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Tooling is a common component of an industrial product’s manufacture. Specific tooling is devised to serve the fabrication of a particular product, while generic tooling can be used in the manufacture of multiple products. In the latter case, companies are confronted with the problem of fairly allocating the indirect costs of the tooling. This article studies how to allocate costs of generic tooling to single production orders.

Ten allocation methods (AMs) are described that are in principle suited to the distribution of generic tooling costs to production orders. Since the presented methods have for the most part been discussed in differing contexts, we apply them to a specified generic tooling problem for comparison. Evaluation of the various methods is based on 16 criteria. Reasoning is supported by a computational Monte Carlo simulation. Furthermore, we suggest using the Analytical Hierarchy Process (AHP) to elaborate one final proposition concerning the most preferable allocation scheme.

The article reports the single allocation rules’ performances for different allocation scenarios. The described characteristics refer to fairness, efficiency, and simplicity as well as to empty-core performance. Using AHP analysis allows for the aggregation of the rules’ criteria ratings. Thus, especially suitable allocation schemes for the problem at hand are identified.

An allocation is required for budgeting reasons and also for the definition of projects’ bottom-up sales prices. Selecting the “right” AM is important, as a suboptimal AM can result in unfair allocation vectors, which will act as incentives to stop using the common resource, potentially leading to higher total costs.

Research on the comparison of AMs is typically performed for certain purposes, such as enterprise networks, horizontal cooperative purchasing scenarios, or municipal service units. This article will augment the research evaluating AMs by introducing a novel set of evaluation criteria and by providing an in-depth comparison of AMs suited for the allocation of generic tooling costs.

Introduction: The Covid-19 pandemic wreaked havoc on the organisations in the form of increased job demands which manifested through increased workload, time pressure, etc. Similarly, stress and burnout engulfed the employees. Remote work became the new normal post-pandemic. Remote workers require more engagement. This has brought Artificial Intelligence (AI) to the forefront for engaging employees in the new normal.

Purpose: With limited studies on AI-enabled employee engagement in the new normal, this study investigates and proposes a conceptual framework of employee engagement in the context of AI and its impact on organisations.

Methodology: A systematic review and meta-synthesis method is undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes.

Findings: Since many organisations are turning to remote work post-pandemic and remote work requires more engagement, organisations are investing in AI to boost employee engagement in the new normal. Several antecedents of employee engagement such as quality of work life, diversity and inclusion, and communication are facilitated by AI. AI helps enhance the quality of work life by playing a major role in providing fair compensation, safe and healthy working conditions, immediate opportunity to use and develop human capacities, continued growth and security, work and total life space, and social relevance of work life. This has led to positive organisational outcomes like increased productivity, employee well-being, and decreased attrition rate. Furthermore, AI helps in measuring employee engagement. The various tools of AI, such as wearable technology, digital biomarker, neural network, data mining, data analytics, machine learning (ML), natural language processing (NLP), etc., have gone a long way in engaging employees in the new normal.

The few management accounting pricing methods in the management accounting literature are ineffective in helping small firms use their idle capacity during lingering economic recessions, and some of these methods may even worsen this problem.

Extending the traditional break-even-cost-volume-profit model, we derive a more effective pricing method, the break-even-full-capacity-utilization (BEFCU) model, to handle this problem. Seeking full capacity utilization, the BEFCU model has two characteristics: (a) highlighting the importance of the exigent fixed cost category for utilizing idle capacity and (b) using a functional cost structure that focuses on a hierarchy of value drivers in the firm's value creation process. Accordingly, under the BEFCU model, management has an instrumental pricing continuum extending from the minimum acceptable BEFCU sale price to the regular sale price.

To demonstrate its practicality, the authors apply the BEFCU model to an actual job shop. This model integrates certain strategies based on built-in flexibility in commitments with suppliers and customers and maintaining a mode of conservatism in accounting for plant assets. The model can also help small tooling companies currently seeking entrance into China; it may take a while for these companies to gain a foothold in this new market because copyrights and other legalities are rarely enforced (Bunkley, 2004).

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

The purpose of this chapter is to introduce the capability map that addresses the potential of transactional Shared Service Centers (SSCs). The mapping approach represents a heuristic logic that provides means for analyzing SSC operation, connects SSCs capabilities with their value, and supports academics and practitioners in developing a transactional SSC that is of strategic importance.

This chapter reports on findings from a longitudinal case study within an organization that has implemented a transactional Human Resource (HR) SSC. Over a period of three years, several formal and informal meetings were attended, more than 20 interviews were conducted with SSC MT and customers, over 500 pages of project documentation and memos were studied, which allowed after integration for an in-depth analysis of how resources are bundled to build different types of capabilities.

We uncovered and mapped the operational and dynamic capabilities of a transactional SSC, their role in value creation, and their interdependencies. While the operational capabilities enable the HR SSC to provide day-to-day services to take care of individual end-users and support the business, the dynamic capabilities enable transformation of HR delivery throughout the organization and increase HR’s strategic contribution.

One limitation of this study is the extent to which the capabilities and their role in value creation are generalizable to transactional non-HR SSCs. SSCs providing services that cover other business functions might develop and deploy different capabilities. The use of a capability map is not limited to the capabilities uncovered in this study, however.

In the literature, the primary focus regarding transactional services is limited to cost savings and efficiency. This chapter addresses the potential of the transactional SSC and introduces the capability map as a tool to leverage its potential.

The core idea behind lean is maximizing customer value and companies’ competitiveness through the streamlining and optimization of its main processes. Lean means creating more value with fewer resources. Thanks to its positive impact on companies’ overall performance, the lean concept is widely adopted across various industries and many organizations. Before being an operational tool, it is a management culture to be disseminated at all levels and applied with patience and conviction. Lean is about continuously improving work processes, purposes, and people. Instead of trying to hold total control of work processes and keep the spotlight, lean management encourages shared responsibility and shared leadership. Lean management was not created in a moment and still is evolving gradually, thanks to the desire of people for continuous improvement.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.