecast bias on the system.
[0068] We observe that there is no single universal solution that
will work well in all situations. An interesting implication of the
proposed invention is that the control policy for Business
Performance Management can be adaptively chosen based on the
business environment. In particular, for the supply chain scenario
considered in this embodiment, the control policy (such as P, PI
and PID or other policies) can be selected based on observations of
appropriate system metrics, such as forecast error. For example,
let us assume that the forecast error is constant for a period,
then increases for some period of time and then comes back to the
original level. Let us also assume that we use only P-Control for
this illustration. We can either use a high parameter value or a
low parameter value or adaptively change between the high and low
values depending on the forecast situation. To quantify the value
of adaptive control, we use the objection function defined earlier
based on desired business performance measures (a weighted
combination is needed for multi-objectives). At each time period,
the parameters are chosen by optimizing the objective. FIG. 11
illustrates potential performance improvements that can be obtained
from adaptively selecting the control policy--in particular, it
shows the percentage difference of the per-period average cost when
using different parameters and when using the parameters
adaptively, using the performance with T.sub.n=2 as a basis for the
comparison. As we can see from FIG. 11, adaptively changing the
parameters helps us achieve a better total cost and thus realize
optimal control of business process. The scope of this invention
extends to combining all control algorithms with Business
Performance Management and is not limited by the details of
particular control algorithms discussed in this embodiment.
[0069] FIG. 12 shows a typical hardware configuration of a computer
system in accordance with the invention that preferably has at
least one Central Processing Unit (CPU) 1200. The CPUs are
interconnected via a system bus 1202 to a random access memory
(RAM) 1204, read-only memory (ROM) 1206, input/output adapter 1208
(for connecting peripheral devices such as disk units and tape
drives to the bus), user interface adapter 1210 (for connecting
user devices such as keyboard, mouse, etc. to the bus),
communication adapter 1212 (for connecting the computer system to
an information network such as Internet, Intranet, etc.) and a
display adapter 1214 (for connecting the bus to a display
device).
[0070] In addition to the environment in FIG. 12, a key aspect of
this invention includes a computer-implemented method for combining
control theory and Business Performance Management. As an example,
this method may be implemented in the particular hardware
environment discussed above. The method may be implemented, for
example, by operating a computer, as embodied by a digital data
processing apparatus to execute a sequence of machine-readable
instructions. These instructions may reside in various types of
signal-bearing media such as a CD, a diskette, etc.
[0071] 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.
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