Ani Calinescu

Advances on measuring the operational complexity of supplier–customer systems

Even structurally simple supplier–customer systems can be operationally complex. This operational complexity can be colloquially defined as the uncertainty associated with managing the dynamic variations, in time or quantity, across information and material flows at the supplier–customer interface. This paper proposes a means of measuring the information demands placed on supplier–customer systems, as a result of this uncertainty. This paper mathematically models the operational complexity of supplier–customer systems from an information-theoretic perspective. A unique feature of this measure is that it captures, in relative terms, the expected amount of information required to describe the state of the system. The measure provides flexibility in the scope and detail of analysis, while at the same time allowing a systematic hierarchical approach. The application of the measure allows valuable insights to be obtained in terms of the degree of uncertainty, level of control and the detail of monitoring required to manage the operational complexity of supplier–customer systems.

A web-based expert system to assess the complexity of manufacturing organizations

Abstract Information-theoretic modelling of manufacturing organizations and their supply chains has led to the development of measures of manufacturing complexity. The measures include assessment of the structural, dynamic and decision-making complexity associated with the processing and movement of material and information around a manufacturing system. A computer program has been written to calculate the decision-making complexity of a manufacturing system, under different system layouts and operating characteristics. In order to make the results of this program accessible to manufacturing organizations, an expert system has been developed to act as a mediator between the program and interested organizations. Given some simple quantitative data on manufacturing performance, the expert system can estimate the organizations complexity and suggest some recommendations to reduce it, based on the data provided by the organization. The expert system will be implemented on the web to enable on-line acquisition and searching of data on companies. The quid pro quo of the expert system is that anonymized data on the organizations will be retained so that complexity benchmarks may be established.

Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems

The primary objective of this paper is to compare five rescheduling strategies according to their effectiveness in reducing entropic-related complexity arising from machine breakdowns in manufacturing systems. Entropic-related complexity is the expected amount of information required to describe the state of the system. Previous case studies carried out by the authors have guided computer simulations, which were carried out in Arena 5.0 in combination with MS Excel. Simulation performance is measured by: (1) entropic-related complexity measures, which quantify: (a) the complexity associated with the information content of schedules, and (b) the complexity associated with the variations between schedules; and (2) mean flow time. The results highlight two main points: (a) the importance of reducing unbalanced machine workloads by using the least utilised machine to process the jobs affected by machine breakdowns, and (b) low disruption strategies are effective at reducing entropic-related complexity; this means that applying rescheduling strategies in order to manage complexity can be beneficial up to a point, which, in low disruption strategies, is included in their threshold conditions. The contribution of this paper is two-fold. First, it extends the application of entropic-related complexity to every schedule generated through rescheduling, whereas previous work only applied it to the original schedule. Second, recommendations are proposed to schedulers for improving their rescheduling practice in the face of machine breakdowns. Those recommendations vary according to the manufacturing organisations’ product type and scheduling objectives. Further work includes: (a) preparing a detailed workbook to measure entropic-related complexity at shop-floor level; and (b) extending the analysis to other types of disturbances, such as customer changes.

Extending the information-theoretic measures of the dynamic complexity of manufacturing systems

This paper revisits and extends the structural and dynamic complexity measures of manufacturing systems, which are based on an information-theoretic interpretation of the amount of information that is needed to describe the state of a manufacturing system. In this paper, a generic manufacturing system is modelled as a set of interacting resources and queues. At any moment in time, the combined specific states of the resources and queues define the overall state of the manufacturing system. The main contribution of this paper is a set of dynamic complexity measures that quantify the rate at which information is generated by the facility, and which may be applied with or without reference to the facilitys schedule. A worked example demonstrates the step-by-step calculations to obtain values of the complexity measures, and guidance is provided on how to obtain the data needed to carry out the calculations. The links between these complexity measures and the behaviour of a facility are identified and described.

Operational complexity and supplier–customer integration: case study insights and complexity rebound

The main contribution of this paper is the demonstration that, contrary to conventional thinking, a measurable increase in the operational complexity of the production scheduling function between two companies can occur following closer supply chain integration. The paper presents the practical application of previous work carried out and validated by the authors in terms of (a) methodology for measuring operational complexity, (b) predicted implications of Supplier–Customer integration and (c) derivation of an operational complexity measure applied to before and after Supplier–Customer integration. This application is illustrated via a longitudinal case study. The analysis is based on information theory, whereby operational complexity of a Supplier–Customer system is defined as the amount of information required to describe the state of this system. The results show that operational complexity can increase when companies decide to integrate more closely, which is a fact likely to be overlooked when making decisions to pursue closer Supply-Chain integration. In this study, operational complexity increases due to reduced buffering arising from reduction in the Suppliers inventory capacity. The Customer did not change their operational practices to improve their schedule adherence post-integration, and, consequently, suffered an increase in complexity due to complexity rebound. Both the Suppliers and Customers decision-making processes after the case study reported in this paper were enhanced by being able to quantify the complex areas to prioritise and direct managerial efforts towards them, through the use of the operational complexity measure. Future work could extend this study (in the ‘low product customisation’ and ‘low product value impact’ quadrant) to investigate Supplier–Customer integration in other quadrants resulting from further combinations between ‘product customisation’ and ‘product value impact’ levels.

Extracting Significant Phrases from Text

Prospective readers can quickly determine whether a document is relevant to their information need if the significant phrases (or keyphrases) in this document are provided. Although keyphrases are useful, not many documents have keyphrases assigned to them, and manually assigning keyphrases to existing documents is costly. Therefore, there is a need for automatic keyphrase extraction. This paper introduces a new domain independent keyphrase extraction algorithm. The algorithm approaches the problem of keyphrase extraction as a classification task, and uses a combination of statistical and computational linguistics techniques, a new set of attributes, and a new learning method to distinguish keyphrases from non-keyphrases. The experiments indicate that this algorithm performs at least as well as other keyphrase extraction tools and that it significantly outperforms Microsoft Word 2000s AutoSummarize feature.

Transferable Lessons from Biological and Supply Chain Networks to Autonomic Computing

Autonomic computing undoubtedly represents the solution for dealing with the complexity of modern computing systems, driven by ever increasing user needs and requirements. The design and management of autonomic computing systems must be performed both rigorously and carefully. Valuable lessons in this direction can be learned from biological and supply chain networks. This paper identifies and discusses but a few transferable lessons from biological and supply chain networks to autonomic computing. Characteristics such as structural and operational complexity and the agent information processing capabilities are considered for biological and supply chain networks. The relevance of the performance measures and their impact on the design, management and performance of autonomic computing systems are also considered. For example, spare resources are often found in biological systems. On the other hand, spare resources are frequently considered a must-not property in designed systems, due to the additional costs associated with them. Several of the lessons include the fact that a surprisingly low number of types of elementary agents exist in many biological systems, and that architectural and functional complexity and dependability are achieved through complex and hierarchical connections between a large number of such agents. Lessons from supply chains include the fact that, when designed and managed appropriately, complexity becomes a value-adding property that can bring system robustness and flexibility in meeting the users’ needs. Furthermore, information-theoretic methods and case-study results have shown that an integrated supply chain requires in-built spare capacity if it is to remain manageable.

Agents in the supply chain: lessons from the life sciences

Publisher Summary Supply chains are complex, dynamic organizations that may have undesirable properties, such as the Bullwhip effect. Attempts to map supply networks have been frustrated by the dynamic nature of these organizations. A multidisciplinary research project at the University of Oxford is integrating researchers from the life and social sciences with engineers, physicists, and computer scientists to address fundamentally the modeling and understanding of complex, agent-based dynamic networks. This cluster of researchers investigate ways in which lessons from the life sciences may be transferred to the design and management of complex networks of entities in supply chains and other distributed networks. The case study networks of the project include fungal networks, slime molds, and ant colonies. The goal of the project is to enable the design and management of complex distributed systems, such as supply chains. This chapter discusses this research project and explains the advantages of agent-based modeling of supply networks.

Reply to A Makui

the related complexities computed by model (2d): As can be seen from Table 2, complexity values for the different cases are much closer to reality and can reflect the real sense for a manufacturing system. The concept of manufacturing complexity has a great influence on the management of the manufacturing systems. So computing the right values of complexity can provide the right tools for an accurate management. From this viewpoint, a modified approach has been proposed for computing the static complexity that can consider simultaneously the distribution of the states and the role of the states in generating the complexity. This new method can give a more accurate value for the static complexity.