Martha C. Cooper

An examination of procedures for determining the number of clusters in a data set

A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters. To provide a variety of clustering solutions, the data sets were analyzed by four hierarchical clustering methods. External criterion measures indicated excellent recovery of the true cluster structure by the methods at the correct hierarchy level. Thus, the clustering present in the data was quite strong. The simulation results for the stopping rules revealed a wide range in their ability to determine the correct number of clusters in the data. Several procedures worked fairly well, whereas others performed rather poorly. Thus, the latter group of rules would appear to have little validity, particularly for data sets containing distinct clusters. Applied researchers are urged to select one or more of the better criteria. However, users are cautioned that the performance of some of the criteria may be data dependent.

Issues in Supply Chain Management

Abstract Successful supply chain management requires cross-functional integration and marketing must play a critical role. The challenge is to determine how to successfully accomplish this integration. We present a framework for supply chain management as well as questions for how it might be implemented and questions for future research. Case studies conducted at several companies and involving multiple members of supply chains are used to illustrate the concepts described.

Supply Chain Management: More Than a New Name for Logistics

Practitioners and educators have variously addressed the concept of supply chain management (SCM) as an extension of logistics, the same as logistics, or as an all‐encompassing approach to business integration. Based on a review of the literature and management practice, it is clear that there is a need for some level of coordination of activities and processes within and between organizations in the supply chain that extends beyond logistics. We believe that this is what should be called SCM. This article proposes a conceptual model that provides guidance for future supply chain decision‐making and research.

Supply Chain Management: Implementation Issues and Research Opportunities

In 1998, the Council of Logistics Management modified its definition of logistics to indicate that logistics is a subset of supply chain management and that the two terms are not synonymous. Now that this difference has been recognized by the premier logistics professional organization, the challenge is to determine how to successfully implement supply chain management. This paper concentrates on operationalizing the supply chain management framework suggested in a 1997 article. Case studies conducted at several companies and involving multiple members of supply chains are used to illustrate the concepts described.

Characteristics of Supply Chain Management and the Implications for Purchasing and Logistics Strategy

The concepts of a supply chain and supply chain management are receiving increased attention as means of becoming or remaining competitive in a globally challenging environment. What distinguishes supply chain management from other channel relationships? This paper presents a framework for differentiating between traditional systems and supply chain management systems. These characteristics are then related to the process of establishing and managing a supply chain. A particular focus of this paper is on the implications of supply chain management for purchasing and logistics.

Supply Chain Management, Partnership, and the Shipper ‐ Third Party Relationship

The paper begins with an overview of some of the forces that have shaped supply chain management and partnership relationships. Next the potential benefits and risks of involvement in supply chain management/partnership relationships are discussed from the perspective of both the shipper and the service provider (warehousers and transportation firms). Results from a major survey of shippers, warehousers and transportation providers are used to illustrate the risks and benefits. Means of minimizing the potential risks are also suggested. The paper concludes with a discussion of issues in supply chain management that would benefit from further analysis and research. These issues include determination of whether a firm should use a supply chain management approach, the management structure to use in supply chain management, and modelling supply chain management systems.

Methodology review: Clustering methods

A review of clustering methodology is presented, with emphasis on algorithm performance and the re sulting implications for applied research. After an over view of the clustering literature, the clustering process is discussed within a seven-step framework. The four major types of clustering methods can be characterized as hierarchical, partitioning, overlapping, and ordina tion algorithms. The validation of such algorithms re fers to the problem of determining the ability of the methods to recover cluster configurations which are known to exist in the data. Validation approaches in clude mathematical derivations, analyses of empirical datasets, and monte carlo simulation methods. Next, interpretation and inference procedures in cluster anal ysis are discussed. inference procedures involve test ing for significant cluster structure and the problem of determining the number of clusters in the data. The paper concludes with two sets of recommendations. One set deals with topics in clustering that would ben efit from continued research into the methodology. The other set offers recommendations for applied anal yses within the framework of the clustering process.

A Study of Standardization of Variables in Cluster Analysis

A methodological problem in applied clustering involves the decision of whether or not to standardize the input variables prior to the computation of a Euclidean distance dissimilarity measure. Existing results have been mixed with some studies recommending standardization and others suggesting that it may not be desirable. The existence of numerous approaches to standardization complicates the decision process. The present simulation study examined the standardization problem. A variety of data structures were generated which varied the intercluster spacing and the scales for the variables. The data sets were examined in four different types of error environments. These involved error free data, error perturbed distances, inclusion of outliers, and the addition of random noise dimensions. Recovery of true cluster structure as found by four clustering methods was measured at the correct partition level and at reduced levels of coverage. Results for eight standardization strategies are presented. It was found that those approaches which standardize by division by the range of the variable gave consistently superior recovery of the underlying cluster structure. The result held over different error conditions, separation distances, clustering methods, and coverage levels. The traditionalz-score transformation was found to be less effective in several situations.

A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis

Five external criteria were used to evaluate the extent of recovery of the true structure in a hierarchical clustering solution. This was accomplished by comparing the partitions produced by the clustering algorithm with the partition that indicates the true cluster structure known to exist in the data. The five criteria examined were the Rand, the Morey and Agresti adjusted Rand, the Hubert and Arabie adjusted Rand, the Jaccard, and the Fowlkes and Mallows measures. The results of the study indicated that the Hubert and Arabie adjusted Rank index was best suited to the task of comparison across hierarchy levels. Deficiencies with the other measures are noted.

Building Good Business Relationships: More than Just Partnering or Strategic Alliances?

Suggests that the concepts of partnerships and strategic alliances are increasingly emphasized in literature and “real life”, which might lead managers to believe that partnership‐style relationships, as opposed to arm′s length relationships, are necessary for a firm to compete successfully. Explores why, how, and when to establish a wide range of possible business‐to‐business relationships. The inter‐organizational relationship literature suggests six reasons for forming relationships: necessity, asymmetry, reciprocity, efficiency, stability, and legitimacy. Compares this framework with six partnership characteristics based on the partnership‐building literature: planning, sharing of benefits and burdens, extendedness, systematic operational information exchange, operating controls, and corporate culture bridge building. Suggests that firms should concentrate on how to develop “good business relationships”, which may have varying levels of partnership characteristics.