This article describes a case study concerned with modelling the price of wholesale diamonds, as part of a project to develop an online diamond auction platform. The work was extended to exploring how to develop an index that could be used to track market trends of wholesale diamond prices. The approach we used is readily generalised to defining market indices for so-called Collectables, and can provide the basis for construction of derivatives. With the burgeoning interest in new markets of collectables such as those generated by the concept of a Non-Fungible Token, it is reasonable to suppose that there will be concomitant increasing interest in developing derivatives for these markets.

Abstract For more than three decades, the International Statistical Institute has been actively promoting Business and Industrial Statistics, initially through its Committee structure (Committee for Statistics in Industry, which evolved into the Statistics in Business and Industry Committee) and then, since 2007, as one of its Associations, the International Society for Business and Industrial Statistics (ISBIS). This article surveys this history and the evolution of its products and services, most notably the IBSIS journal Applied Stochastic Models in Business and Industry (ASMBI) and y ‐BIS, the young statisticians' component of ISBIS.

World University Ranking (WUR) systems play a significant role in how universities are funded and whom they can attract as faculty and students. Yet, for the purpose of comparing universities as institutions of higher education, current systems are readily gamed, provide little guidance about what needs to be improved, and fail to allow for the diversity of stakeholder needs in making comparisons. We suggest a list of criteria that a WUR system should meet, and which none of the current popular systems appears satisfy. By using as a starting point the goal of creating value for the diverse and sometimes competing stakeholder requirements for a university, we suggest via a thought experiment a rating process that is consistent with all the criteria, and a way in which it might be trialled. Also, the resulting system itself adds value for individual users by allowing them to tune it to their own particular circumstances. However, an answer to the simple question ‘Which is the best university’ may well be: there is no simple answer.

Given the high level of global mobility, pandemics are likely to be more frequent, and with potentially devastating consequences for our way of life. With COVID-19, Australia is in relatively better shape than most other countries and is generally regarded as having managed the pandemic well. That said, we believe there is a critical need to start the process of learning from this pandemic to improve the quantitative information and related advice provided to policy makers. A dispassionate assessment of Australia’s health and economic response to the COVID-19 pandemic reveals some important inadequacies in the data, statistical analysis and interpretation used to guide Australia’s preparations and actions. For example, one key shortcoming has been the lack of data to obtain an early understanding of the extent of asymptomatic and mildly symptomatic cases or the differences across age groups, occupations or ethnic groups. Minimising the combined health, social and economic impacts of a novel virus depends critically on ongoing acquisition, integration, analysis, interpretation and presentation of a variety of data streams to inform the development, execution and monitoring of appropriate strategies. The article captures the essential quantitative components of such an approach for each of the four basic phases, from initial detection to post-pandemic. It also outlines the critical steps in each stage to enable policy makers to deal more efficiently and effectively with future such events, thus enhancing both the social and the economic welfare of its people. Although written in an Australian context, we believe most elements would apply to other countries as well.

An invaluable feature of the approach developed by AT&T to providing superior value to their customers has been the best-practice process that drove the cycle of continuous improvement, based on the concept of a value tree. This process has since lent itself readily to the task of creating superior value for other stakeholders. Culture and its various categories such as safety culture or risk culture are key drivers of value for several different stakeholder groups. The purpose of this article is to show how the same stakeholder value management process, with a judicious adaptation of a value tree, works well when applied to the task of managing culture, and so opens up new pathways for managers to explore in the endless pursuit of business improvement. A comparative analysis demonstrates how it improves on other current methods in widespread use, in particular avoiding a shortcoming in the wide range of methods deriving from the Safety Awareness Questionnaire.

Performance measures permeate our lives, whether or not we are aware of them.They can support or frustrate what we are trying to do, help or hinder enterprises going about their business, encourage or distort behaviors, clarify or confuse purpose.We illustrate some of the consequences of poor performance measurement, explore some of the reasons why poor metrics are in use, and describe a systematic way to look for performance measures in a variety of settings.There are real opportunities and challenges awaiting an inquiring and creative data scientist.

Bill van Zwet's contributions to his profession are, in their own way, comparable to his very substantial research, research supervision and teaching contributions. He had several leadership roles in professional societies, edited leading theoretical journals, started up or revived important conference series, founded a research center and played a huge role in providing colleagues in Central and Eastern Europe access to the western world of probability and statistics. And to each of these several activities, he brought farsightedness, energy and wit, qualities that shone through when he was interviewed about what he'd done (Statist. Sci. 24 (2009) 87-115). In fact, because Bill provided a wealth of detail in "the interview," this article focuses more on the recollections of some of the people with whom he interacted.

Net Promoter Score, touted as the “single customer metric you need” and calculated from customers' answer to one simple question about their loyalty, has been in use since 2003 and adopted in a wide variety of settings. However, it has not lived up to its claimed benefits. This article evaluates the NPS approach in terms of its positive and negative results. This article is for people interested in NPS, still considering implementing NPS in their company, or interested in its technical underpinnings. It points out the benefits and shortcomings and explains why, and it describes what can be done to achieve the outcomes NPS theory claimed it would produce, but has not. The article is written in two parts for quite distinct audiences: firstly, for executives and managers who need customer data and information to make marketing decisions; and secondly, for market researchers, statisticians, and business analysts who are responsible for capturing and providing reliable, understandable, and meaningful customer data to the executives and managers who need the information. Consequently, the two sections are written in two different styles. The first section takes the form of a summary for managers and executives of our findings and recommendations in language aimed at business leaders; the second section provides a detailed analysis and critical review of NPS for market researchers, statisticians, and business analysts. Both sections present a better solution than NPS for understanding what customers value, delivering the best value to customers, winning market share, and creating truly loyal customers.

SummaryThe paper describes a comprehensive approach to problems of performance measurement that can be used to tackle a wide range of situations, including designing monthly board and leadership reports in enterprises, assessing research quality and monitoring the efficiency and effectiveness of government programmes. It provides a review of various methods for tackling these problems and outlines some current areas of research. Although technical statistical issues are buried somewhat below the surface, statistical thinking is very much part of the main line of argument, meaning that performance measurement should be an area attracting serious attention from statisticians.

This article describes an approach to introducing strategic planning to an enterprise, motivated by a primary focus on creating value for key stakeholders of the enterprise. Two key consequences of the approach are that the efforts of the enterprise are aligned in a coherent fashion to achieve targeted external stakeholder impact and that successful deployment of the strategic plan can be assessed explicitly in terms of external stakeholder value. The approach, which was developed initially in the context of strategic planning for professional societies, has been adopted in introducing strategic planning to university departments and would appear to have more general applicability to commercial enterprises.

Net-Promoter Score (NPS) is now ubiquitous as an easily-collected market research metric, having displaced many serious market research processes. Unfortunately, this has been its sole success. It possesses few, if any, of the characteristics that might be regarded as highly desirable in a high-level market research metric; on the contrary, it has done considerable damage both to companies and to their customers.

This article describes an approach to planning, monitoring and evaluating research collaborations based on a structured approach to eliciting and measuring the value that each partner seeks to derive from the collaboration. During the phase of formulating the collaborative arrangements, the process can bring clarity to the initial expectations of each partner and so, possibly avoid prospective difficulties from the outset. During the course of the collaboration, it provides a means of assessing where improvements in the relationship might be needed. And at the end of the project, it provides a basis for assessing what has worked well, and what might need to be considered carefully in a future collaboration. The process also provides a basis for benchmarking collaborative ventures, based on ratings associated with the critical drivers of successful partnerships. Part of the process is studied in the context of the formation of a collaboration relating to cyber security research.

Jerome H. Friedman was born in Yreka, California, USA, on December 29, 1939. He received his high school education at Yreka High School, then spent two years at Chico State College before transferring to the University of California at Berkeley in 1959. He completed an undergraduate degree in physics in 1962 and a Ph.D. in high-energy particle physics in 1968 and was a post-doctoral research physicist at the Lawrence Berkeley Laboratory during 1968–1972. In 1972, he moved to Stanford Linear Accelerator Center (SLAC) as head of the Computation Research Group, retaining this position until 2006. In 1981, he was appointed half time as Professor in the Department of Statistics, Stanford University, remaining half time with his SLAC appointment. He has held visiting appointments at CSIRO in Sydney, CERN and the Department of Statistics at Berkeley, and has had a very active career as a commercial consultant. Jerry became Professor Emeritus in the Department of Statistics in 2007. Apart from some 30 publications in high-energy physics early in his career, Jerry has published over 70 research articles and books in statistics and computer science, including co-authoring the pioneering books Classification and Regression Trees and The Elements of Statistical Learning. Many of his publications have hundreds if not thousands of citations (e.g., the CART book has over 21,000). Much of his software is incorporated in commercial products, including at least one popular search engine. Many of his methods and algorithms are essential inclusions in modern statistical and data mining packages. Honors include the following: the Rietz Lecture (1999) and the Wald Lectures (2009); election to the American Academy of Arts and Sciences (2005) and the US National Academy of Sciences (2010); a Fellow of the American Statistical Association; Paper of the Year ( JASA 1980, 1985; Technometrics 1998, 1992); Statistician of the Year (ASA, Chicago Chapter, 1999); ACM Data Mining Lifetime Innovation Award (2002), Emanuel & Carol Parzen Award for Statistical Innovation (2004); Noether Senior Lecturer (American Statistical Association, 2010); and the IEEE Computer Society Data Mining Research Contribution Award (2012). The interview was recorded at his home in Palo Alto, California during 3–4 August 2012.

Summary Jae Chang Lee was born in Chang‐Nyoung, South Korea, on 9 August, 1942. After high school, he attended Seoul National University during a period of military dictatorship. As a leader of the student protest movement, he was forced to flee the country. He completed undergraduate and graduate studies at Ohio State University (PhD 1972) in USA and then joined the faculty at Moravian College in Pennsylvania. He returned permanently to South Korea in 1981 to a faculty position at Korea University. He has held visiting positions at the US National Bureau of Standards, the US Naval Coastal Systems Center, Ohio State University, Temple University, the University of Wollongong and Chuo University. Professional contributions include President of International Statistical Institute, Organiser of the International Statistical Institute (ISI) Congress in Seoul in 2001, President of the International Association for Statistical Computing and of the Korean Statistical Society (KSS), Editor, Journal of the Korean Statistical Society and Co‐Editor, Computational Statistics and Data Analysis . Honours include ISI Methorst Award, Distinguished Service Award (KSS), Presidential Award for Statistical Contribution and Order of Service Merit from the Korean Government, Certificate of Distinguished Contribution from the Japanese Society of Computational Statistics and elected member, Korean Academy of Science and Technology. The interview was conducted during 23–27 April 2013, at Jae C's country home in Pocheon City.

Stat, a new statistical journal designed for rapid communication of interesting and novel research, was launched in August 2012. During its first year of operation, 21 articles were published on a wide variety of topics and with theory inspired by a diverse range of applications. Additionally, an associated blog, StatBlog, was established with its own panel of Associate Editors to enable rapid and timely discussion of published articles.This report and an associated post on StatBlog reflect briefly on the establishment of the journal, and on current and emerging issues relating to publication of statistical research. Copyright © 2013 John Wiley & Sons, Ltd.

It’s a commonly held adage that successful businesses are good for the community. What is not always so well understood is that successful communities are good for business.Richard Pratt