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Monitoring and assessment are essential in the measurement of tourism business performance. Therefore, the purpose of this chapter is to illustrate how monitoring and assessment procedures can be applied in the hospitality business.

A case study and micro examples provide a framework for the monitoring and assessment of business performance in the hospitality business.

This chapter provides reasons why the tourism business uses control measures to monitor business performance. This is complemented with practical steps in the assessment procedures and guidelines for assessments. Different types of assessment procedures together with the characteristics of performance management provide a well-rounded overview to tourism business owners on how to conduct monitoring and assessment.

Due to the explorative nature of the monitoring and assessment case study, more empirical studies are needed to investigate and test performance measurement from a developing country perspective.

Discussions from the case study support the steps and practical guidelines in the monitoring and assessment of the tourism business.

The case study offers new practices into prospective entrepreneurs’ measurement and understanding in the monitoring and assessment of business performance.

This study aims to investigate the potential influence of organisational responses to conflicting institutional demands towards barriers to environmental performance (EP) monitoring of government green procurement (GGP) in Malaysia.

The paper used a qualitative methodology based on a single case study involving policymakers, procurement officials and a monitoring authority. The study data were analysed drawing on the perspectives of organisational responses to conflicting institutional demands.

The three key challenges that hindered EP monitoring of GGP in Malaysia were policy irregularities, knowledge asymmetry and communication gaps. These challenges are likely the consequences of the acquiescence, avoidance, compromise and defiance strategies commonly used in dealing with the institutional complexity faced in Malaysia’s public policy arena.

The government, at various institutional levels, may benefit from the theoretical and empirical findings of the case study. Knowledge of barriers can facilitate the policymakers in designing the monitoring process meticulously. Meanwhile, awareness of the influence of organisational responses to institutional complexity on GGP barriers can help redefine field actors’ interests and values in improving policy monitoring. In addition, reporting of the monitored EP bridges the institutional gaps between the macro-state level and the micro-organisational level of GGP, besides increasing the government’s transparency and accountability regarding green procurement.

Fewer challenges in the EP monitoring system contribute to an improved GGP policy. In turn, an improved policy may enhance public health and reduce environmental degradation.

The study contributes to the GGP monitoring and institutional theory by showing that barriers to EP monitoring culminate from the organisational response to the institutional demands faced in the policy environment. The authors argue that this is one of the few studies that have examined the barriers to EP monitoring of public policy explicated in the context of organisational responses to institutional demands.

Quantitative metrics should be used as a risk management option whenever possible. This work proposes a framework for the risk quantification and the resulting risk-based design of control charts to monitor quality control points.

Two quality control models were considered for the risk quantification analysis. Estimated operating characteristic curves, expressing the defect rate (on a ppm basis) as a function of the sample size, process disturbance magnitude and process capacity, were devised to evaluate the maximum rate of defective product of the processes. The proposed framework applicability on monitoring critical control points in Hazard Analysis and Critical Control Point (HACCP) systems was further evaluated by Monte Carlo simulations.

Results demonstrate that the proposed monitoring systems can be tuned to achieve an admissible failure risk, conveniently expressed as the number of non-conforming items produced per million products, and these risks can be properly communicated. This risk-based approach can be used to validate critical control point monitoring procedures in HACCP plans. The expected rates of non-conforming items sent out to clients estimated through stochastic simulation procedures agree well with theoretical predictions.

The procedures outlined in this study may be used to establish the statistical validity of monitoring systems that uses control charts. The intrinsic risks of these control systems can be assessed and communicated properly in order to demonstrate the effectiveness of quality control procedures to auditing third parties.

This study provides advancements toward practical directives for the implementation of statistical process control in the food industry. The proposed framework allows the assessment and communication of intrinsic failure risks of quality monitoring systems. It may contribute to the establishment of risk-based thinking in the constitution of quality management systems.

This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone.

This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis.

The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online.

This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.

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.

This study aims to study the implementation of lean thinking at the strategic level of an Italian manufacturing company. Companies implementing continuous improvement (CI) projects in their production processes often take the monitoring phase for granted. This research deploys an A3 lean thinking project in the monitoring phase of strategic KPIs upon completion of several ongoing improvement projects.

The research methodology is action research aiming at disseminating the problems that the company is facing. The study relies on the lean action plan developed by Womack and Jones (2003): Planning for lean and Lean action. Lean planning consists of the following steps: find a change agent; get the knowledge; find a lever. Lean action uses the A3 lean approach.

The company reached high-performance improvements due to the proposed lean action plan.

This study contributes by presenting a lean action plan in the monitoring phase, highlighting the importance of the lean thinking-monitoring continuum in reducing time waste for faster diagnosis and using action research to analyze and instill reflective learning.

The research relies on the A3 methodology to showcase the benefits that a mature paradigm, often coined to production, still has unexplored potentials.

This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart.

To improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics.

For this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand.

The application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart.

The implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control.

The OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.

This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development.

Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied.

The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%.

An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

The purpose of this paper is to demonstrate the application of the control chart procedure to monitor the characteristics whose profile over time resembles a set of connected line segments.

Fit a regression spline model by taking the subgroup average of the characteristic as response variable and time as the explanatory variable. Then monitor the response variable using the regression spline control chart with the fitted model as center line and upper and lower control limits at three standard deviation units of the response variable above and below the center line.

The proposed chart is successfully deployed to monitor the daily response time profile of a client server of an application support process. The chart ensured the stability of the process as well as detected the assignable cause leading to the slowing down of the server performance.

The methodology can be used to monitor any characteristics whose performance profile over time resembles a set of connected line segments. Some of the examples are the consumption profile of utility providers like power distribution companies, usage profiles of telecom networks, loading profile of airline check-in process, e-commerce websites, etc.

To the best of the author’s knowledge, construction of control charts using regression spline is new. The usage of the control chart to monitor the performance characteristics which exhibits a nonlinear profile over time is also rare.

This paper explores through Schumacher's perspective on ‘the proper use of land’: the reasons for, and the means and consequences of, monitoring soil condition in managing agricultural landscapes sustainably. This particular perspective illustrates its argument with soil monitoring initiatives operating at various scales within the global agricultural context. Schumacher's land management goals are health, beauty and permanence, yet productivity is the goal most land managers focus on. The chosen indicators for soil monitoring need to reflect these goals. Hence, the indicators of choice for monitoring soil condition are attributes that can be: easily measured, improve soil productivity or protect the soil. Often attributes that have intrinsic ‘beauty’ (value), maintain ‘health’ (function) in ecosystems and are difficult to measure are ignored as soil condition indicators. The usefulness of information gained through monitoring soil condition is to make decisions that will be relevant for varied audiences and at different points in the decision-making process.