US 20060247939 A1 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.
Claims(13) 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 3. A method according to 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 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 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 9. The method according to 10. The method according to 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 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 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. 1. Field of the Invention 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. 2. Background Description 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. 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. 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. 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: 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. Referring now to the drawings, and more particularly to 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 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 As shown in 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 These data inputs to the supplier 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 T T T T D=Demand O=Orders NS=Net Stock=(Excess Inventory On-hand−Backlogs) DNS=Desired Net Stock=Safety Stock=α×FD, α positive ENS=Net Stock Error=(DNS−NS) WIP=Pipeline Orders DWIP=Desired Pipeline=Lead Time Demand=T EWIP=Pipeline Error=(DWIP−WIP) 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, 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. -
- Objective Function for capturing trade-off between responsiveness and volatility in the system
Min{c_{h}*max(NS,0)+c_{b}max(−NS,0)+p*dev(O)} where c_{h}, c_{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). - Control Policies
- Proportional Control (P-Control): (T
_{n}=1 implies Order Up To Base stock policy that is commonly used in inventory management literature).$u\left(t\right)=\frac{e\left(t\right)}{{T}_{n}}$ - Proportional Integral Control (PI-Control):
$u\left(t\right)=\frac{1}{{T}_{n}}\left[e\left(t\right)+\frac{\sum _{i}e\left(t\right)}{{T}_{i}}\right]$ - Proportional Derivative Control (PD-Control):
$u\left(t\right)=\frac{1}{{T}_{n}}\left[e\left(t\right)+{T}_{d}\left(e\left(t\right)-e\left(t-1\right)\right)\right]$ - Proportional Integral Derivative Control (PID-Control):
$u\left(t\right)=\frac{1}{{T}_{n}}\left[e\left(t\right)+\frac{\sum _{i}e\left(t\right)}{{T}_{i}}+{T}_{d}\left(e\left(t\right)-e\left(t-1\right)\right)\right]$ Now, we can define the governing equations for PID Control using z-transforms:$\mathrm{FD}=\frac{\mathrm{zD}}{z+{T}_{a}\left(-1+z\right)}$ $\mathrm{NS}=\frac{z}{-1+z}\left[\frac{O}{{z}^{{T}_{p}+1}}-D\right]$ $\mathrm{WIP}=\left[\frac{O}{-1+z}-\frac{O}{{z}^{{T}_{p}}\left(-1+z\right)}\right]$ $O=\left[\mathrm{FD}\right]+\left[\left(\frac{1}{{T}_{n}}+\frac{z}{{T}_{n}{T}_{i}\left(-1+z\right)}+\frac{{T}_{d}\left(-1+z\right)}{{T}_{n}z}\right)\left(\mathrm{ENS}+\mathrm{EWIP}\right)\right]$
- Proportional Control (P-Control): (T
- Objective Function for capturing trade-off between responsiveness and volatility in the system
Given these above equations for PID control, the various other control policies can be obtained by setting the control policy parameters accordingly. - T
_{n}=1, T_{i}=∞, and T_{d}=0 implies Order-Up-To Policy - T
_{i}=∞ and T_{d}=0 implies P-Control - T
_{d}0=implies PI-Control - T
_{i}=∞ implies PD-Control - All non-zero and less than infinity implies PID-Control
The transfer function for the orders as a function of the demand for PID-Control is given below.
where:
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. -
- For P-Control, we find that the roots of the equation are (T
_{a}/(T_{a}+1)) and (−½). 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_{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. - For PD-Control, we find that the roots of the equation are (T
_{a}/(T_{a}+1)) and$\left(\frac{1}{2}-\frac{1+{T}_{d}}{2{T}_{n}}\right)\pm \left(\frac{1}{2}\sqrt{{\left(1-\frac{1+{T}_{d}}{{T}_{n}}\right)}^{2}+4\frac{{T}_{d}}{{T}_{n}}}\right)$ - By setting T
_{n}=3, T_{d}=0.2, we get the roots to be (T_{a}/(T_{a}+1)), 0.728 and −0.228, which means that the system is stable for such a parameter choice. - For PI-Control, we find that the roots of the equation are (T
_{a}/(T_{a}+1)) and$\left(1-\frac{1+{T}_{i}}{2{T}_{i}{T}_{n}}\right)\pm \left(\frac{1}{2}\sqrt{{\left(2-\frac{1+{T}_{i}}{{T}_{i}{T}_{n}}\right)}^{2}-4\left(1-\frac{1}{{T}_{n}}\right)}\right)$ - By setting T
_{n}=3, T_{i}=10, we get the roots to be (T_{a}/(T_{a}+1)), 0.833 and 0.8, which means that the system is stable for such a parameter choice.
- For P-Control, we find that the roots of the equation are (T
Thus, we can attain stability from a control theory perspective by carefully setting the control policy parameters. As an example, 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 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 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. 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. In addition to the environment in 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. Referenced by
Classifications
Legal Events
Rotate |