In this paper, we present a novel ranking technique that we developed in the context of an application that arose in a Service Delivery setting. We consider the problem of ranking agents of a service organization. The service agents typically need to interact with other service agents to accomplish the end goal of resolving customer requests. Their ranking needs to take into account two aspects: firstly, their importance in the network structure that arises as a result of their interactions, and secondly, the value generated by the interactions involving them. We highlight several other applications which have the common theme of ranking the participants of a value creation process based on the network structure of their interactions and the value generated by their interactions. We formally present the problem and describe the modeling technique which enables us to encode the value of interaction in the graph. Our ranking algorithm is based on extension of eigen value methods. We present experimental results on real-life, public domain datasets from the Internet Movie DataBase. This makes our experiments replicable and verifiable.

One of the distinguishing features of the services industry is the high emphasis on people interacting with other people and serving customers rather than transforming physical goods like in the traditional manufacturing processes. It is evident that analysis of such interactions is an essential aspect of designing effective and efficient services delivery. In this work we focus on learning individual and team behavior of different people or agents of a service organization by studying the patterns and outcomes of historical interactions. For each past interaction, we assume that only the list of participants and an outcome indicating the overall effectiveness of the interaction are known. Note that this offers limited information on the mutual (pairwise) compatibility of different participants. We develop the notion of service interaction networks which is an abstraction of the historical data and allows one to cast practical problems in a formal setting. We identify the unique characteristics of analyzing service interaction networks when compared to traditional analyses considered in social network analysis and establish a need for new modeling and algorithmic techniques for such networks. On the algorithmic front, we develop new algorithms to infer attributes of agents individually and in team settings. Our first algorithm is based on a novel modification to the eigen-vector based centrality for ranking the agents and the second algorithm is an iterative update technique that can be applied for subsets of agents as well. One of the challenges of conducting research in this setting is the sensitive and proprietary nature of the data. Therefore, there is a need for a realistic simulator for studying service interaction networks. We present the initial version of our simulator that is geared to capture several characteristics of service interaction networks that arise in real-life.

Globalization is here to stay. Companies source, manufacture, and sell across borders. There are several destinations available for undertaking these activities offering varying degrees of incentives and at each destination the company incurs a different delivery cost. Multinational companies need to make more realistic decisions about where to make, source, locate, move, and store products to minimize the total cost of delivery keeping in mind the incentives offered by the governments and the logistics costs at and from the location. Current literature on supply chain optimization does not emphasize on tax. To attract foreign investment, many developing economies have included tax-holidays in their export-import (EXIM) policy for companies operating in free trade zones (FTZs). In this paper, we propose a tax integrated mixed integer model, for optimally deciding the foreign direct investment (FDI)-outsourcing (the choice of establishing captive production centers versus complete outsourcing) alternatives at the various stages of a global supply chain. For a general acyclic supply chain, this decision problem is NP-hard and obtaining analytical results on optimal FDI-outsourcing strategy may be difficult. We linearize the tax integrated model by introducing exactly one hub at each stage. In this case, termed hub-based sourcing-single hub case, we prove that the greedy strategy is an optimal FDI-outsourcing strategy. However, by associating multiple hubs at each stage the decision problem remains NP-hard. Finally, we empirically analyze the tax integrated model (for the general case) on a use-case scenario in which some locations in the choice have free trade zones offering tax incentives.

In this paper, we develop a framework to classify supply chain risk-management problems and approaches for the solution of these problems. We argue that risk-management problems need to be handled at three levels: 1) strategic, 2) operational, and 3) tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions, and disasters. To handle unforeseen events in the supply chain, there are two obvious approaches: 1) to design chains with built-in risk tolerance and 2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and the associated consequences and impacts from these events. Having described these approaches, we then focus our efforts on mapping out the propagation of events in the supply chain due to supplier nonperformance, and employ our insight to develop two mathematical programming-based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design

An Integrated Manufacturing and Service Network (IMSN) is a grouping of companies, working together to offer a bundle of products and product-related services, that deliver value to customers over the entire useful life of the product, from purchase to disposal. The success of such an alliance is highly dependent on the seamless integration and interaction of business strategy, operational processes and enterprise systems between constituent companies and their design, manufacturing and sales operations. In this paper, we present system dynamics models to study the behavior of an IMSN and investigate the bullwhip effects that arise within such networks due to mismatches and delays between the manufacturing and service systems within the network. We show that integration and collaboration between the manufacturing and service operations with two-way information flow between them enhances profitability and minimizes the bullwhip effect within repair centers.

The design and planning of supply chain networks supporting production and distribution of multiple product generations overlapping with each other is of critical importance in the high-tech industry. We address the strategic supply chain network design problem in rapidly changing industry segments, where the selection of partners such as component suppliers, contract manufacturers and logistics providers is done, based on the capabilities of the partners for supporting the strategic needs of the current and near future generations of a finished product. We develop a mixed integer-programming model for integrated planning and scheduling across the supply chain and show how such a model may be used for making decisions related to introduction and rollovers of finished products and components from one generation to another. We assume that all stakeholders in the supply chain collaborate and share information on their capacities, schedules and cost structures. Based on this information the model addresses the issue of partner selection and planning for optimal profit. The model was solved using optimization tools from ILOG. Managerial insights are obtained by performing a series of simulated experiments on the model developed. For example, we show that an expensive supplier possessing the ability to develop and supply components required across a number of generations, might be preferred against a cheaper supplier supplying components suitable for a specific generation of the product. In addition, we show how the supply chain network configuration changes over the lifecycle of the product, wherein cheaper overseas suppliers slowly replace responsive and expensive local suppliers as the product matures. We also develop a framework to quantify and compare the costs and benefits of pursuing alternative product introduction plans and deadlines. Such a framework might be employed to determine the optimal product introduction schedule. We show here that in some cases it might not be profitable to launch a product in the market after a certain period of time.

In this paper, we develop a framework to classify supply chain risk management problems and approaches for the solution of these problems. We argue that risk management problems need to be handled at three levels strategic, operational and tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions and disasters. To handle unforeseen events in the supply chain there are two obvious approaches: (1) to design chains with built in risk-tolerance and (2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and also the associated consequences and impacts from these events. We can then focus our efforts on mapping out the propagation of events in the supply chain due to supplier non-performance, and employ our insight to develop two mathematical programming based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design.

Variability reduction and business-process synchronization are acknowledged as keys to achieving sharp and timely deliveries in supply-chain networks. In this paper, we introduce a new notion, which we call six sigma supply chains to describe and quantify supply chains with sharp and timely deliveries, and develop an innovative approach for designing such networks. The approach developed in this paper is founded on an intriguing connection between mechanical design tolerancing and supply-chain lead-time compression. We show that the design of six sigma supply chains can be formulated as a mathematical programming problem, opening up a rich new framework for studying supply-chain design optimization problems. To show the efficacy of the notion and the design methodology, we focus on a design optimization problem, which we call the inventory optimization (IOPT) problem. Given a multistage supply-chain network, the IOPT problem seeks to find optimal allocation of lead time variabilities and inventories to individual stages, so as to achieve required levels of delivery performance in a cost-effective way. We formulate and solve the IOPT problem for a four-stage make-to-order liquid petroleum gas supply chain. The solution of the problem offers rich insights into inventory-service level tradeoffs in supply-chain networks and proves the potential of the new approach presented in this paper.Note to Practitioners-This paper builds a bridge between mechanical design tolerancing and supply-chain management. In particular, the paper explores the use of statistical tolerancing techniques in achieving outstanding delivery performance through variability reduction. Informally, a six sigma supply chain is that which delivers products within a customer specified delivery window, with at most 3.4 missed deliveries per million. The innovations in this paper are the following: 1) to define two performance metrics delivery probability and delivery sharpness to describe the precision and accuracy of deliveries, in terms of process capability indexes C/sub p/,C/sub pk/, and C/sub pm/; 2) to formulate the supply-chain design optimization problem using the process capability indices; 3) to suggest an efficient solution procedure for the design optimization problem. The paper presents the case study of a two-echelon distribution network and using the framework developed in the paper shows the role of inventory in controlling lead time variability and achieving six sigma levels of delivery performance.

In this paper, we develop a framework for robust supply chain design, i.e., to build supply chains, which can handle disruptive events with minimal loss. There are two obvious steps to handle exceptions: (1) to design chains with built in risk-tolerance; and (2) to contain the damage once the undesirable event has occurred. In this paper, we consider design of robust (to risks) supply chains. First, we identify all the critical undesirable events that can occur at various stake holders and their interfaces and map out how each of them propagates through the supply chain. In this way, we identify the critical exceptions for various partners of the chain and understand the impact of all these exceptions, particularly on the supply. We develop a simple mixed integer programming optimization model, adapted from the credit risk minimization model, for partner selection as a means towards robust supply chain design. We choose the partners so that the effect of exceptions on the supply chain is minimized.

Variability reduction and business process synchronization are acknowledged as key to achieving sharp and timely deliveries in supply chain networks. In this paper, we introduce a new notion, which we call six sigma supply chains to describe and quantify supply chains with sharp and timely deliveries, and develop an innovative approach for designing such networks. We show that design of six sigma supply chains can be formulated as a mathematical programming problem, opening up a rich, new framework for studying supply chain design optimization problems. To show the efficacy of the notion and the design methodology, we focus on a design optimization problem, which we call as the Inventory Optimization (IOPT) problem. We formulate and solve the IOPT problem for a four stage, make-to-order liquid petroleum gas supply chain. The solution of the problem offers rich insights into inventory-service level tradeoffs in supply chain networks and proves the potential of the new approach presented in this paper.

Supply chain configurations should change over the lifecycle of the product. During the early stages expensive suppliers with greater reliability and flexibility are desirable in order to meet uncertain market requirements, whereas in later stages with steady and declining demand, low cost suppliers should be chosen. Given the extremely short and overlapping product lifecycles in the Hi-Tech industry, it becomes necessary to use formal planning models to manage such dynamic supply chain configurations. In this paper, we develop a mixed integer-programming model for integrated planning across the supply chain and show how such a model may be used for decision-making across the product lifecycle. We assume that all stakeholders in the supply chain share information on their capacities, schedules and cost structures. Based on this information the model addresses the issue of partner selection and planning for optimal profit. The model was solved using optimization tools from ILOG.

In this paper, we come up with an innovative approach through which variability reduction and synchronization can be realized in supply chains. The approach developed is founded on a connection between mechanical design tolerancing and supply chain lead time compression. We use two metrics for delivery performance, delivery sharpness and delivery probability, which measure the accuracy as well as the precision with which products are delivered to the customers. Then we solve the following specific problem: given the delivery sharpness and delivery probability to be achieved, how can variability be allocated across individual stages of the supply chain in a cost-effective way. We call this the variance pool allocation (VPA) problem and we suggest a systematic approach for solving the VPA problem. We show that a variety of important supply chain design problems, such as supply chain partner selection, can be posed as instances of the VPA problem. We formulate and solve the VPA problem for a plastics industry supply chain and demonstrate how the solution can be used to choose the best mix of supply chain partners.

In this paper, we develop a mixed-integer programming model for integrated partner selection and scheduling in an Internet-enabled dynamic manufacturing network environment. We assume that all stakeholders in the supply chain (SC) share information on their capacities, schedules, and cost structures. Based on this information, the model addresses the issue of partner selection and SC synchronization for profit maximization, while considering various manufacturing and logistics constraints. Furthermore, we study the dynamic configuration of the SC and its performance with respect to different buyer locations, different order patterns, and the utilization of transshipment hubs. The model is solved using optimization tools from ILOG, located in Paris, France, and Mountain View, CA.

In this paper we develop a quadratic programming model for integrated supply chain planning in an electronics-manufacturing environment supported by digital marketplaces. We consider a scenario where a sub-assembly manufacturer procures components from suppliers through an exchange and sells its manufactured sub-assemblies through another exchange. The model addresses for the sub-assembly manufacturer the issue of pricing and customer selection in the sub-assembly marketplace for maximum revenue and partner selection within the components procurement marketplace for minimal cost of manufacturing and delivery. Simultaneously, the model determines optimal schedules that synchronize activities across the supply chain.

In this paper, our objective is to come up with a sound methodology to design supply chains with outstanding delivery performance. As the first step towards this objective, we consider supply chains with a linear workflow, which we call pipelined supply chains. We define a new index of delivery performance called delivery sharpness which measures the precision as well as the accuracy with which products are delivered to the customers. The specific problem we solve is: given the delivery sharpness to be achieved, how can we allocate variability across individual stages of the supply chain in a cost-effective way. We call this the variance pool allocation (VPA) problem. In formulating and solving the VPA problem, we explore interesting relationships among process capability indices C/sub p/, C/sub Pk/, and C/sub Pm/, and generalize the notion of Motorola six sigma performance. The VPA problem leads to a four step design methodology and the resulting optimization problem is solved using the method of Lagrange multipliers. We present an interesting example of a supply chain in the plastics industry and illustrate the different steps of our methodology.

We develop a linear programming model for integrated partner selection and scheduling in a web-enabled global manufacturing network environment. We assume that all stakeholders in the supply chain share information on their capacities, schedules and cost structures. Based on this information, the model addresses the issue of partner selection for minimal cost of manufacturing and delivery. The model is solved using ILOG optimization tools.

Supply chain networks are formed out of complex interactions amongst several companies whose aim is to produce and deliver goods to the customers at the time and place specified by them. Computing the total lead time for customer orders entering such a complex network of companies is an important exercise. In this paper, we present analytical models for evaluating the average lead times of make-to-order supply chains. In particular, we illustrate the use of fork-join queueing networks to compute the mean and variance of the lead time. The existing literature on approximate methods of analysis of fork-join queueing systems assume heavy traffic and require tedious computations. We present two applications of a tractable approximate analytical method for lead time computations in a class of fork-join queueing systems. For the case where the arrivals are deterministic and service times are normally distributed, we present an easy to use approximate method. Specifically, we illustrate the use of the above method in setting service levels in assemble-to-order type supply chains.

A supply chain network can be viewed as a network of facilities in which a customer order will flow through internal business processes such as procurement, production, and transportation, ultimately reaching the required products on time to customers. The delivery performance of such a network can be maximized by pushing the work through the system in a way that the finished products reach the customer in a customer specified delivery window, with a very high probability. This entails synchronization and hence strict control of variability of deliveries at all intermediate points, to ensure that the raw materials and semi-finished work arrive at work spots in a timely fashion. We explore the use of the Motorola six sigma tolerancing methodology to achieve synchronization in supply chains. In particular, we use the six sigma approach to: (1) analyze a given supply chain process for six sigma delivery performance; and (2) design synchronized supply chains to guarantee six sigma delivery performance. We provide an example of a plastics industry supply chain, for which we report analysis and design experiments that demonstrate the use of the six sigma approach in designing synchronized supply chains with high levels of delivery performance.

Integrated supply chains are networks of companies in alliance sharing the same destiny for mutual business advantage. In such networks, end-to-end material and information flows-from raw material production to retailer sales-are optimally and collaboratively managed to create value to the customers and other stakeholders. We first introduce the configuration and physics of integrated supply chain networks (ISNs) and identify the automation and Internet technologies that are critical for their effective and efficient operation. Next, we identify the strategic, tactical, and operational decisions in ISNs and the analytical techniques used in making these decisions. We conclude the paper with a discussion on some significant problems that require attention.

The problem of optimal divisible load distribution in distributed bus networks employing a heterogeneous cluster of processors is addressed. The objective is to minimize the total processing time of the entire load subject to the communication and computation delays. In the mathematical model we adopt, both the granularity of the load fractions and all the associated overheads (also referred to as start-up costs) in the process of communication and computation, are considered explicitly in the problem formulation. We introduce a directed flow graph model for representing the load distribution process. This representation is novel to this literature. With this model, we first derive a closed-form solution for an optimal processing time. We propose an integer approximation algorithm and derive ultimate performance bounds for the class of homogeneous networks. We then extend the problem to a special class of application problems in which the data partitioning is restricted to a finite number of partitions. For this case, we present a recursive procedure to obtain optimal processing time. We then present two different integer approximation algorithms-PIA and IIA that could generate integer load fractions and yield suboptimal solutions. The choice of these algorithms are also analyzed. All the results are extended to a class of homogeneous networks to obtain ultimate performance bounds. Several illustrative examples are provided for ease of explanation.

New product development is an important business process and constitutes a major contributor to the business excellence of any manufacturing firm. Designing an optimized new product development process is an important problem in itself and is of significant practical and research interest. Lead time is an important performance metric for a product development organization. We develop lead time models for product development organizations that involve multiple, concurrent projects with contention for human/technical resources. The objective is to explore how the lead times call be reduced using efficient scheduling, input control, load balancing, and variability reduction. The models are based on single class and multiclass queueing networks and capture important facets of a product development organization. Within the new product development process, we focus attention on the product design process. Two product design organizations, which we call Company ABC and Company XYZ, provide the real-world setting for our model-based lead time reduction. First, we present a coarse, conceptual queueing network model of Company ABC and show how rapid performance analysis can be used to explore opportunities for accelerating the design process. In particular, we show how effective input control, process control, load balancing, and cross-functional work can cut the lead times. Next, we present multiclass queueing network models for both the companies ABC and XYZ. The re-entrant line models show up certain scheduling issues pertaining to internal flows in the product design network. Using a class of fluctuation smoothing scheduling policies, we demonstrate how lead times can be reduced appreciably, without committing additional resources. The models presented are sufficiently generic and conceptual, and will be of much value in project planning and management in product design organizations and also more generally in product development organizations.

Flexible assembly systems (FAS) have attracted considerable interest in the area of manufacturing automation and promises to be very efficient approach for producing small and medium sized batches of products. A very important problem which affects the performance of FAS is machine loading problem. In this paper we model machine loading problem as nonlinear integer optimization problem and develop a simulated annealing algorithm to solve the problem.

Quality is the hallmark of a competitive product. It is necessary to use inspection stations to check product quality and process performance. In this paper, the authors are concerned with the problem of location of inspection stations in a multistage manufacturing system. The authors present two stochastic search algorithms for solving this problem, one based on simulated annealing and the other on genetic algorithms. These algorithms are developed to determine the location of inspection stations resulting in a minimum expected total cost in a multistage manufacturing system. The total cost includes inspection, processing and scrapping cost at each stage of the production process. A penalty cost is also included in it to account for a defective item which is not detected by the inspection scheme. A set of test examples are solved using these algorithms. The authors also compare performance of these two algorithms.

We consider the transient performance analysis of failure-prone manufacturing systems producing multiple part types. We decompose the exact monolithic model into: 1) a slower time scale structure state process modeling the failure and repair, and 2) a faster time scale performance model describing the part processing and the material movement. We combine the solution of these two models to show that the accumulated reward over a given time interval is a solution of a set of forward or adjoint multidimensional linear hyperbolic partial differential equations. This result generalizes the existing results on composite performance-dependability analysis of manufacturing systems. We also present efficient numerical methods for computing the distribution of the cumulative operational time, and the mean and variance of the cumulative production over a given time interval. Further, we bring out the significance of these results in the manufacturing systems context through several examples.>

Performance evaluation of failure-prone manufacturing systems is of significant practical interest. In this paper, the authors consider the parametric sensitivity analysis of manufacturing systems producing multiple part types. This involves transient analysis of the Markov model of the system and computation of the derivative of the cumulative throughput with respect to the varying parameters. Such an analysis would be useful in studying the effects of various not exactly known parameters on the system performance, identifying the bottlenecks and hot spots and in system optimization studies. In this paper, the authors derive expressions for the sensitivity of the first two moments of the throughput-related performability with respect to failure and repair rates of various subsystems such as machines, guided vehicles, robots, etc. The authors also present two examples to illustrate the theoretical results of this paper.>

Uses discrete-time Markov reward models to obtain moment recursions of the cumulative performance of an automated manufacturing system (AMS). AMS exists in various structure states due to failures, repairs and reconfigurations of its components. This multi-state behavior is modeled using a Markov reward structure that is similar to the continuous-time versions. This methodology may also be used to compute other performance measures, such as the expected in-process inventory and the expected production per unit time. A three-stage transfer line with finite buffers is used to illustrate the methods obtained in the paper.>

In this paper, the authors are concerned with the problem of location of inspection centers in a multistage manufacturing system. A genetic algorithm based approach is developed to determine the location of inspection centers resulting in a minimum expected total cost. The total cost includes inspection, manufacturing and scrapping cost at each stage of the manufacturing process. A penalty cost is included in it to account for a defective item which is not detected by the inspection scheme. A set of test problems are solved using this algorithm.>

We present several situations in manufacturing systems where transient analysis is very important. Manufacturing systems and models in which such situations arise include: systems with failure states and deadlocks, unstable queueing systems, and systems with fluctuating or nonstationary workloads. Even in systems where equilibrium exists, transient analysis is important in studying issues such as accumulated performance rewards over finite intervals, first passage times, sensitivity analysis, settling time computation, and deriving the behavior of queueing models as they approach equilibrium. In this paper, we focus on transient analysis of Markovian models of manufacturing systems. After presenting several illustrative manufacturing situations where transient analysis has significance, we discuss two problems for demonstrating the importance of transient analysis. The first problem is concerned with the computation of distribution of time to absorption in Markov models of manufacturing systems with deadlocks or failures, and the second problem shows the relevance of transient analysis to a multiclass manufacturing system with significant setup times. We also discuss briefly computational aspects of transient analysis.>

This paper deals with the transient analysis of manufacturing systems in the presence of failures and repairs. The exact model is decomposed into: 1) a structure state process, modeling the failure and repair events which occur at a slower time scale; and 2) a performance model, modeling the part processing and material movement events occurring at a faster time scale. The solutions of these two models are combined to yield the mean and standard deviation of cumulative production over an interval. Such studies, called combined performance-dependability studies, are important in the manufacturing context. We define combined performance and dependability (performability) measures in the manufacturing context, and provide a mathematical formulation. Detailed performability studies are carried out for cellular manufacturing systems, in particular for a flexible manufacturing cell and a two-cell manufacturing system featuring blocking and prioritized, centralized repair. We also present several numerical examples.>

Deadlocks constitute an important issue to be addressed in the design and operation of FMSs. It is shown that prevention and avoidance of FMS deadlocks can be implemented using Petri net models. For deadlock prevention, the reachability graph of a Petri net model of the given FMS is used, whereas for deadlock avoidance, a Petri-net-based online controller is proposed. The modeling of the General Electric FMS at Erie, PA, is discussed. For such real-world systems, deadlock prevention using the reachability graph is not feasible. A generic, Petri-net-based online controller for implementing deadlock avoidance in such real-world FMSs is developed.>

The problem of modeling knowledge about the fault behavior of a system and utilizing this model for reasoning about and diagnosing failures is addressed. A solution that merges graph and fault-tree-based failure analysis with rule-oriented reasoning is presented. Failure analysis is divided into two phases, a failure source location phase and a failure cause identification phase. Each phase consists of a failure model and a process that operates on it. The failure models for the first and second phases are based on lesel-structured fault propagation digraphs and augmented fault trees, respectively. The augmented fault tree (AFT) is a conceptual structure that encodes probabilistic, temporal, and heuristic information in addition to the causal aspects of failures modeled by conventional fault trees. The two models are combined to form a novel hierarchical failure knowledge representation scheme. Upper levels of this hierarchy are made up of the fault propagation digraphs. Each level represents a view of the system under a particular granularity, and the granularity increases with levels. This feature permits control over the resolution of fault diagnosis. The lowest level consists of a set of cause-consequence knowledge bases containing production rules. These production rules are derived from augmented fault trees and represent the cause-effect relations among failure events that lead to the corresponding subsystem's failure. A knowledge acquisition procedure to generate these failure models and failure analysis processes that operate on them are described. The methodology proposed is inherently parallel as the processes may operate on different levels independently.