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This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Aims to measure syllable aggregation consistency of Romanized Chinese data in the title fields of bibliographic records. Also aims to verify if the term frequency distributions satisfy conventional bibliometric laws.

Uses Cooper's interindexer formula to evaluate aggregation consistency within and between two sets of Chinese bibliographic data. Compares the term frequency distributions of polysyllabic words and monosyllabic characters (for vernacular and Romanized data) with the Lotka and the generalised Zipf theoretical distributions. The fits are tested with the Kolmogorov‐Smirnov test.

Finds high internal aggregation consistency within each data set but some aggregation discrepancy between sets. Shows that word (polysyllabic) distributions satisfy Lotka's law but that character (monosyllabic) distributions do not abide by the law.

The findings are limited to only two sets of bibliographic data (for aggregation consistency analysis) and to one set of data for the frequency distribution analysis. Only two bibliometric distributions are tested. Internal consistency within each database remains fairly high. Therefore the main argument against syllable aggregation does not appear to hold true. The analysis revealed that Chinese words and characters behave differently in terms of frequency distribution but that there is no noticeable difference between vernacular and Romanized data. The distribution of Romanized characters exhibits the worst case in terms of fit to either Lotka's or Zipf's laws, which indicates that Romanized data in aggregated form appear to be a preferable option.

Provides empirical data on consistency and distribution of Romanized Chinese titles in bibliographic records.

Compulsory airtightness testing was introduced for new dwellings in England and Wales in 2006 and in Scotland in 2010 to ensure that they are constructed according to design air permeability targets. These targets are set to limit heat loss through air infiltration. Previous work examining the large Air Tightness Testing and Measurement Association (ATTMA) data set of UK airtightness test data suggested that, in a proportion of dwellings, the targets were being met by post-completion sealing as opposed to airtight construction, but did not quantify the prevalence of this practice. The paper aims to discuss this issue.

In this paper, the distribution of as-built airtightness and the proportion of dwellings undergoing post-completion sealing are estimated from the ATTMA data set covering 2015–2016. This is carried out by Bayesian statistical modelling, using the data set of recorded test results and a modelled representation of the testing process.

This analysis finds the mode of the as-built distribution of air permeability as 4.38 ± 0.01 m³/m²h. It predicts that 39 per cent of dwellings aiming for one of the five most common design targets have sealing interventions at the point of pressure testing to meet their target. The as-built distribution of the ATTMA data is compared to airtightness test data obtained from just before compulsory testing was introduced, showing an improvement in the modal air permeability of 3.6 m³/m²h since testing became mandatory.

This paper has investigated the available data beyond simply what is reported, to estimate what the real levels of airtightness in the UK new build stock may be.

The purpose of this monograph is to examine the types of data that are required in the design and analysis of physical distribution systems, and to establish a method for obtaining such data to successfully accomplish the objectives of a distribution study. The development of the data base includes determining: (1) data specification; (2) data collection; (3) data analysis; and (4) data validation. All of these stages in data base construction are analysed and evaluated in this paper. This discussion will probably be of most importance to those managers contemplating a computer‐oriented study; an attempt has been made in the monograph to provide a managerial as opposed to a technical overview of data base requirements.

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

Distribution concerns who gets what. But does “who” refer to the personal distribution of income among individuals or the functional distribution of income among suppliers of productive factors? For nearly 150 years, Anglophone distribution theory followed the Ricardian emphasis on functional distribution – the income shares of labor, land, and capital. Only beginning in the 1960s, and consolidated by a research outpouring in the early 1970s, does mainstream economics turn to the personal conception of distribution. This essay documents Anglophone (primarily American) economics’ move from functional to personal distribution, and tries to illuminate something of its causes and timing.

The challenge of truckload routing is increased in complexity by the introduction of stochastic demand. Typically, this demand is generalized to follow a Poisson distribution. In this chapter, we cluster the demand data using data mining techniques to establish the more acceptable distribution to predict demand. We then examine this stochastic truckload demand using an econometric discrete choice model known as a count data model. Using actual truckload demand data and data from the bureau of transportation statistics, we perform count data regressions. Two outcomes are produced from every regression run, the predicted demand between every origin and destination, and the likelihood that that demand will occur. The two allow us to generate an expected value forecast of truckload demand as input to a truckload routing formulation. The negative binomial distribution produces an improved forecast over the Poisson distribution.

The purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.

The step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.

A comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.

The sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.

The proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.

Various multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

The purpose of this paper is to propose a method for statistical inference on data from power law distributions in order to explain behavior and social phenomena associated with web‐based social spaces such as discussion forums, question‐and‐answer sites, web 2.0 applications and the like.

The paper starts by highlighting the importance of explaining behavior in social networks. Next, the power law nature of social interactions is described and a hypothetical example is used to explain why analyzing sub‐sets of data might misrepresent the relationship between variables having power law distributions. Analysis requires the use of the complete distribution. The paper proposes logarithmic transformation prior to correlation and regression analysis and shows why it works using the hypothetical example and field data retrieved from Microsoft's Netscan project.

The hypothetical example emphasizes the importance of analyzing complete datasets harvested from social spaces. The Netscan example shows the importance of the logarithmic transformation for enabling the development of a predictive regression model based on the power law distributed data. Specifically, it shows that the number of new and returning participants are the main predictors of discussion forum activity.

This paper offers a useful analysis tool for anyone interested in social aspects of the Internet as well as corporate intra‐net systems, knowledge management systems or other systems that support social interaction such as cellular phones and mobile devices. It also explains how to avoid errors by paying attention to assumptions and range restriction issues.

In the past few years significant changes in computer technology have taken place. The development of the mini and micro computer for business applications has occurred in the past five years. New mass storage devices, faster central processing units and more sophisticated input/output devices have also been developed recently. These technological developments have fostered further use of the computer in the management of physical distribution operations. This paper describes the nature of the changes in distribution operations and in the distributions manager's use of the computer to control distribution activities. The survey reported in this paper explores the changes in the use of the computer to control all distribution management activities. Comparisons are drawn with a similar survey conducted in 1972. The paper also presents more detailed information about the use of the computer as a diagnostic and planning tool than has been presented before.