01388294

A System Dynamics Framework for Sense-and-Respond Systems

Lianjun An, J.J. Jeng, Markus Ettl, Jen-Yao Chung

IBM T.J. Watson Research Center

Yorktown, New York 10598, U.S.A.

alianjun,jjjeng,msettl,[email protected]

Abstract

Sense-and-Respond systems realize the concepts of autonomic computing at the level of business processes. One of the key requirements to build Sense-and-Respond systems is to accurately capture and model the dynamical behavior of business metrics, a.k.a. key performance indicators (KPI). System Dynamics (SD) models and the runtime engines provide means to understand both key performance indicators and the dynamic behaviors (e.g. causality) among them. In this paper, we present a system dynamics model based upon a scenario from supply chain management domain. Our purpose is to demonstrate an alternative approach of building Sense-and-Respond systems.

Specifically, we use system dynamics to formally define the KPIs of both the retail inventory and the supplier backlog. Additionally, we introduce objective functions and control variables as the optimization elements being part of the system dynamics formalism. Therefore, the decision (e.g. the order size from manufacturer to suppliers) would correspond to the optimal solution of the system with respect to the defined objective. These concepts will be explained through scenarios. The enabling reference architecture and deployment method using system dynamics are also presented in this paper. After the system dynamics models and corresponding components are deployed to the field, the whole system will manifest the Sense-and-Respond behavior in a dynamical fashion.

Keywords: system dynamics, Sense-and-Respond system, optimal control, autonomic business process

1. Introduction The Sense-and-Respond enterprise has been studied intensively since its concept was popularized by Stephan Haeckel [1]. It has a great advantage when compared with the make and offer model. A Sense-and-Respond enterprise can quickly capture market change, accurately forecast future need, and respond in a timely and optimal fashion. Based on collected

business data and the analysis results, decision maker could make appropriate strategic plans and adjusts business policies in order to respond to emergent business situations. The ultimate is that an enterprise can be operated effectively and profitably in a competitive and changing environment. Information technology provides a vehicle to realize the vision of Sense-and-Respond enterprise. The Internet connects the whole cyber world and provides immense business data. The back-office systems, e.g. ERP and CRM, are exploited to manage enterprise resources and service offerings. Web services are used to integrate business processes across organization boundaries.

However, to implement a Sense-and-Respond system is a nontrivial task. The difficulty arises from the fact that it is very hard to capture the behavior of an enterprise and its environment. It is well known that agents are aimed to model the real-world or virtual entities situated in a dynamical environment. Nevertheless, it still requires to build analyzing, reasoning, cooperating, tuning functionalities into a component and to form an active agent for the sake of building Sense-and-Respond systems. There are a few commercial products to for companies to monitor their business activities. Basically, they attempt to capture the KPIs at the different levels within an organization. Usually, they are accompanied with visual tools available to display there data in an explicit way. Business dashboard is commonly used to display business activity status to both executives and business analysts. At the back end, data integration and mining technologies are used in the area of data analysis for the sake of detecting business situations.

Beyond that, people built intelligent analyzers into the system for supporting decision making. For example, optimization algorithms are used to derive optimal solutions for specific business processes. People also use system dynamics to analyze the most relevant factors under certain objective setting. System dynamics is a methodology for studying and managing complex feedback systems, such as one finds in business and other social systems. In fact it has been used to address practically every sort of feedback

Proceedings of the IEEE International Conference on E-Commerce Technology for Dynamic E-Business (CEC-East’04) 0-7695-2206-8/04 $ 20.00 IEEE

system. In this paper, we illustrate the ideas how system dynamics can be exploited to capture the KPI behaviors in a Sense-and-Respond system. The analysis of business situations often leads to decision making and action rendering to enterprise entities. Some vendors provide business intelligence (BI) tools in this area, e.g. i2 and Vensim [6], to help business analysts to understand the dynamical behavior and make decisions. After evaluation, people make corrective decision to keep their own business on the right track and provide feedback control over their business processes.

In this paper, we investigate feedback control process and its application to the development of Sense-and-Respond systems. Especially, we argue that using system dynamics to understand dynamics and the dependencies among KPIs is very critical for an enterprise to make timely and appropriate decisions in response to unwanted situations. System dynamics helps business analysts to avoid overlooking the current situation (static value of KPI) and making bias judgment when a decision is being made. We also extend Sterman’s model [2,3] to include certain control function that is equipped with the capability of optimal control in the target dynamical system. A sequence of incremental actions would be found as solution of the optimal control problem and to optimize the predefined objective function. More importantly, that objective function includes time horizon, hence, the dynamical behavior can be captured. The sequence of actions would guarantee the sequence of state values in optimized path (trajectory) in the constrained state space. In the second part of paper, we present an architecture that can be used to support monitoring hierarchical data of KPI simultaneously (due to KPI dependency) and enabling incremental action rendering (due to action dynamics).

The organization of this paper is as follows. Section 2 presents the motivation of this work and the problems we attempt to solve. Section 3 provides formal formulation of Sense-and-Respond systems, key performance indicators and actions. Section 4 compares the Sense-and-Respond system with control system. Section 5 gives the account of the enabling architecture of the framework. Section 6 gives the relation work. Section 7 concludes this paper with future work.

2. Motivation We are facing a great challenge to design and deploy Sense-and-Respond system in the business process and to enable manageability of business process in large scope. Its success depends not only on autonomic capability of computing system but also on simulation

capability of human behavior. Especially, the system could provide high quality decision making based on up to date relevant business data. To identify business situation, we can set up monitoring system to collect the key performance indicators (KPIs) during runtime. If some KPI is above certain threshold, situation is deleted and some action might need to be taken. Since the KPI data are static, the situation should not be overlooked. In this case, the business situation is derived based on the composition of KPI values, e.g., time-sensitive correlation patterns for certain type of KPIs. However, in many cases, the dynamical perspectives of KPI values need to be taken into consideration for the sake of detecting business situations. That is, the rate change of KPI could be more important in some scenarios. For instance, when a warehouse replenishes parts to raise its inventory level based on the amount being ordered, the accumulation rate of ordered amount should be examined. At the beginning, even the total amount is not too big; but the bigger accumulation rate might be the signal for speeding up the replenishment. By the same token, the large total amount may not be the signal for speeding up the replenishment; on the contrary, if the accumulation rate closes to zero, then it is time to slow down the replenishment. The dynamical information (changes in time dimension) makes proactive action become possible. Otherwise, an inappropriate timing of taking action could cause serious problems in the supply chain network, e.g., instability. Especially, the influence could be magnified at upstream level such as supplier’s supplier (bullwhip effect).

In fact, in supply chain management, people have been developing data mining technique to abstract useful information from up to date business data. Statistical models and queuing theories have been exploited to analyze data and to forecast the demand and trend. Finally, people use optimization methods to adjust supply chain management policy (see [4]). Two value chains based on different operation mode (“build-to-stock” and “assembly-to-order”) are accounted in this paper. It is obvious that all up to date KPI (inventory level of parts in warehouses of a supply chain network in this case) values and variation in sample and time are used to forecasting the demand and figure out corrective action. In addition to the criticality of dynamical aspect of KPIs, the other important area of our framework is to catch the dependency relationships among KPIs. Some KPI could be aggregated from other KPIs. KPIs can thus form hierarchical structure among themselves. Causality relationship between KPIs provides relevant information for analyzing the business situation. For instance, amount of production


is lower than expected value because of shortage of some parts.

The system dynamics [2,3] approach addresses both aspects of KPI: dynamics and causality. We developed autonomic business process model based the concepts of system dynamics. Such model is built based on system thinking and thorough analysis of key performance indicators. A model for system dynamics includes several types of conceptual entities such as stocks and flows. Where stocks represents accumulations (level) of physical entity or knowledge and flows represent the dynamic change in the content of the stocks over time. Then stock and flow diagram is drawn to show inbound and outbound flow from stocks as well as all influence factors and causal loops. In our case, we identify being observed KPIs as stocks and use stock and flow diagram to model variables and flow rate dependency, causal relationships among them. This process ends up with a dynamical system for the observable KPIs. It is a visualized tool for system thinking. It helps us to discovery all elements of concern to see interaction in the complex system. It can be simulated using ordinary differential equation solver provided by matlab and Vensim [6]. The tool provided by Vensim also allows change setting very easily. For instance, when changing some independent parameters, the solution curve (in state and time coordinates) would changes accordingly. The simulation provides full dynamical information in time dimension.

As we argued before, it is required to capture dynamics and dependency of KPIs in Sense-and-Respond systems. System dynamics provides a formal way to carry out this modeling process and dynamic behavior in detail. It could be questioned about accuracy of the modeling and artificiality of dependency since the KPI relationships are mainly based upon subjective opinions from domain experts. However, a careful modeling procedure can minimize logical inconsistency and conflict on dependency relationships. Some value range of parameter should be determined based on statistical analysis of historical data and experiences. The bottom line is that the formalization of human understanding of the relationships among KPIs enhances the understanding of system behavior in general even the data was acquired from humans themselves. Capturing and quantifying the system behavior based on systems thinking could lead to truth to some extent, which can not be fulfilled if no model exists in any fashion. In most case, heuristic modeling and objective quantification would improve business efficiency.

System dynamics approach does provide better understanding of business situation in qualitative way.

Some simulation software, such as provided by Vensim, even helps human to determine visually what kind of action should be taken. Such understanding may be enough to make strategic and tactical planning. But that is not enough for taking accurate actions in operational action quantitatively. To compensate this shortcoming, we need to build optimal control into dynamical system obtained from system dynamics modeling. In fact, whenever some parameters could change in certain range, we could replace it by a fixed value plus a control variable. We will illustrate that in the next formulation exercise section. After formulating objection function with time dimension, we can use dynamic programming and the maximum principle to solve the problem. If we think the KPI values varies in a restrained state space, then the solution would give best trajectory in the that space in the sense that it optimizes the defined objective functions. At the same time, the control value will be resolved as a function of time. The resolved control path would correspond to incremental corrective actions.

In summary, we are attempting to resolve the following issues in this paper:

• Use systems dynamics to model Sense-and-Respond system (or autonomic business process management system);

• Develop system dynamics based model to capture the dynamics and causality among key performance indicators;

• Apply optimal control theory to develop action models that are used to guide the responsive actions of Sense-and-Respond systems in a measurable fashion.

3. System Dynamics Formalism of Supply Chain

This section presents system dynamics formalism of supply chain scenarios through problem statements, system dynamics formulation, and optimal control for dynamical systems.

3.1 Problem Statement

As a usual practice, a retailer would order certain amount of product and put them in its inventory to meet sale request from customer. From one side, not enough inventories would lose sale opportunities and could not create revenue. From the other side, two much inventories would increase cost to maintain the inventory and even takes risk that some of them couldn’t be sold out at all. It is important to establish effective replenishment policy, so that retailer could reduce inventory cost, meet market demand and


increase revenue, and reduce risk. It is not an easy task to realize such requirements. There are a lot of factors to be considered, for instance, market demand, supplier’s capabilities, and transportation condition etc. Specifically, supplier would need to take time to adjust product amount due to resource re-allocation; product delivery could be delayed due to distance and whether condition. More importantly, all factors would change along time and we lives in a dynamical world. In our study, we only consider the problem from retailer point of view. Our objective is to reduce inventory cost and to utilize sale opportunities disregard supplier’s benefits. Based on sequenced incoming sale, product delay, transit time information, the system could determine the order amount automatically, and at the same time, optimize the quantified objective. In order world, we build a Sense-and-Respond system based on the dynamical model. The action --- how much to order from suppliers at each time step --- would be based on the inputs of both last and current time steps as well as the values of predefined objective functions.

3.2 System dynamics modeling

We consider two variables as stocks in the problem. One is amount of factory order backlog (FOB) and the other is level of retailer’s inventory (RI). The retailer’s order increases the FOB and shipped out product decreases the FOB. The retailer’s sale decreases the RI and product replenishment increases the RI. The dynamical system can be written as the following.

RSRRRdt

RId

PRRORdt

FOBd

−=

−=

)(

)(

where ROR is for Retailer Order Rate and PR for Production Rate, and RR is for Replenish Rate and RSR for Retail Sale Rate. Now we model the system as stock and flow diagram [Figure 1] and describe how to express those flow rates as function of other dependent variables.

OrderBacklog

RetailInventory

RetailerOrderRate

RetailerSaleRate

TimeInTransit

AverageRetailRate

TestInput

TimeToAverageSales

OrderRateAdjuster

ProductionRate

ReplenishRate

DesiredProductionRate

TimeToAdjustProduction

TargetProductionDelay

-

-+

+

+

Figure 1. Retailer order based on market demand and running condition

In the diagram, arrow indicates casual relationship and the sign )(+ is for reinforcing effect and )(− for

Neutralizing or stabilizing effect. In our problem, the dependencies are entered as the following.

1) RSR = TestInput(TI) = 100 + STEP(20,10). (It equals 100 for time < 10 and equals 120 after that)

2) RR = DELAY FIXED(PR, TimeInTransit, PR). (the current replenish rate is same as production rate at TimeInTransit(as TIT) ago)

3) AverageRetailRate = SMOOTH(RSR, TimeToAverageSales). (AverageRetailRate is the smoothed RSR with window size TimeToAverageSales(TAS)).

4) DesiredProductionRate = FOB/TargetProductionDelay. (TargetProductionDelay as TPD)

5) ROR = AverageRetailRate + OrderRateAdjuster. (OrderRateAdjuster as ORA)

6) PR = SMOOTH(DesiredProductionRate, TimeToAdjustProduction). (the production rate is smoothed DesiredProductionRate with window size TimeToAdjustProduction(TAP))

Combining with dependencies, the system can be re-written as the following

.)),,/(()(

),,/()),(()(

TESTITITTAPTPDFOBSmoothDelaydt

RId

TAPTPDFOBSmoothORATASTESTSmoothdt

FOBd

−=

−+=

Assume that TESTI, TRD, TAS, TAP, TPD, TIT and ORA are functions of time. This is a nonlinear ordinary differential equation with delay effect. It can be solved numerically by using ODE solver (Runge-Kutta method for instance [7]) with given initial condition of FOB and RI. Vensim [6] has a built-in solver. After inputting all function and initial conditions, it graphically displays its solution as well as intermediate functions. It helps us to understand behavior of system visually. Especially, it can automatically simulate on change. When moving the bar on parameter, the displayed function curves will change correspondingly. It gives us idea about reasonable range of the parameters. In figure 2, we show both retailer sales to customer and retailer order from supplier. Since the sale has a jump from 100 to 120, the order increases from 100 to 125 gradually. The gradual increase is due to smooth from sales to average sales plus the influence from the order rate adjuster.


RetailerSaleRate

200

170

140

110

80

0 10 20 30 40 50 60 70 80 90 100Time (Day)

RetailerSaleRate : Current

RetailerOrderRate

200

175

150

125

100

0 10 20 30 40 50 60 70 80 90 100Time (Day)

RetailerOrderRate : Current

Figure 2. Retailer sale rate verses retailer order rate

In figure3, we show retailer inventory levels for both production delay being 6 days (left) and being 9 days (right). In the left, the inventory keeps to be positive value and meet the current demand. The level is lower around time=35 due to demand jump at t=10. It delays because production delay and time in transit.

In the right figure, the inventory becomes negative since production delay is 9 days. The inventory level can not keep up with demand in our current setting. To fix that problem, either factory needs inventory or retailer order product beforehand (leading time) based on demand forecast. We do not study further to include forecast in the model here since we only discuss it conceptually.

RetailInventory

800

600

400

200

0

0 10 20 30 40 50 60 70 80 90 100Time (Day)

RetailInventory : Current Day

RetailInventory

800

400

0

-400

-800

0 10 20 30 40 50 60 70 80 90 100Time (Day)

RetailInventory : Current Day

Figure 3. Retailer inventory with different production delay (top 6 days, bottom 9 days)

3.3. Optimal control for dynamical system

In the last subsection, we develop a dynamical system to describe the behavior of the factory order backlog and retailer inventory level. Especially, an Order Rate Adjuster (ORA) is put into the first equation. In fact, we put that term purposely and take it as a control function in the system. As said before, we want not only to reduce inventory cost and also to catch sale opportunities. Therefore, we setup the objection function for that

∫=t

dttRIabstCtfunc0

))((*)()(

where C(t) (> 0) is the cost to keep unit of production per day when RI is positive and the cost to loss sale opportunity of unit of production per day when RI is negative. This formula is correct conceptually but might not be correct quantitatively since we simply use negative RI for sale opportunity lost. Now optimization problem is defined as the following

)))(((|)(min )(tuIsolutionRItfunc

tu∈

Where the solution(I(u(t)) is the solution set of the system given in the last subsection for give u(s) (0<s<=t). The minimizer u*(t) of the problem, combining with the average retail rate, would give the best possible the retailer order choice under the current constrain (or current setting). Theoretically, the problem can be studied by introducing co-states as well as their equations --- Hamilton-Jacobi equation [8]. Numerically, we utilize dynamic programming method and at each time-step, we not only advance states to the next time step but also use maximal principle to find a minimizer for that time step.

4. Sense-and-response versus control system

In control theory, there exist two actors: controller and environment. The studying space will be the following,

),,,,( 0 ΩΣ QQ δ

where ,,, 21 rqqqQ = is a finite set of discrete states;

21 Σ×Σ=Σ is a finite set of actions; QQ 2: 21 →Σ×Σ×δ is a

partial transition relation, QQ ⊆0is a set of initial states,

and Ω is a trajectory acceptance condition.1Σ contains

the actions of the controller and 2Σ contains the

actions of environment, so that each transition between states depends on a joint action ),( 21 σσ .


1) If transition function ),,( 21 σσδ q gives a set of

possible new states, then the finite state automation is non-deterministic.

2) If ),,( 21 σσδ q gives a null set of states, then

the state transition is prevented.

3) If ),,( 21 σσδ q gives a unique state, then the

transition is deterministic.

A system trajectory ωωωσσ 2121 ])[],[],[( Σ×Σ×∈⋅⋅⋅ Qq is a finite

or infinite sequence of states and actions which satisfies, for ,Zi∈

0]0[ Qq ∈ and ])[],[],[(]1[ 21 iiiqiq σσδ∈+ .

The trajectory acceptance condition describes a desired goad that the system should achieve, which is expressed as a specification of the state trajectory. The Sense-and-Respond system has the same issue as the control system, like observability and controllability. In fact, each Sense-and-Respond step corresponds to the pair of

1σ and2σ mentioned above. In predictive

level, the system detects the influence of environment action

1σ and then recommends action 2σ to bring

trajectory back to safe set. In adaptive level, the system was setup to automatically take the action

2σ and to

bring trajectory back to safe set. It could has some delay between 2σ and

1σ . In autonomic level, based on

current state q as well as its trend (rate change of state), these 1σ and 2σ change value and take action

simultaneously.

Since we solve the derived control and dynamical system numerically, we actually discretizes the equations along the time. The discretized version of system could be deployed into runtime environment. The actions coming from environment would be the input function mentioned TESTI, TRD, TAS, TAP, TPD, TIT mentioned in subsection 3.2 (some might be predefined as a policy). The values (encoded actions) would feed into the system sequentially. The length of time interval corresponds to the length of time step chosen for the equation system. For instance, if we choose time unit being day and the length of time step being 0.25, then we feed data four times per day. The control actions correspond to retailer order rate coming from solving the equation at each time step. So output values, based on our previous time step chosen, would also be four times per day. Order actions will take based on the output values (decoded action from system). Therefore, the model becomes a real runtime control engine and runs based on real time. It operates four times per day in the mentioned time unit and step length chosen.

5. Enabling Architecture We discuss how to make a SD model become real runtime control engine and describe the process to develop and deploy runtime SD. We need to set up the runtime sensing environment, so that the system can get all required input data sequentially based time step constraints. For our supply chain example present in section 3, we present components and interaction view of the proposed enabling system in Figure 4.

1) The event stream processor is responsible to retrieve sensing data from customer, manufacturing system and transportation management.

2) KPI calculator prepares data (encoding process). So that the SD system could understand and interpret data properly.

3) The encoded data with other inventory and order data was put into the operation data store. The ETL component would abstract relevant information and put them into data warehouse.

4) The KPI pattern learner system uses data mining technique and statistical analysis method to find pattern, demand seasonality and forecast.

5) KPI Sequencer feeds the encoded data into the control engine in correct order and interval (based time step setting).

6) The control engine runs based the SD model retrieved from model repository and setting. The setting includes static parameters and initial conditions. Some are propagated from the inventory and order system. Some are learned from expert system (KPI patter learner).

7) The control engine coordinates with optimal controller which works based on the defined objective function. The dynamical system internal state could be persistent so that the system could be recovered when system failure happens.

8) Based on decision policy, the decision enforcer uses the decisional metrics created by the control engine to carry out the action – order products from supplier in our case.


Customer

OEM

Public Exchange

VAR

...Supplier

Trading Company

CM

OEM

Order Management Systems

...DIstributors

NetworkCommunity

PublicExchanges Carriers

EMS/CM

...Demand/Supply

Inventory Management

SystemsCentral

Planning Procurement

Forecasting

...Sector

Tool

Workstation

Manufacturing System

Figure 4. Runtime control system based SD model

In Figure 5, we show the developing and deploying process. It includes the system dynamics hypothesis, formal formulation, instrumentation, calibration, development, deployment and then running and monitoring.

Figure 5. Develop and deploy the runtime SD process

6. Related Work Haeckel [1] was the first research proposing the Sense-and-Respond Concept. He lays out a strategy to create such a "sense-and-respond" approach that will allow companies to move quickly amid change. Among the key steps: companies must use innovative ways to gather information about customer needs. For instance, car manufacturers used video cameras in airport parking lots to discover that people often struggle to lift heavy suitcases over the high lower edges of trunks. In mall parking areas, the cameras revealed that shoppers had nowhere to put soft drinks they just bought. Now, low trunk edges and cup holders are standard features in almost every car. Haeckel’s approach is focused on creating Sense-and-Respond strategy at the corporate level. However, our approach is aimed for addressing the issues of developing Sense-and-Respond approach at the business process level. Our focus is also on building real systems that enable autonomic business process management systems in enterprises.

Grace Lin et al [9] presented the SAR Blue Enterprise system that is an autonomic integrated management system driving planning and execution in alignment with strategy and business objectives through its sensing, responding, and analyzing capabilities. It is an enterprise management paradigm that enables automatic sensing of complex internal and external business environmental changes, and responds quickly with the best available policies in order to achieve the business objectives. The IBM Research team of Sense-and-Respond systems perform analytic studies of SCM that have addressed strategic value chain decisions such as global manufacturing and distribution network design, demand forecasting, capacity planning, inventory management, supply planning and allocation, production scheduling and business process analysis. During the last decade, IBM Research has extended its model of research to include work with IBM’s customers. Bringing IBM scientists and customers together to tackle real-world business problems has advanced the application of information technology as well as the underlying science and mathematics. The major difference between their and our approaches lies in our approach of using system dynamics to model and drive the behavior of Sense-and-Respond systems.

7. Future Work and Conclusion We apply the system dynamics and optimal control concept to the Sense-and-Respond system. The system dynamics modeling and simulation exposes the KPI dynamics behavior and causality relationship between KPIs. Combing with the optimal control, the SD runtime could give the best action should take to achieve the objective defined by objective function. We present an architecture to enable a developed SD model become a runtime control engine and automate certain business processes. Our study is on conceptual level in this paper. We will develop SD models based on real scenarios and calibrate the model by comparing the result with others result from statistical analysis [4]. Historic data also provides means to validate the model. Enabling architecture would be implemented to provide real time control for the process and used in autonomic computing. We investigate further about the relationship between system dynamics and optimal control with the Sense-and-Respond system in the business process.


Acknowledgement Our appreciation goes to our managers that provide full support of this work - Steve Buckley, Henry Chang, David Cohn and Brenda Dietrich.

References [1] Haeckel, S., Slywotzky, A., Adaptive

Enterprise: Creating and Leading Sense-and-Respond Organizations, Harvard Business School Press, Cambridge, MA, 1999

[2] Forrester, J.W., Industry Dynamics. MIT Press, Cambridge, MA, 1961.

[3] Sterman, J.D., Business Dynamics: System Thinking and Modeling for a Complex World. Irwin McGraw-Hill, Boston, 2000.

[4] Ettl, M., Cheng, F., Lin, G., Yao, D.D., Inventory Management in High-Technology Value Chains. 2003.

[5] Oliva, R., Model Calibration as a Testing Strategy for System Dynamics Models, European Journal of Operational Research 151, 2003, 552-568.

[6] Vensim: http://www.vensim.com

[7] Press, W.H., Flannery, B.P., Teukolsky, S.A., Vettering, W.T., Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, 1992

[8] Sethi, S.P., Optimal control theory: applications to management science and economics, Kluwer Academic Publishers, Boston, 2000.

[9] G. Lin et al. “The New Frontier: Sense-and-Respond System for Global Value Chain Optimization,” OR/MS Today, May 2002.


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