The need for a more authoritative approach to investment decision-making and cost control has been a requirement of office spending for many years now. The commercial offices find itself in an increasingly demanding position to allocate its budgets as wisely and prudently as possible. The significant percentage of total spending on buildings demands a more accurate and adaptable method of achieving quality of service within the constraints on the budgets. By adoption of life cycle costing techniques with risk management, practitioners have the ability to make accurate forecasts of likely future running costs. This thesis presents a novel framework (Artificial Neural Networks and probabilistic simulations) for modelling of operating and maintenance historical costs as well as economic performance measures of LCC. The methodology consisted of eight steps and presented a novel approach to modelling the LCC of operating and maintenance costs of two sustainable commercial office buildings. Finally, a set of performance measurement indicators were utilised to draw inference from these results. Therefore, the contribution that this research aimed to achieve was to develop a dynamic LCC framework for sustainable commercial office buildings, and by means of two existing buildings, demonstrate how assumption modelling can be utilised within a probabilistic environment. In this research, the key themes of risk assessment, probabilistic assumption modelling and stochastic assessment of LCC has been addressed. Significant improvements in existing LCC models have been achieved in this research in an attempt to make the LCC model more accurate and meaningful to estate managers and high-level capital investment decision makers A new approach to modelling historical costs and forecasting these costs in sustainable commercial office buildings is presented based upon a combination of ANN methods and stochastic modelling of the annual forecasted data. These models provide a far more accurate representation of long-term building costs as the inherent risk associated with the forecasts is easily quantifiable and the forecasts are based on a sounder approach to forecasting than what was previously used in the commercial sector. A novel framework for modelling the facilities management costs in two sustainable commercial office buildings is also presented. This is not only useful for modelling the LCC of existing commercial office buildings as presented here, but has wider implications for modelling LCC in competing option modelling in commercial office buildings. The processes of assumption modelling presented in this work can be modified easily to represent other types of commercial office buildings. Discussions with policy makers in the real estate industry revealed that concerns were held over how these building costs can be modelled given that available historical data represents wide spending and are not cost specific to commercial office buildings. Similarly, a pilot and main survey questionnaire was aimed at ascertaining current level of LCC application in sustainable construction; ranking drivers and barriers of sustainable commercial office buildings and determining the applications and limitations of LCC. The survey result showed that respondents strongly agreed that key performance indicators and economic performance measures need to be incorporated into LCC and that it is important to consider the initial, operating and maintenance costs of building when conducting LCC analysis, respondents disagreed that the current LCC techniques are suitable for calculating the whole costs of buildings but agreed that there is a low accuracy of historical cost data.

Continuously monitoring and automatically identifying existing problems in traffic signal operation is a challenging and time-consuming task. Although data are becoming available due to the adoption of emerging detection technologies, efforts on utilizing the data to diagnose signal control are limited. The current practices of retiming signals are still periodic and based on several days of aggregated turning movement counts. This dissertation developed a framework of automatic signal operation diagnosis with the aim to support decision-making processes by assessing the signal control and identifying the signal retiming needs. The developed framework used a combination of relatively low-cost data from Wi-Fi sensors and historical signal timing records from existing signal controllers. The development involved applying multiple data matching and filtering algorithms to allow the estimation of travel times of vehicular traversals. The Travel Time Index (TTI) was then used as a measure to assess the traffic conditions of various movements. Historical signal timing records were also analyzed, and an additional signal-timing measure, referred to as the Max-out Ratio (MR), was proposed to evaluate the frequency in which the green time demand of a phase exceeded its preset value. Thresholds for the TTI and MR variables were used as a basis for the diagnosis. This diagnosis first identified the needs for assigning additional green times for individual signal phases. Further assessments were then made to determine whether or not the cycle length for the entire intersection or capacity was sufficient. The developed framework was implemented in a real-world signalized intersection and proved to be capable of identifying retiming needs, as well as providing support for the retiming process. Compared to field observations, the diagnosis results were able to reflect the signal operations of most of the movements during various time periods. Moreover, the flexibility of the developed framework allows users to select different thresholds for various movements and times of day, and thus customize the analysis to agency needs.

Density-based clustering is one of the well-known algorithms focusing on grouping samples according to their densities. In the existing density-based clustering algorithms, samples are clustered according to the total number of points within the radius of the defined dense region. This method of determining density, however, provides little knowledge about the similarities among points. Additionally, they are not flexible enough to deal with dynamic data that changes over time. The current study addresses these challenges by proposing a new approach that incorporates new measures to evaluate the attributes similarities while clustering incoming samples rather than considering only the total number of points within a radius. The new approach is developed based on the notion of Gravity where incoming samples are clustered according to the force of their neighbouring samples. The Mass (density) of a cluster is measured using various approaches including the number of neighbouring samples and Silhouette measure. Then, the neighbouring sample with the highest force is the one that pulls in the new incoming sample to be part of that cluster. Taking into account the attribute similarities of points provides more information by accurately defining the dense regions around the incoming samples. Also, it determines the best neighbourhood to which the new sample belongs. In addition, the proposed algorithm introduces a new approach to utilize the memory efficiently. It forms clusters with different shapes over time when dealing with dynamic data. This approach, called Aging, enables the proposed algorithm to utilize the memory efficiently by removing points that are aged if they do not participate in clustering incoming samples, and consequently, changing the shapes of the clusters incrementally. Four experiments are conducted in this study to evaluate the performance of the proposed algorithm. The performance and effectiveness of the proposed algorithm are validated on a synthetic dataset (to visualize the changes of the clusters’ shapes over time), as well as real datasets. The experimental results confirm that the proposed algorithm is improved in terms of the performance measures including Dunn Index and SD Index. The experimental results also demonstrate that the proposed algorithm utilizes less memory, with the ability to form clusters with arbitrary shapes that are changeable over time.

La littérature en marketing souligne l’importance du personnel en contact avec la clientèle dans le secteur des services. Nous développons une recherche ancrée en contrôle de gestion qui intègre ce résultat en analysant le lien entre (1) le contrôle de la performance opérationnelle et (2) la performance organisationnelle dans l’hôtellerie. Nous considérons plus précisément les relations entre l’assignation des droits décisionnels, le système de mesure de la performance, et le système d’incitation dédiés au personnel en contact avec la clientèle ; et leur influence sur la performance de l’organisation. Ces relations sont étudiées à la lumière du critère stratégique d’orientation marché, traduit à partir d’une étude qualitative par le concept de proposition de valeur hôtelière. Afin de tester notre modèle fondé sur la théorie de l’agence et sur la base d’une étude menée auprès des hôteliers en PACA, nous utilisons une méthodologie quantitative, l’approche PLS. Les résultats conduisent, d’une part, à valider les liens de complémentarité entre les trois composantes du design organisationnel ; et d’autre part, à rejeter les hypothèses liées à l’existence de relations entre (1) le design organisationnel et la performance organisationnelle, et (2) la proposition de valeur et le design organisationnel. D’un point de vue global, ces résultats sont cohérents avec littérature, et montrent la nécessité de considérer l’assignation des droits de décision lors de l’étude des systèmes de contrôle de gestion. Ils offrent un éclairage sur le secteur des services en montrant l’importance de la mesure non financière dans les pratiques d’incitation aux niveaux inférieurs de la hiérarchie. / The marketing literature indicates that employees in contact with the customers in the service sector are prominent. This research in management accounting considers this result and investigates the relations between the control of operational performance and the organizational performance in the hotel industry. Specifically, we are interested in the relations between assignment of decision rights, performance measurement system and incentive system dedicated to employees in contact with customers, and we consider the effects of this organizational design on organizational performance. These relations are studied in light of the strategic concept of market orientation, this one being operationalized through a qualitative study of the concept of hotel value proposition. In order to test our agency model, we use a PLS approach on the basis of an empirical study of hotels in PACA. Our results indicate support to the hypothesized positive relations between assignment of decision rights and use of incentive systems, and between use of incentive systems and non-Financial measurement. We find no support to the hypotheses that organizational design is positively related to organizational performance, and that hotel value proposition influences organizational design. Overall, these results are consistent with prior literature indicating that assignment of decision rights and management control systems are complementary choices. Furthermore, they provide insights on the specificities of service activities, as they indicate the importance of non-Financial performance measurement in incentive systems at the worker level.

The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture- and motion-prediction. Thus, this thesis investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do. With respect to motion prediction, one of the most challenging opportunities for improvement concerns computation speed. Especially, when considering dynamic motion prediction, the underlying optimization problems can be large and computationally complex. Even though the current optimization-based tools for predicting human posture are relatively fast and accurate and thus do not require as much improvement, posture prediction in general is a more tractable problem than motion prediction and can provide a test bead that can shed light on potential issues with motion prediction. Thus, we investigate the use of ANN with posture prediction in order to discover potential issues. In addition, directly using ANN with posture prediction provides a preliminary step towards using ANN to predict the most appropriate combination of performance measures (PMs) - what drives human behavior. The PMs, which are the cost functions that are minimized in the posture prediction problem, are typically selected manually depending on the task. This is perhaps the most significant impediment when using posture prediction. How does the user know which PMs should be used? Neural networks provide tools for solving this problem. This thesis hypothesizes that the ANN can be trained to predict human motion quickly and accurately, to predict human posture (while considering external forces), and to determine the most appropriate combination of PM(s) for posture prediction. Such capabilities will in turn provide a new tool for studying human behavior. Based on initial experimentation, the general regression neural network (GRNN) was found to be the most effective type of ANN for DHM applications. A semi-automated methodology was developed to ease network construction, training and testing processes, and network parameters. This in turn facilitates use with DHM applications. With regards to motion prediction, use of ANN was successful. The results showed that the calculation time was reduced from 1 to 40 minutes, to a fraction of a second without reducing accuracy. With regards to posture prediction, ANN was again found to be effective. However, potential issues with certain motion-prediction tasks were discovered and shed light on necessary future development with ANNs. Finally, a decision engine was developed using GRNN for automatically selecting four human PMs, and was shown to be very effective. In order to train this new approach, a novel optimization formulation was used to extract PM weights from pre-existing motion-capture data. Eventually, this work will lead to automatically and realistically driving predictive DHMs in a general virtual environment.

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This study addresses in an exploratory fashion the following questions. 1. Would non-monetary performance measures grouped into a statement of public efforts and accomplishments significantly reduce the uncertainty of decision makers concerning past entity performance? 2. Would knowledge of such data alter their resultant budgetary decisions?

Purpose: The purpose of this study is to create a brief, but comprehensive process for developing KPIs independent of context. Bridging the current gap in the literature for a process that can be applied in any industry or situation and include instructions for each of the steps in the process. To fulfill the purpose, the following research question is answered: Independent of industry and application, how could a process for organizations that are interested in developing new KPIs be structured? Method: This is an abductive qualitative study that combine extant literature with empirical data from both in-depth analysis of a single case company as well as interviews with companies from different industries. The literature was analyzed through a content analysis. Thematical analysis was utilized for analyzing the interviews. A tentative process for new KPI development synthesized from the theory and empirical findings was applied in the case company to identify a process that is applicable in a practical setting. Findings: The findings consist of a process for developing new KPIs with (1) six activities that companies should undertake, (2) six critical success factors, one for each activity, they need to consider, and (3) actions and tools to utilize in each activity. Implications: This study contributes to the literature by combining the currently wide array of research into a brief but comprehensible and applicable process that can be used independent of context. For practitioners, this study contributes by creating an understanding of how to efficiently develop new KPIs that enables the whole organization to achieve the strategy and goals. Limitations and future research: There are two main limitations in this study; the process is only applied at one company and the timeframe inhibited the ability to study the effects of the developed KPIs. Hence, future research should investigate if the process is valid in other contexts as well as study the effects of the KPIs being developed through the process. / Syfte: Syftet med denna studie är att skapa en koncis och kontextoberoende process for utveckling av nya KPIer. Detta överbryggar det nuvarande gapet i litteraturen för en process som innehåller detaljerade instruktioner och kan appliceras i vilken industri och situation helst. Följande forskningsfråga besvaras för att uppfylla syftet: Oberoende av industri och användning, hur kan en process för organisationer som vill utveckla nya KPIer vara strukturerad? Metod: Detta är en abduktiv, kvalitativ studie som kombinerar nuvarande litteratur med empirisk data från både en djupgående analys av ett företag samt flertalet intervjuer med företag från olika industrier. Litteraturen analyserades med en innehållsanalys. En tematisk analys nyttjades för att analysera intervjuerna. En tentativ process för utveckling av nya KPIer syntetiserad från teoretiska och empiriska resultat applicerades i ett företag för att identifiera en process som var applicerbar i praktiken. Resultat: Resultatet består av en process för utveckling av nya KPIer med (1) sex aktiviteter företag bör utföra, (2) sex kritiska framgångsfaktorer, en för varje aktivitet, de behöver ta hänsyn till och (3) steg och verktyg att nyttja i varje aktivitet.  Bidrag: Denna studie bidrar till litteraturen genom att kombinera det nuvarande breda spektrumet av studier till en omfattande men koncis och applicerbar process som kan användas oberoende av kontext. Studiens praktiska bidrag är att processen skapar en förståelse för hur företag effektict kan utveckla nya KPIer som möjliggör att företagets strategi och mål uppnås.  Begränsningar och framtida forskning: Studien har två huvudsakliga begränsningar: processen är enbart applicerad i ett företag och tidsramen förhindrade undersökning av de utvecklade KPIernas effekt. Därför vore det värdefullt om framtida forskning studerade processens användning i fler industrier och situationer samt vilken effekt KPIer utvecklade med processen har.

Resource efficiency has become an important topic due to dramatic increase of world-population and globalization. A company that is not able to efficiently utilize its resources is less sustainable. It produces at higher cost as well as with higher environmental impact. Especially small and medium-sized enterprises are mostly not able to deal with these kinds of issues in addition to their daily business. Their characteristics differentiate them from large companies and require approaches that respond to their specific needs. Not only technological aspects but also organizational problems as well as lack of methodology are reasons for low resource efficiency in small companies. This thesis therefore focuses on developing a tool which facilitates the increase of resource efficiency particularly for small and medium-sized enterprises.To start with, this thesis investigates the meaning of resource efficiency and the basics of performance measures as well as special characteristics of small and medium-sized enter-prises. Afterwards, measures are designed in order to quantify resource efficiency. A case study is carried out as well to investigate how to ensure applicability of a supporting tool in small and medium-sized enterprises from different industries. In the final part, a computer-based tool is developed which facilitates the assessment of resource efficiency potentials using 12 resource efficiency measures, a consistent approach and a ranking method.Keywords: Resource efficiency, performance measures, small and medium-sized enterprises / <p>QC 20121101</p>

The purpose of this study is to examine the nature of performance measures utilised by the maintenance function in todays business organisations. In the process, the increasing variety and significance of these measures are addressed from operational and strategic perspectives. A survey-based research method was utilised to gather the research data. Several statistical procedures were utilised to analyse the data. The findings of this study point to the multifaceted nature of the maintenance measures and measurement. Multiple categories of maintenance measures were identified. These categories varied from the machine-specific, to measures impacting organisational performance. The relative lack of emphasis placed on the environment and strategic facets of maintenance is noted. The findings of this study have direct implications to organisations, which are attempting to measure the effectiveness of their maintenance efforts. The need to align the maintenance performance efforts with the organisational strategic direction is emphasised. In this context, the integration of the maintenance performance information systems with the overall organisational performance management information system might facilitate the needed alignment. This study utilises 120 maintenance measures. As such, it represents a comprehensive view of the maintenance effort.