48 research outputs found
Viable service systems and decision making in service management
No full textThis paper addresses decision making in the management of complex service systems,
highlighting the contribution of the viable systems approach as an interpretative and
governance methodology based on systems thinking.
In the last few decades, business management has undergone significant changes due to
rapid developments in markets. New competitive strategies and technologies have
stimulated global discussion about business models and tools (Ghoshal, 2005). The role of
relationships has become increasingly relevant in businesses, and researchers as well as
industries are shifting their focus to a service-oriented approach, moving from a paradigm
of product to one of service (IfM-IBM Cambridge SSME Report, 2008)
Integrating the internet of things and big data analytics into decision support models for healthcare management
No full textPurpose – This paper presents formulations of decision models for the innovative management of healthcare systems
through the application of the Internet of Things (IoT) and Big Data Analytics (BDA). By integrating the technology of IoT
and the intelligence of BDA we derive service designs that amplify the personalized, co-creative nature of effective health
care. Capturing and interpreting data about patients’ needs and desires, resource availabilities (doctors, nurses, medical
equipment, medical supplies and others), treatment options and potential outcomes enables a smart and adaptive health
management system for planning, scheduling and coordinating service activities in a jointly managed, co-creative system.
From a service-system point of view (Maglio et al., 2009), healthcare systems are configurations of people, information,
organizations, and technologies operating together for mutual benefit and common objectives. The traditional approaches to
the design of these service system take one of two perspectives: a patient-centric view, oriented to patient health (Polese,
2013) in terms of quality and speed of care; and a provider view, focused on resource utilization and efficiency (patient
waiting time or service level is the only consideration given to the patient - Sarno and Nenni, 2016). In the new digitization
era, the design and management of healthcare services should structurally incorporate both perspectives under the awareness
that service itself means value co-creation among the involved actors (Vargo and Lusch, 2004), personalizing the health care
experience for each patient and adapting organizational and planning processes to context variability.
Large investments in fixed assets and highly trained staffs severely limit the flexibility in capacity of every healthcare
service system. The inertia of the supply chain limits its ability to respond to the non-stationary nature of demand that is
driven by individual patient needs. Therefore, resource allocations that are typically used in other service industries to
respond to variable demand are not effective in healthcare systems. The use of IoT and BDA to generate more accurate and
dynamic updates of parameters that affect demand and resource availability enables a different approach known as demand
response (DR). Electric utilities are a good example of an inertia-constrained supply chain that uses IoT and BDA to enable
DR (Siano and Sarno, 2016). Although healthcare service systems presently do not adopt the practice of DR, the necessary
technologies are in place to do so. An essential requirement of DR is also an innovative feature of this practice – DR
requires co-creation. Through the application of finite capacity scheduling (FCS) a healthcare system can utilize real-time
data about patient locations, medical conditions and desires to dynamically assign patients and healthcare resources to
medical procedures for improved efficiency in the use of healthcare resources and the personalization of patient care. This
research formulates the key decision models that will support DR in healthcare systems and take advantage of IoT and BDA.
We identify the unique tradeoffs that DR presents to the design and management of healthcare systems, specify the data
requirements of the predictive models that are recommended, formulate the mathematical structure of the decision models
and describe the changes to the management and culture of the healthcare ecosystem that will be necessary.
Design/Methodology/approach – The formulation of adaptive, intelligent decision models for planning, scheduling and
controlling health care services will be accomplished through application of the techniques of operations research guided by
the perspective of Service Dominant Logic (SDL) and the Viable Systems Approach (VSA). Although these decision
models can trace their pedigree to classic resource planning and scheduling models of goods-dominant research, they
embody distinctive and essential features of co-creative systems. Our formulations of these models will expose these
features. The state of the art in the application of IoT and BDA in healthcare enables the acquisition of massive amounts of
detailed data from patient electronic medical records; real-time patient condition monitors; location devices for patients,
healthcare personnel, medical equipment; social networks’ comments; resource status reports and schedules; and intelligent
medical knowledge bases. Furthermore, cognitive assistants are now providing medical professionals with up-to-date
knowledge to support diagnosis and treatment. Our model formulations will be directed at defining the specific model
constructs that take advantage of the recent advances in IoT and BDA. Specifically, we will develop a hierarchy of models
that exhibit the benefits of utilizing increasing penetrations of big data sources and increasing degrees of decision adaptation
(homeostasis).
Findings – 1) How can big data inform us about the needs and desires of all of the players in the health care system? 2)
How can big data analytics be used in predicting patient needs and desires as well as the intentions of healthcare providers?
3) How can IoT enable the beneficial usage of big data for co-creative healthcare management?
Research limitations/implications - This research represents the first step of a wider research project aimed at assessing the
feasibility and the convenience of new forms of value co-creation in healthcare management. The suite of models that this
research creates will initiate a quantitative evaluation of the relative performance of the models in the suite. However, this
evaluation, which will be done via computer simulation, is outside the scope of the current paper.
Ultimately, our models will demonstrate the specific ways in which BDA and IoT can be used effectively in healthcare
management. Service innovators in the healthcare industry will be able to use the results of this stream of research to see
beyond the hype of these new technologies and learn how to leverage them effectively.
Originality/value – The advances in the engineering of IoT devices and the development of statistical methods for BDA
have been very impressive. The applications of these technologies have been heavily promoted, but there has been very
little research into the integration of IoT and BDA into model-based decision support systems. This study will be original in
its foundation in decision modeling
Editoriale Service Research Integration and Future Directions-The Naples Forum on Service
No full textManagement and marketing research in service is at a turning point, and service research is increasingly challenging existing marketing myths and questioning the validity of traditional approaches. In this mainframe, service-dominant (S-D) logic and service science, along with many-to-many marketing and the viable systems approach, have captured the attention of researchers worldwide in their attempt to advance service research. With S-D logic tracing a new cultural approach to market exchange, systems and network theory represents a set of general interpretation schemes—as insightful metamodels—to be coupled with specific theoretical models in order to better understand the complexity characterizing service science. In this light, it seems unwise to search for the “best model” or the best theoretical proposal capable of framing all marketing or management problems. More appropriately, we are convinced that researchers should try to valorize the integration of scientific approaches positioned at different theoretical levels. Despite the challenge of integrating various cultures, models, approaches, and perspectives, we sense that there is a gradual convergence toward a holistic framework in management and marketing research
S-D logic research directions and opportunities : the perspective of systems, complexity and engineering
No full textThe need for a systems approach to modeling and understanding service is now well
established, (Barile 2009; Barile and Polese 2009; Golinelli 2010; Ng et.al.,
2011a). Following the construction of Maglio et al (2009) we view a service-system
as a network of agents and interactions that integrate resources for value cocreation.
The context of value creation is intrinsic to the system design.and the
adaptive, interactive actions of agents classifies the network as an ecosystem (Lusch
et al, 2010).
To date, several disciplines have broached the systems view of service and the
engineering of service systems. Operations research applied to services began with a
rather simplistic, macro view of resource integration in the form of Data
Envelopment Analysis (DEA), introduced by Charnes, Cooper and Rhodes in
1978 (Charnes, Cooper et al. 1994, Banker, Charnes et al. 1984). Micro models of
service systems have tended to study the systems’ IT components (Hsu 2009; Qiu
2009). Engineering, which has always been associated with “assembling pieces that
work in specific ways” (Ottino, 2004) and “a process of precise composition to
achieve a predictable purpose and function” (Fromm, 2010 p. 2) has contributed to
greater scalability and purposeful control in service systems. However, the agents of
the system usually are people whose activities may not be easily controlled by
predictable processes and yet are critical aspects of the value-creating system (Ng et
al, 2011b). There is need for a new combinative paradigm, such as third-generation
activity theory in which two or more activity systems come into contact, to explore
dialogue, exchanging perspectives of multiple actors, resulting in networks or groups
of activity systems that are constantly interacting (Nardi 1996, Oliveros et al 2010,
Marken 2006)
Novel Integration of Sustainable and Construction Decisions into the Design Bid Build Project Delivery Method Using BPMN
No full textAbstractA recent study on the cost of green buildings indicated that Resource Reuse is the category of credits least often achieved in LEED-certified projects. The literature suggests that there are a number of constraints and barriers to resource reuse, but perhaps the most critical one is the lack of easily accessible information about resource availability and usability. The emerging industries of building materials reuse and de-construction in the United States suffers from the absence of a “system” that would streamline business processes, establish a supply-demand chain, and connect vendors and deconstruction contractors with architects. This research derives an innovative restructuring of the architectural design process that enables resource reuse in new construction. We developed this model by capturing expert knowledge using a Delphi research protocol and mapped the Building Materials Reuse Workflow in Business Process Modeling and Notation language. We set out the knowledgebase requirements that should be integrated with Building Information Modeling to support decision-making by architects and project stakeholders
OPTIMAL INVENTORY POLICIES FOR THE ONE WAREHOUSE, N-RETAILER SYSTEM
No full textThis research examines an inventory system consisting of one warehouse and N identical retailers. It is assumed that each facility in the system operates a continuous review (Q, R) replenishment policy, that all unmet demand is backordered, that the transportation lead times between the warehouse and its supplier and between the warehouse and the retailers are fixed, and that each retailer faces independent, unit Poisson demand. Furthermore, the retailers are identical in terms of lead time, demand rate, lot size, and reorder points. The model of the system which is used in this study is the Deuermeyer-Schwarz model. Within the context of this model, the optimal allocation of safety stock among the warehouse and retailers is determined subject to a constraint on the total amount of safety stock in the system. This optimization is carried out under two different objective functions: fill-rate and expected backorders. The results of this study are general statements about the form of these optimal policies, the characterization of the locus of optimal safety stock positions for all finite values of the constraint as a policy line in the two-dimensional policy space, insights into the effects of safety stocks in this system, and a highly accurate and simple heuristic for computing optimal safety stock positions
Approximating probability density functions and their convolutions using orthogonal polynomials
No full text
