Stochastic optimization of staffing for multiskill call centers

Dans cette thèse, nous étudions le problème d’optimisation des effectifs dans les centres d’appels, dans lequel nous visons à minimiser les coûts d’exploitation tout en offrant aux clients une qualité de service (QoS) élevée. Nous introduisons également l'utilisation de contraintes probabilistes qui exigent que la qualité de service soit satisfaite avec une probabilité donnée. Ces contraintes sont adéquates dans le cas où la performance est mesurée sur un court intervalle de temps, car les mesures de QoS sont des variables aléatoires sur une période donnée. Les problèmes de personnel proposés sont difficiles en raison de l'absence de forme analytique pour les contraintes probabilistes et doivent être approximées par simulation. En outre, les fonctions QoS sont généralement non linéaires et non convexes. Nous considérons les problèmes d’affectation personnel dans différents contextes et étudions les modèles proposés tant du point de vue théorique que pratique. Les méthodologies développées sont générales, en ce sens qu'elles peuvent être adaptées et appliquées à d'autres problèmes de décision dans les systèmes de files d'attente. La thèse comprend trois articles traitant de différents défis en matière de modélisation et de résolution de problèmes d'optimisation d’affectation personnel dans les centres d'appels à compétences multiples. Les premier et deuxième article concernent un problème d'optimisation d'affectation de personnel en deux étapes sous l'incertitude. Alors que dans le second, nous étudions un modèle général de programmation stochastique discrète en deux étapes pour fournir une garantie théorique de la consistance de l'approximation par moyenne échantillonnale (SAA) lorsque la taille des échantillons tend vers l'infini, le troisième applique l'approche du SAA pour résoudre le problème d’optimisation d'affectation de personnel en deux étapes avec les taux d’arrivée incertain. Les deux articles indiquent la viabilité de l'approche SAA dans notre contexte, tant du point de vue théorique que pratique. Pour être plus précis, dans le premier article, nous considérons un problème stochastique discret général en deux étapes avec des contraintes en espérance. Nous formulons un problème SAA avec échantillonnage imbriqué et nous montrons que, sous certaines hypothèses satisfaites dans les exemples de centres d'appels, il est possible d'obtenir les solutions optimales du problème initial en résolvant son SAA avec des échantillons suffisamment grands. De plus, nous montrons que la probabilité que la solution optimale du problème de l’échantillon soit une solution optimale du problème initial tend vers un de manière exponentielle au fur et à mesure que nous augmentons la taille des échantillons. Ces résultats théoriques sont importants, non seulement pour les applications de centre d'appels, mais également pour d'autres problèmes de prise de décision avec des variables de décision discrètes. Le deuxième article concerne les méthodes de résolution d'un problème d'affectation en personnel en deux étapes sous incertitude du taux d'arrivée. Le problème SAA étant coûteux à résoudre lorsque le nombre de scénarios est important. En effet, pour chaque scénario, il est nécessaire d'effectuer une simulation pour estimer les contraintes de QoS. Nous développons un algorithme combinant simulation, génération de coupes, renforcement de coupes et décomposition de Benders pour résoudre le problème SAA. Nous montrons l'efficacité de l'approche, en particulier lorsque le nombre de scénarios est grand. Dans le dernier article, nous examinons les problèmes de contraintes en probabilité sur les mesures de niveau de service. Notre méthodologie proposée dans cet article est motivée par le fait que les fonctions de QoS affichent généralement des courbes en S et peuvent être bien approximées par des fonctions sigmoïdes appropriées. Sur la base de cette idée, nous avons développé une nouvelle approche combinant la régression non linéaire, la simulation et la recherche locale par région de confiance pour résoudre efficacement les problèmes de personnel à grande échelle de manière viable. L’avantage principal de cette approche est que la procédure d’optimisation peut être formulée comme une séquence de simulations et de résolutions de problèmes de programmation linéaire. Les résultats numériques basés sur des exemples réels de centres d'appels montrent l'efficacité pratique de notre approche. Les méthodologies développées dans cette thèse peuvent être appliquées dans de nombreux autres contextes, par exemple les problèmes de personnel et de planification dans d'autres systèmes basés sur des files d'attente avec d'autres types de contraintes de QoS. Celles-ci soulèvent également plusieurs axes de recherche qu'il pourrait être intéressant d'étudier. Par exemple, une approche de regroupement de scénarios pour atténuer le coût des modèles d'affectation en deux étapes, ou une version d'optimisation robuste en distribution pour mieux gérer l'incertitude des données.In this thesis, we study the staffing optimization problem in multiskill call centers, in which we aim at minimizing the operating cost while delivering a high quality of service (QoS) to customers. We also introduce the use of chance constraints which require that the QoSs are met with a given probability. These constraints are adequate in the case when the performance is measured over a short time interval as QoS measures are random variables in a given time period. The proposed staffing problems are challenging in the sense that the stochastic constraints have no-closed forms and need to be approximated by simulation. In addition, the QoS functions are typically non-linear and non-convex. We consider staffing optimization problems in different settings and study the proposed models in both theoretical and practical aspects. The methodologies developed are general, in the sense that they can be adapted and applied to other staffing/scheduling problems in queuing-based systems. The thesis consists of three articles dealing with different challenges in modeling and solving staffing optimization problems in multiskill call centers. The first and second articles concern a two-stage staffing optimization problem under uncertainty. While in the first one, we study a general two-stage discrete stochastic programming model to provide a theoretical guarantee for the consistency of the sample average approximation (SAA) when the sample sizes go to infinity, the second one applies the SAA approach to solve the two-stage staffing optimization problem under arrival rate uncertainty. Both papers indicate the viability of the SAA approach in our context, in both theoretical and practical aspects. To be more precise, in the first article, we consider a general two-stage discrete stochastic problem with expected value constraints. We formulate its SAA with nested sampling. We show that under some assumptions that hold in call center examples, one can obtain the optimal solutions of the original problem by solving its SAA with large enough sample sizes. Moreover, we show that the probability that the optimal solution of the sample problem is an optimal solution of the original problem, approaches one exponentially fast as we increase the sample sizes. These theoretical findings are important, not only for call center applications, but also for other decision-making problems with discrete decision variables. The second article concerns solution methods to solve a two-stage staffing problem under arrival rate uncertainty. It is motivated by the fact that the SAA version of the two-stage staffing problem becomes expensive to solve with a large number of scenarios, as for each scenario, one needs to use simulation to approximate the QoS constraints. We develop an algorithm that combines simulation, cut generation, cut strengthening and Benders decomposition to solve the SAA problem. We show the efficiency of the approach, especially when the number of scenarios is large. In the last article, we consider problems with chance constraints on the service level measures. Our methodology proposed in this article is motivated by the fact that the QoS functions generally display ``S-shape'' curves and might be well approximated by appropriate sigmoid functions. Based on this idea, we develop a novel approach that combines non-linear regression, simulation and trust region local search to efficiently solve large-scale staffing problems in a viable way. The main advantage of the approach is that the optimization procedure can be formulated as a sequence of steps of performing simulation and solving linear programming models. Numerical results based on real-life call center examples show the practical viability of our approach. The methodologies developed in this thesis can be applied in many other settings, e.g., staffing and scheduling problems in other queuing-based systems with other types of QoS constraints. These also raise several research directions that might be interesting to investigate. For examples, a clustering approach to mitigate the expensiveness of the two-stage staffing models, or a distributionally robust optimization version to better deal with data uncertainty

Strategic Nurse Allocation Policies Under Dynamic Patient Demand

ABSTRACT STRATEGIC NURSE ALLOCATION POLICIES UNDER DYNAMIC PATIENT DEMAND by Osman T. Aydas The University of Wisconsin-Milwaukee, 2017 Under the Supervision of Professor Anthony D. Ross Several studies have shown a strong association between nurse staffing and patient outcomes. When a nursing unit is chronically short-staffed, nurses must maintain an intense pace to ensure that patients receive timely care. Over time this can result in nurse burnout, as well as dissatisfied patients and even medical errors. Improved accuracy in the allocation of nursing staff can mitigate these operational risks and improve patient outcomes. Nursing care is identified as the single biggest factor in both the cost of hospital care and patient satisfaction. Yet, there is widespread dissatisfaction with the current methods of determining nurse staffing levels, including the most common one of using minimum nurse-to-patient ratios. Nurse shortage implications go beyond healthcare quality, extending to health economics as well. In addition, implementation of mandatory nurse-to-patient ratios in some states creates a risk of under- or over-estimating required nurse resources. With this motivation, this dissertation aims to develop methodologies that generate feasible six-week nurse schedules and efficiently assign nurses from various profiles to these schedules while controlling staffing costs and understaffing ratios in the medical unit. First, we develop and test various medium-term staff allocation approaches using mixed-integer optimization and compare their performance with respect to a hypothetical full information scenario. Second, using stochastic integer programming approach, we develop a short-term staffing level adjustment model under a sizable list of patient admission scenarios. We begin by providing an overview of the organization of the dissertation. Chapter 1 presents the problem context and we provide research questions for this dissertation. Chapter 2 provides a review of the literature on nurse staffing and scheduling specifically from the Operations Management journals. We introduce the challenges of nursing care and nurse scheduling practices. We identify major research areas and solution approaches. This is followed by a discussion of the complexities associated with computing nursing requirements and creating rosters. Staffing requirements are the result of a complex interaction between care-unit sizes, nurse-to-patient ratios, bed census distributions, and quality-of-care requirements. Therefore, we review the literature on nursing workload measurement approaches because workloads depend highly on patient arrivals and lengths of stay, both of which can vary greatly. Thus, predicting these workloads and staffing nurses accordingly are essential to guaranteeing quality of care in a cost-effective manner. For completeness, a brief review of the literature on workforce planning and scheduling that is linked to the nurse staffing and scheduling problem is also provided. Chapter 3 develops a framework for estimating the daily number of nurses required in Intensive Care Units (ICUs). Many patient care units, including ICUs, find it difficult to accurately estimate the number of nurses needed. One factor contributing to this difficulty is not having a decision support tool to understand the distribution of admissions to healthcare facilities. We statistically evaluate the existing staff allocation system of an ICU using clinical operational data, then develop a predictive model for estimating the number of admissions to the unit. We analyze clinical operational data covering 44 months for three wards of a pediatric ICU. The existing staff allocation model does not accurately estimate the required number of nurses required. This is due in part to not understanding the pattern and frequency of admissions, particularly those which are not known 12 hours in advance. We show that these “unknown” admissions actually follow a Poisson distribution. Thus, we can more accurately estimate the number of admissions overall. Analytical predictive methods that complement intuition and experience can help to decrease unplanned requirements for nurses and recommend more efficient nurse allocations. The model developed here can be inferred to estimate admissions for other intensive care units, such as pediatric facilities. Chapter 4 examines an integrated nurse staffing and scheduling model for a Pediatric Intensive Care Unit (PICU). This model is targeted to recommend initial staffing plans and schedules for a six-week horizon given a variety of nurse groups and nursing shift assignment types in the PICU. Nurse rostering is an NP-hard combinatorial problem, which makes it extremely difficult to efficiently solve life-sized problems due to their complexity. Usually, real problem instances have complicated work rules related to safety and quality of service issues, as well as preferences of the personnel. To avoid the size and complexity limitations, we generate feasible nurse schedules for the full-time equivalent (FTE) nurses, using algorithms that will be employed in the mixed-integer programming models we develop. Pre-generated schedules eliminate the increased number of constraints, and reduce the number of decision variables of the integrated nurse staffing and scheduling model. We also include a novel methodology for estimating nurse workloads by considering the patient, and individual patient’s acuity, and activity in the unit. When the nursing administration prepares the medium-term nurse schedules for the next staffing cycle (six weeks in our study), one to two months before the actual patient demand realizations, it typically uses a general average staffing level for the nursing care needs in the medical units. Using our mixed-integer optimization model, we examine fixed vs. dynamic medium-term nurse staffing and scheduling policy options for the medical units. In the fixed staffing option, the medical unit is staffed by a fixed number of nurses throughout the staffing horizon. In the dynamic staffing policy, we propose, historical patient demand data enables us to suggest a non-stationary staffing scheme. We compare the performance of both nurse allocation policy options, in terms of cost savings and understaffing ratios, with the optimal staffing scheme reached by the actual patient data. As a part of our experimental design, we evaluate our optimization model for the three medical units of the PICU in the “as-is” state. In Chapter 5, we conduct two-stage short-term staffing adjustments for the upcoming nursing shift. Our proposed adjustments are first used at the beginning of each nursing shift for the upcoming 4-hour shift. Then, after observing actual patient demand for nursing at the start of the next shift, we make our final staffing adjustments to meet the patient demand for nursing. We model six different adjustment options for the two-stage stochastic programming model (five options available as first-stage decisions and one option available as the second-stage decision). Because the adjustment horizon is less than 12 hours, the current patient census, patient acuity, and the number of scheduled admissions/discharges in the current and upcoming shift are known to the unit nurse manager. We develop a two-stage stochastic integer programming model which will minimize total nurse staffing costs (and the cost of adjustments to the original schedules developed in the medium-term planning phase) while ensuring adequate coverage of nursing demand. Chapter 6 provides conclusions from the study and identify both limitations and future research directions

Operations and Maintenance Optimization of Stochastic Systems: Three Essays

This dissertation presents three essays on topics related to optimally operating and maintaining systems that evolve randomly over time. Two primary areas are considered: (i) joint staffing and pricing strategies for call centers that use co-sourcing to improve service operations and reduce costs; and (ii) optimally maintaining stochastically degrading systems when either multiple systems are associated via a common environment, or when a single-unit system is maintained using a population of heterogeneous spare parts. First we present a queueing and stochastic programming framework for optimally staffing a call center utilizing co-sourced service capacity. The interplay between the call center and external service provider is modeled as a leader-follower game in which the call center, acting as the follower, solves a two-stage stochastic integer program. The problem is reformulated as a quadratically-constrained linear program to obtain the optimal contract prices and the optimal staffing problem yields a closed-form solution. Numerically we demonstrate that significant cost reductions can be achieved, even in the presence of imperfect and asymmetric information. Second the problem of optimally replacing multiple stochastically degrading systems using condition-based maintenance is considered. Properties of the optimal value function and policy motivate a tractable, approximate model with state- and action-space transformations and a basis-function approximation of the action-value function. It is demonstrated that near optimal policies are attainable and significantly outperform heuristics. Finally, we consider the problem of optimally maintaining a stochastically degrading system using spares of varying quality. Conditions are provided under which the optimal value function exhibits monotonicity and the optimal policy is characterized. Numerically we demonstrate the utility of our proposed framework, and provide insights into the optimal policy as an exploration-exploitation type policy

Workflow Analysis, Scheduling, and Chance Constraint Models in Community Pharmacy Operations

Community pharmacy networks provide most of the US population's prescribed medication, but not picking up the medication or using it improperly can lead to problems such as medication non-adherence and medication misuse. This research focuses on improving community pharmacy network services by proposing a change in the role pharmacists play in these networks. A key task pharmacists perform which is a critical step in the medication dispensing process is verifying that the medication filled is the one prescribed and that it does not conflict with other medications the patient is taking. This dissertation proposes that pharmacists provide important counseling services (i.e. PDPC services) to patients inside community pharmacies. We discuss how adding PDPC services changes the workflow of a community pharmacy and discuss strategies to overcome obstacles preventing pharmacists from providing PDPC services. We use a Discrete Event Simulation (DES) model to simulate a local community pharmacy as well as a community pharmacy network to evaluate strategies that can be used to either improve the workflow process internally (internal strategies) or provide an external resource that can be used to provide support to the pharmacy (external strategies). The internal strategies studied are adding a staff member, predicting prescription pick up times, and providing short duration PDPC services in busy hours. The external strategies studied are utilizing a central fill to dispense part of the pharmacy's demand and adding PDPC kiosks to provide PDPC services inside the pharmacy. The effect of each strategy and the extent of its benefits are studied and highlighted in chapters 2 & 3. The central fill location problem was modeled as a chance constraint stochastic P-median capacitated facility location problem. Three extensions to the location model are added and discussed in detail. Several lower bounds were provided to the problem and an efficient solution method was used to solve the problem. Finally the model was applied to a community pharmacy network in PA in a case study. The results showed that ignoring the highest demand scenarios can save the community pharmacy network from having to add an additional central fill

Optimisation stochastique de problèmes d’ordonnancement en santé

RÉSUMÉ : Les problèmes d'ordonnancement en santé sont complexes, car ils portent sur la fabrication d'ordonnancements qui absorbent les perturbations survenant dans le futur. Par exemple, les nouveaux patients urgents ont besoin d’être intégrés rapidement dans le planning courant. Cette thèse s'attaque à ces problèmes d'ordonnancement en santé avec de l'optimisation stochastique afin de construire des ordonnancements flexibles. Nous étudions en premier lieu la fabrication d'horaires pour deux types d’équipes d’infirmières: l’équipe régulière qui s'occupe des unités de soins et l’équipe volante qui couvre les pénuries d’infirmières à l’hôpital. Quand les gestionnaires considèrent ce problème, soit ils utilisent une approche manuelle, soit ils investissent dans un logiciel commercial. Nous proposons une approche heuristique simple, flexible et suffisamment facile à utiliser pour être implémentée dans un tableur et qui ne requiert presque aucun investissement. Cette approche permet de simplifier le processus de fabrication et d'obtenir des horaires de grande qualité pour les infirmières. Nous présentons un modèle multi-objectif, des heuristiques, ainsi que des analyses pour comparer les performances de toutes ces méthodes. Nous montrons enfin que notre approche se compare très bien avec un logiciel commercial (CPLEX), peut être implémentée à moindre coût, et comble finalement le manque de choix entre les solutions manuelles et les logiciels commerciaux qui coûtent extrêmement cher. Cette thèse s'attaque aussi à l'ordonnancement des chirurgies dans un bloc opératoire, fonctionnant avec un maximum de deux chirurgiens et de deux salles, en tenant compte de l'incertitude des durées d'opérations. Nous résolvons en premier lieu une version déterministe, qui utilise la programmation par contraintes, puis une version stochastique, qui encapsule le programme précédent dans un schéma de type ``sample average approximation''. Ce schéma produit des plannings plus robustes qui s’adaptent mieux aux variations des durées de chirurgies. Cette thèse présente le problème de prise de rendez-vous en temps réel dans un centre de radiothérapie. La gestion efficace d'un tel centre dépend principalement de l'optimisation de l'utilisation des machines de traitement. En collaboration avec le Centre Intégré de Cancérologie de Laval, nous faisons la planification des rendez-vous patients en tenant compte de leur priorité, du temps d'attente maximale et de la durée de traitement, le tout en intégrant l'incertitude reliée à l'arrivée des patients au centre. Nous développons une méthode hybride alliant optimisation stochastique et optimisation en temps réel pour mieux répondre aux besoins de planification du centre. Nous utilisons donc l'information des arrivées futures de patients pour dresser le portrait le plus fidèle possible de l'utilisation attendue des ressources. Des résultats sur des données réelles montrent que notre méthode dépasse les stratégies typiquement utilisées dans les centres. Par la suite, afin de proposer un algorithme stochastique et en temps réel pour des problèmes d'allocation de ressources, nous généralisons et étendons la méthode hybride précédente. Ces problèmes sont naturellement très complexes, car un opérateur doit prendre dans un temps très limité des décisions irrévocables avec peu d'information sur les futures requêtes. Nous proposons un cadre théorique, basé sur la programmation mathématique, pour tenir compte de toutes les prévisions disponibles sur les futures requêtes et utilisant peu de temps de calcul. Nous combinons la décomposition de Benders, qui permet de mesurer l'impact futur de chaque décision, et celle de Dantzig-Wolfe, qui permet de s'attaquer à des problèmes combinatoires. Nous illustrons le processus de modélisation et démontrons l’efficacité d'un tel cadre théorique sur des données réelles pour deux applications: la prise de rendez-vous et l'ordonnancement d'un centre de radiothérapie, puis l'assignation de tâches à des employés et leur routage à travers l’entrepôt.----------ABSTRACT : Scheduling problems are very challenging in healthcare as they must involve the production of plannings that absorb perturbations which arise in the future. For example, new high-priority patients needs to be quickly added in the computed plannings. This thesis tackles these scheduling problems in healthcare with stochastic optimization such as to build flexible plannings. We first study the scheduling process for two types of nursing teams, regular teams from care units and the float team that covers for shortages in the hospital. When managers address this problem, they either use a manual approach or have to invest in expensive commercial tool. We propose a simple heuristic approach, flexible and easy enough to be implemented on spreadsheets, and requiring almost no investment. The approach leads to streamlined process and higher-quality schedules for nurses. %improves both the process and the quality of the resulting schedule. The multi-objective model and heuristics are presented, and additional analysis is performed to compare the performance of the approach. We show that our approach compares very well with an optimization software (CPLEX solver) and may be implemented at no cost. It addresses the lack of choice between either manual solution method or a commercial package at a high cost. This thesis tackles also the scheduling of surgical procedures in an operating theatre containing up to two operating rooms and two surgeons. We first solve a deterministic version that uses the constraint programming paradigm and then a stochastic version which embeds the former in a sample average approximation scheme. The latter produces more robust schedules that cope better with the surgeries' time variability. This thesis presents an online appointment booking problem for a radiotherapy center. The effective management of such facility depends mainly on optimizing the use of the linear accelerators. We schedule patients on these machines taking into account their priority for treatment, the maximum waiting time before the first treatment, and the treatment duration. We collaborate with the Centre Intégré de Cancérologie de Laval to determine the best scheduling policy. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop a hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. We use information on the future arrivals of patients to provide an accurate picture of the expected utilization of resources. Results based on real data show that our method outperforms the policies typically used in treatment centers. We generalize and extend the previous hybrid method to propose a general online stochastic algorithm for resource allocation problems. These problems are very difficult in their nature as one operator should take irrevocable decisions with a limited (or inexistent) information on future requests and under a very restricted computational time. We propose a mathematical programming-based framework taking advantage of all available forecasts of future requests and limited computational time. We combine Benders decomposition, which allows to measure the expected future impact of each decision, and Dantzig-Wolfe decomposition, which can tackle a wide range of combinatorial problems. We illustrate the modelling process and demonstrate the efficiency of this framework on real data sets for two applications: the appointment booking and scheduling problem in a radiotherapy center and the task assignment and routing problem in a warehouse

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Optimal Dynamic Control of Queueing Networks: Emergency Departments, the W Service Network, and Supply Chains under Disruptions.

Many systems in both the service and manufacturing sectors can be modeled and analyzed as queueing networks. In such systems, control and design is often an important issue that may significantly affect the performance. This dissertation focuses on the development of innovative techniques for the design and control of such systems. Special attention is given to real-world applications in (a) the design and control of patient flow in the hospital emergency departments, (b) design and control of service/call centers, and (c) the design and control of supply chains under disruption risks. With respect to application (a), using hospital data, analytical models, and simulation analyses we show how (1) better patient prioritization, (2) enhanced triage systems, and (3) improved patient flow designs allow emergency departments to significantly improve their performance with respect to both operational efficiency and patient safety. Regarding application (b), we give specific attention to a two-server and three-demand class network in the shape of a ``W'' with random server disruption and repair times. Studying this network, we show how effective control and design strategies that efficiently make use of (partial) flexibility of servers can be implemented to achieve high performance and resilience to server disruptions. In addition to establishing stability properties of different known control mechanisms, a new heuristic policy, termed Largest Expected Workload Cost (LEWC), is proposed and its performance is extensively benchmarked with respect to other widely used polices. Regarding application (c), we demonstrate how supply chains can boost their performance using better control and design strategies that efficiently take into account supply disruption risks. Motivated by several real-world examples of disruptions, production flexibility, and supply contracts within supply chains, we model the informational and operational flexibility approaches to designing a resilient supply chain. By analyzing optimal ordering policies, sourcing strategies, and the optimal levels of back-up capacity reservation contracts, various disruption risk mitigation strategies are considered and compared, and new insights into the design of resilient supply chains are provided.PHDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/94002/1/soroush_1.pd

ICT-driven interactions: on the dynamics of mediated control

Interactions driven by Information Communications Technologies (ICT) have gained significant acceptance and momentum in contemporary organisational settings, this is illustrated by their massive adoption and varied deployment across the various levels of an organisation’s hierarchy. ICTs such as mobile telephones, Personal Digital Assistants (PDA), videoconferencing, BlackBerries and other forms of portable and immovable computing technologies provide enduring bases for mediated interactions in human activities. This thesis looks into the dynamics of ICT-driven interactions and, distinctively, focuses on the manifestations and implications of mediated control in a collaborative environment. The study draws on the concept of administrative behaviour which leads to the observation that the nature of mediated control is not static, but evolutionarily dynamic that springs from highly unpredictable contexts of work. Thus, interactions driven by ICTs influence and change the dynamics of mediated control against the background of the rhythm, structure and direction of an organisation’s purposeful undertakings. Findings indicate, quite paradoxically, that networks set up through the instrumentality of technology mediated interaction discourage domination and inspire individual discretion in spite of their promise of electronic chains. The analysis reflects the notion that mediated control is not only about the predetermination of targets that are attained at the subordinate level. Indeed, the study advocates a fundamental conceptualisation of mediated control as double-sided concept, integrating the use of discretion that, occasionally, makes subordinates drive and initiate key control techniques that steer organisational life. Therefore, through the application of philosophical hermeneutics for a rigorous data interpretation, this study develops an innovative and holistic understanding of mediated control which not only adds to, but also extends, the current organisational perception of control by the incorporation of discretion and, in the process, makes a distinctive contribution to scholarship