Human Rights in the Ecumenical Agenda since the WCC’s formation : historical perspectives

Paper presented at the Conference on Christian Perspectives on Human Dignity and Human Rights held in Wuppertal (Germany) online from 9–12 April 202

Managing university records in the world of governance

Purpose: The purpose of the research reported here was to investigate the relationship between corporate governance and records management in the context of higher education in Sub-Saharan Africa Design/methodology/approach: This is qualitative research taking the form of a collective case study of six institutions Findings: That good records management can and does contribute to effective corporate governance and accountability. However, this relationship is not necessarily present in all circumstances Research limitations/implications: That further corporatisation in higher education is likely to be supported by, and result in, better records management Originality/value: The paper proposes governance record keeping as an approach to managing records and documents in the world of governance, audit and ris

Depreciation

Regulation of public utilities in the form in which we know it today is a development of the past fifteen years. The New York and Wisconsin laws in 1907 practically mark the beginning of present methods of regulation. Under these and other laws passed since that time regulation has become less and less a matter of bargaining and of local politics and has come to be more and more a matter of fitting the requirements of regulation to the cost of doing the business. Among these costs that of meeting the loss occasioned by the retirement of property has been by no means the least important. A literature all its own has grown up around the subjects of depreciation, the physical phenomenon, its causes, and the importance of provision for it. Much that was written on the subject was evidently the result of a decidely imperfect understanding—both of the physical questions involved and of its financial and accounting aspects. I will not attempt to do more than speak briefly of some aspects of the accounting problem

Holistic analysis of the effect on electricity cost in South Africa’s platinum mines when varying shift schedules according to time-of-use tariffs

In the past the cost of electricity was not a significant concern and was not common practice for mining companies to consider peak time-of-use (TOU) tariffs for their shift schedules. It has become more prevalent, as TOU tariffs continue increasing, to consider energy saving important. A study was carried out to analyse the mining operation of a South African deep-level platinum mine in respect of integrated load management, shift changes and TOU schedules. This was achieved by thoroughly analysing energy consumers, mine operational schedules and their interconnectedness. A specific mining system was analysed as a case study and a maximum savings scenario was determined, using the methodology formulated. The maximum savings scenario schedule change resulted in a 1.3% cost reduction. System improvements had an additional potential reduction effect of 8.4%, which was primarily the result of a reduction in compressors’ power consumption. The implications of the proposed schedule adjustments necessitated a realistic scenario. The realistic scenario had an effective financial reduction of 0.7%. The realistic schedule change, however, opened the door for large system operational improvements, which could increase the reduction potential by 7.6%. The study methods described illustrate the potential implications of integrated load management and operational schedule optimisation on the power demand and cost savings in the mining industry, specifically focusing on deep-level platinum mines

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operate within the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable. On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tactical and operational planning. In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions. More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure. Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand that a facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge for investors and operators of rail inter-modal terminals. We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand

Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data

Modelling and forecasting port throughput enables stakeholders to make efficient decisions ranging from management of port development, to infrastructure investments, operational restructuring and tariffs policy. Accurate forecasting of port throughput is also critical for long-term resource allocation and short-term strategic planning. In turn, efficient decision making enhances the competitiveness of a port. However, in the era of big data we are faced with the enviable dilemma of having too much information. We pose the question: is more information always better for forecasting? We suggest that more information comes at the cost of more parameters of the forecasting model that need to be estimated. We compare multiple forecasting models of varying degrees of complexity and quantify the effect of the amount of data on model forecasting accuracy. Our methodology serves as a guideline for practitioners in this field. We also enjoin caution that even in the era of big data more information may not always be better. It would be advisable for analysts to weigh the costs of adding more data: the ultimate decision would depend on the problem, amount of data and the kind of models being used