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A persistent theme in writing on personnel management has been the relative marginality of the personnel function within the management team as a whole (see Legge pp. 50–7) and Poole (p. 97). Because of its indirect contribution to the raising of profits, human resources management has tended not to be a priority for investment and the concerns and perspectives of personnel practitioners have tended to receive only fitful consideration in the forums at which strategic business decisions are taken. One response to this situation by the personnel profession has been to develop expertise in computer‐based data‐processing techniques which permit personnel managers to assume more of an accounting function. Such quantitative techniques enable personnel managers to monitor and evaluate the utilisation of human resources, arguably improving the efficiency and cost‐effectiveness of the enterprise. The potential and the purpose of this strategic response on the part of the profession, therefore, is to raise the centrality and indispensability of the personnel function, and elevate its importance within the management hierarchy (pp. 79–85).

E-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.

An approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.

The results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.

This research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

For many years the demise of the graduate recruitment scheme has been predicted, some suggesting that dynamic employers want graduates that can take substantial responsibility and contribute to bottom line profitability within months of joining an organisation. Contrary to this view, some “blue chip” (organisation that if invested in, would be considered to be very reliable and safe and among the strongest performers in its sector) employers have retained their “scheme”, believing that hand picked recruits developed in(to) the organisational “mould” are more likely to achieve the medium‐ to long‐term results so desperately sought. This paper demonstrates that one such scheme produces greater career progression among scheme recruits when compared to graduates recruited outside the scheme. Further, this paper demonstrates that one such organisation operating such a scheme actively facilitates the personal development of such scheme entrants to generate enhanced career progression as a consequence of enhanced performance. The paper concludes that scheme practices could be equally applied to non‐scheme recruits to further enhance the overall performance of the organisation.

This study examines how accounting tools and techniques are used to create and support membership and reporting boundaries for a multi-entity sustainability scheme. It also considers whether boundary setting for this initiative helps to connect corporate activity with planetary boundaries and the SDGs.

A case study of a national agrifood sustainability scheme, analysing extensive documentary data and multi-entity sustainability reports. The concept of partial organising is used to frame the analysis.

Accounting, in the form of planning, verification, target setting, annual review and reporting, can be used to create a membership and a reporting boundary. Accounting tools and techniques support the scheme's standard-setting and monitoring elements. The study demonstrates that the scheme offers innovation in how sustainability reporting is managed. However, it does not currently provide a cumulative assessment of the effect of the sector's activity on ecological carrying capacity or connect this activity to global sustainability indicators.

Future research can build on this study's insights to further develop our understanding of multi-entity sustainability reporting and accounting's role in organising for sustainability. The authors identify several research avenues including: boundary setting in ecologically significant sectors, integrating global sustainability indicators at sectoral and organisational levels, sustainability controls in multi-entity settings and the potential of multi-entity reporting to provide substantive disclosure.

This paper provides insight into accounting's role in boundary setting for a multi-entity sustainability initiative. It adds to our understanding of the potential of a multi-entity reporting boundary to support connected measurement between corporate activity and global sustainability indicators. It builds on work on partial organising and provides insight into how accounting can support this form of organising for sustainability.

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.

Control charts have been widely used in the manufacturing industry as the most important statistical process control (SPC) technique. One assumption for successfully applying control charts is that the process is stable to begin with and adjustments are made only at a later point when the process is deemed to be out of control. A situation that often arises is when the process is subject to a slow trend caused by factors such as equipment deterioration, power decline during its consumption, or other reasons. For this type of situation, an integrated monitoring and adjustment approach can be considered and the relevant issues involved are discussed in this paper. This problem of implementing SPC is studied from the point of view of process management, together with the concept of engineering process control. A general approach is then formulated for monitoring processes with trend and subject to regular adjustments and a specific model for a general case with linear trend is studied in more detail.

This study aims to create a reliable, collaborative and sustainable business environment with suppliers of a company for providing high-quality and low-cost products on time. A supplier management system that sustains existing suppliers by sharing work based on systematic performance evaluation while developing the supplier base with potential suppliers is proposed.

Built on quantitative approaches, supplier management functions are integrated in the designed system. A quantitative strengths, weaknesses, opportunities and threats (SWOT) analysis is adapted for evaluating potential suppliers. A multi-objective integer linear programming (ILP) model is developed for the distribution of orders among selected potential and existing suppliers. A performance evaluation scheme based on an exponentially weighted moving average (EWMA) is proposed to evaluate and monitor suppliers' performance over time.

Proposed system develops a supplier base by methodically selecting and approving new suppliers, and a sustainable relationship with both new and existing suppliers is established based on performance over time. Decisions on retaining or removing suppliers from the base are objectively made by quantitative evaluations. Orders are fairly distributed among suppliers under the constraints imposed by the management. Dependence on a certain set of suppliers and its associated risks are reduced while agility in offering goods is enabled.

Business processes for selecting new suppliers, distributing orders among all suppliers, evaluating and monitoring performance over time are quantitatively integrated to add value in operational decision-making. The proposed system is original in the holistic approach for managing and sustaining multiple suppliers of a company based on performance.

Describes an initiative to refocus the appraisal, review and career planning within the Shoe and Allied Trades Research Association (SATRA). First of a two‐part article; the second part describes the implementation of the scheme and the monitoring of its effectiveness.