Smart manufacturing: a framework for managing performance

Chapter 4 - Analysis of factors influencing smart and sustainable manufacturing systems using a multi-criteria decision making tool

Since the publication of the initial American College of Cardiology (ACC)/American Heart Association (AHA) Methodology for the Selection and Creation of Performance Measures,1 there has been an explosion in the development and application of performance measures. Although initially envisioned as a means for physician-led quality-improvement efforts, performance measures have been primarily used as tools for accountability and performance-based reimbursement instead. Given the centrality of and experience with performance measures for quantifying healthcare quality, the American College of Cardiology Foundation (ACCF)/AHA Task Force on Performance Measures sought to update its methodology so that ongoing efforts to measure performance could benefit from emerging insights. The original methodology, proposed in 2005,1 remains the foundation for developing process performance measures. The principal recommendations of the 2005 report are summarized in Table 1. The 2010 report does not address detailed issues of analysis,3 pay for performance,4 or nonfinancial rewards for better performance5 because these topics have been addressed in other statements. The focus of the 2010 report is to provide a state-of-the-art perspective on the construction, collection, and emerging directions of performance measurement as a means to improve healthcare quality. View this table: Table 1. ACCF/AHA Attributes of Performance Measures Figure 1. An overview of the steps in providing care by domain. Reprinted from Spertus et al.1 Performance measures that articulate discrete processes of care, as opposed to structural aspects of care or outcomes, are distinctly different from both clinical practice guidelines and appropriate use criteria because they represent a subset of the clinical guidelines for which the evidence is sufficiently strong: typically where the highest-quality evidence of benefit unequivocally exceeds risk (Class I recommendation, Level of Evidence: A),8 failure to provide the therapy to an eligible patient meaningfully reduces the likelihood that the patient will experience the best possible outcome. In this report, …

This study examines the links between financial performance and executive compensation for high-performance companies (HPC). HPC display sustained and superior cash flow returns, asset growth, and total shareholder returns. In previous empirical analysis, HPC companies displayed specific identifiable financial performance drivers and measures when compared to companies in the S&P 500 (Needles et al., 2004). Most recently, HPC sustained their high performance when compared to the S&P 500 over varied economic periods. Further, the research identified operating asset management characteristics of these companies, especially as they relate to the cash cycle (Needles et al., 2004). Continuing this stream of research, this study first identifies the financial and non-financial performance measures related to compensation of top management of HPC as reported in the companies’ public disclosures. Then, these findings for HPC are matched to a set of comparable non-HPC. Finally, we evaluate the stated performance measures for executive compensation in light of the performance drivers and measures identified by previous research to be distinguishing characteristics of HPC. We hypothesize that HPC will more closely align stated performance measures for executive compensation with performance characteristics that have been shown to be characteristics of HPC. We find that HPC are more focused and unambiguous in their use of both financial and non-financial performance measures in executive compensation.

Context-aware workflow management for smart manufacturing: A literature review of semantic web-based approaches

The purpose of this paper is to collect and structure the various features of Smart Manufacturing (SM). Researchers have previously identified various characteristics and technologies of Smart Manufacturing System (SMS); this paper collects, discusses and merges some of those characteristics and technologies available in the current body of knowledge. In the future, it is expected that this selection of characteristics and technologies will help to compare and distinguish other initiatives like Industry 4.0, smart factory, intelligent manufacturing, distributive manufacturing, etc. which are frequently used synonymous with SM. The result of this paper is a comprehensive list of characteristics and technologies that are associated with a SMS. As many of the listed items show variating overlaps, certain technologies and characteristics are merged and clustered. This results in a set of five defining characteristics and ten technologies that are considered relevant for a SMS. The authors hope to provide a basis for a broad and interdisciplinary discussion within the SM community about the defining technologies and characteristics of a SMS.

Condition monitoring systems conduct important roles in manufacturing process lines by monitoring condition status of machines to prevent accident faults of them. Those systems need to be improved in their functionalities by adapting more intellectual, self-reconfigurable, and self-decisional mechanisms for supporting smart manufacturing operations. Whereas, many enterprises in manufacturing industry have in trouble with well-designing these smart condition monitoring systems for fitting their own purpose in their specific domains. In this paper, we propose a systems engineering approach to designing smart condition monitoring systems. The systems engineering is powerful methodology to design complex systems in many multi-disciplinary domains, so we have tailored the systems engineering processes for designing the system architecture of our system-of-interest (i.e. smart condition monitoring systems). The deliverables of this research work can contribute to provide a technical solution for well-designing other smart manufacturing systems in several industrial domains. In the end, our research works can resolve the market domains such as high-productivity and production of high-quality products in manufacturing process lines.

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the self-organized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.

PurposeAmidst the complicated nature of the UAE’s facilities management (FM) industry, the need to recalibrate the existing performance measurement (PM) system measures and criteria has been resonating to ensure their ability to capture the FM industry trends and dynamics, thus enhancing organizational excellence. Therefore, this research aimed to propose a specific PM tool to the country’s FM industry to accurately assess performance and establish strategic enhancements.Design/methodology/approachThe study reviewed literature on the available PM systems to gather the available measures, which were presented to a focus group of seven participants, who were purposively selected based on their expertise in FM and PM implementation in the UAE to adjust them and add ones relevant to the UAE’s FM industry.FindingsThe focus group conducted various changes, from retaining certain measures and criteria, renaming them to simplify or make them more representative of the industry, ranking them based on their importance to limit their numbers, to finally categorizing them as enablers or results. Consequently, the final proposed tool was composed of nine dimensions with 51 measures as performance enablers and three dimensions with 11 measures as performance results. Seven measures were added by the experts, who highlighted their increasing popularity in the UAE’s FM industry.Originality/valueThrough addressing the critical void in literature, this paper develops a specific PM tool aligning with the intricacy of the UAE’s FM industry, thus providing proactive contribution to the industry’s effective and sustainable growth.

Diabetes mellitus performance measures in individuals with limited life expectancy.

A framework for human-centered production planning and control in smart manufacturing

This research explores how child protection is constructed through performance measurement, and how the selection and reporting of performance indicators influences policy and practice. Performance measurement is now a mainstay of public sector management. At one level, it has a role in addressing ever-present questions about the quality and effectiveness of child protection services. However, there are concerns about assessing performance within a management framework, rather than a professional or client-centred framework. Therefore, the thesis aims to critically engage with the problems and potential of performance measurement in child protection with a view to maximising its capacity to promote better outcomes for children and families. It focuses particularly on the role of family support, because despite its proclaimed importance in legislation and policy, in practice family support work remains marginalised, situated 'outside' core child protection work. The research approaches performance measurement from a critical policy perspective, in which indicators have both technical and policy functions. While performance measurement may be viewed rationally as a means for government to manage resources, from a broader policy perspective the way it is conceptualised and implemented has major consequences for service delivery. The policy significance of performance measurement is explored through two studies. The first study analyses developments in policy and practice in three jurisdictions: England, Victoria (Australia) and Queensland (Australia). This analysis contrasts the prominent position of family support in child protection legislation and policy, with its marginal position in both practice and performance measurement. It outlines the socio-political context for performance measurement in child protection, drawing out the values and power relations embedded in performance indicators that give them a role in communicating policy intent. The study concludes that performance measurement in all three jurisdictions is directing policy attention (and therefore resources) towards investigation and out-of-home care, largely ignoring family support. The second study moves beyond the policy critique to more concrete concerns about how to reconstruct performance measurement in child protection to encompass family support indicators. Using a survey method, it seeks to describe and analyse the practice of family support in one jurisdiction (Queensland) according to a performance measurement model of inputs, outputs, processes and outcomes. It concludes that useful performance data can be collected to aid understanding of the contribution of family support to the overall child protection system. Such data will help to move family support beyond assumptions about its value, towards empirical scrutiny of its methods and results. It suggests that family support will command greater policy attention if included in the child protection performance measurement effort. The thesis concludes that viewed from a critical policy perspective, performance measurement can be harnessed in the interests of good practice. It can assist with the repositioning of family support, moving it from the realm of unproblematic moral goodness to more critical inquiry. It argues there is nothing inherently wrong with measuring effectiveness and efficiency, but it is important to examine the manner in which performance measurement is developed and implemented, its methods and consequences. Performance indicators are not neutral or merely technical. Establishing indicators can influence how social problems such as child abuse and neglect are defined, where resources are placed, what service types are funded, and how outcomes for children and families are defined. It questions the privilege performance indicators may be given within managerial approaches to government, as if they somehow provide an objective and unbiased account of how services are performing. However, this is not to suggest that performance indicators are worthless or meaninglessmrather that their meaning should be explicit, and their underpinning value stances debated within policy processes. Such debate will enhance the congruency between child protection research and indicators, and democratise the processes by which performance measures are developed, used and reported.

The Industrial robots are widely used in smart manufacturing environments to improve productivity and automation, but their increasing energy consumption has become a critical issue. Traditional time-based control methods update control signals at fixed sampling intervals, which often leads to actuator redundancy and energy inefficiency, especially in steady-state or low-dynamic operating modes. To address this issue, this paper proposes an energy-efficient event-based control strategy for energy-efficient operation of industrial robots in smart manufacturing systems. The proposed approach integrates an event-based mechanism with nonlinear robot control so that control adjustments are made only when tracking accuracy or energy-related metrics exceed specified thresholds. This design significantly reduces unnecessary control signals while ensuring satisfactory trajectory tracking accuracy. A Lyapunov function-based analysis is performed to demonstrate the stability of the closed-loop control system using the proposed event-based method. Furthermore, the triggering mechanism guarantees a strictly positive lower bound for the intervals between events, thereby eliminating the Zeno effect and ensuring practical feasibility. The energy efficiency of the proposed strategy is further investigated by analyzing the behavior of the actuators and the overall energy consumption. Experimental results show that the proposed approach achieves significant energy savings compared to traditional time-based control methods without compromising control performance. This highlights its potential for energy-efficient operation of industrial robots in smart manufacturing applications.

In this paper, the authors proposed a set-theoretic model of the functioning of Smart Manufacturing (SM) system. As part of the work, an analysis of existing scientific works in the subject area was carried out. We focus on the description of the model of SM functioning as a cyber-physical system, as a result of which the physical and informational level of the model functioning are considered. The conditions and tasks for the SM functioning are defined, various types of representation of the information space are considered, and the system of access control to elementary messages is described. As one of the components of digital production, a description of the logistic system was proposed and the main parameters of its functioning were determined.

PurposeThe intent of microfinance institutes (MFIs) in developing countries is to provide loans to very poor people in order to help them transform their lives. MFIs tend to receive subsidies; sustainability is being sought to free MFIs from non‐market dependencies. Sustainability is expected to be achieved with “best practices,” of which management with performance measures is a component. The purpose of this paper is to examine the use of performance measures by three Kenyan MFIs, which are classified as formal and client based, and likely to use rational and explicit performance measures. Clients in these MFIs are placed into self‐help groups with two responsibilities: to provide mutual support and advice to the borrowing client; and to provide the MFI with a guarantee that loans of group members will be repaid.Design/methodology/approachBased on a review of the economics and performance measurement systems literatures, research questions were developed along with an interview guide. Case studies were used to administer an interview guide which was distributed to the respondents prior to the face‐to‐face interviews.FindingsThe study concludes that MFIs have relatively well‐developed performance measures that support their particular businesses. There was a good balance between the use of financial and non‐financial performance measures. However, output measures were more commonly used than process measures. The nature of the MFIs suggests the importance of performance measurement. The managers of the MFIs are concerned with performance measurement, as expected within a bureaucracy, and a top‐down demand is present. In addition, group members or clients are interested in performance measurement as each member guarantees the loans of all fellow group members who have loans with the MFI. Thus, the customers exert a bottom‐up demand for performance measurement.Originality/valueThe findings support the view that performance measures are a means for managing MFIs and are a likely requirement for sustainability. Furthermore, the findings have identified performance measures (similar to those at banks) that are appropriate for the three MFIs in Kenya. The findings are important since the identified performance measures may be adopted by other evolving MFIs in this relatively new sector. In addition, the findings contribute to a better understanding of the genesis of the less popular results and determinants performance measurement framework of Fitzgerald et al.

PurposeThis study proposes a systematic and comprehensive circular economy (CE) performance measure as an instrument to operationalize and quantify circularity. It seeks to evaluate the relative contribution of blockchain technology to evaluate various measures in this study. A general research agenda for investigating blockchain capabilities to performance assessment in CE is presented.Design/methodology/approachEmpirical survey data from 32 CE and blockchain experts are collected to inform this study. Inductive reasoning, heatmaps, and a middle-range theory building approach are utilized to generalize theoretical patterns for blockchain support of CE performance assessment and measurement. A series of propositions are then developed as a middle-range theory for the true, false, perceptible, and hidden affordances of blockchain technology capabilities for CE performance measurement.FindingsOverall, sixteen performance measures and metrics are identified and examined. The authors find most of these measures and metrics – based on literature and expert opinion – can be supported using blockchain technology capabilities. Four major blockchain capabilities, transparency and traceability, reliability and security, smart contracts, and incentivization and tokenization are shown to have varying potential support for CE performance assessment. There needs to be an evaluation of true, false, perceptible, and hidden affordances of blockchain technology capabilities for CE performance measurement in future studies.Originality/valueBlockchain application for CE, and specifically performance measurements, is a new area. Research and practice evaluation on this issue is important but needs substantially additional investigation to help CE progress. This study provides a framework for evaluation and a foundation for future research at the nexus of CE, blockchain technology, and performance measurement.