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Optimizing Care Processes with Operational Excellence & Process Mining


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Healthcare transformation is necessary to address rising costs of care. Operational Excellence can optimize care processes and create more value for each patient with the same resources. Operational Excellence uses different optimization tools in a continuous improvement (DMAIC-)cycle to optimize processes. In this regard, process mining can play an important role in discovering, analyzing and controlling care processes by making use of available data. Additionally, understanding the type of care process and care organization is important in order to choose the right Operational Excellence approach. Operational Excellence also includes a combination of leadership and work design (sociotechnical systems) which improves the success of process optimizations.
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P. Kubben et al. (eds.), Fundamentals of Clinical Data Science,
Chapter 13
Optimizing Care Processes
withOperational Excellence & Process
HenriJ.Boersma, TiffanyI.Leung, RobVanwersch, ElskeHeeren,
andG.G.van Merode
13.1 Introduction
Providing high-quality and accessible health care is very important due to growing
awareness and public pressure to do so [1]. However, this is becoming increasingly
difcult as the demand for care continues to rise. Due to an aging population and
increased patient demand for new services, technologies, and drugs, it is expected
that healthcare expenditures will only continue to increase in the future [2].
Considering the burden of costs, healthcare has to be transformed in order to keep it
available and accessible. To cope with this challenge, healthcare managers and pro-
fessionals have been looking for new methods of resource utilization and optimiza-
tion to potentially apply to health care. However, health care is not a standard
manufactured product and a patient is not a simple widget in a manufacturing pro-
cess line. Each patient has needs unique to his or her physiology, genetics, social
circumstances, and other characteristics, for which different management options
may be appropriate. This uncertainty in both demand for care and the provision of
care is visible at all levels of the healthcare system, from an individual consultation
with a general practitioner to a complex care process in a very large hospital [3].
Because of this, a care process and the coordination of the process often becomes
very complex and not efcient [4]. Using data, either measured manually or
extracted from data systems, about these care processes is therefore very important
in order to understand and, subsequently, improve and control the process. In this
H. J. Boersma (*) · T. I. Leung · G. G. van Merode
Maastricht University Medical Center+, Maastricht, The Netherlands
Maastricht University, Maastricht, The Netherlands
R. Vanwersch · E. Heeren
Maastricht University Medical Center+, Maastricht, The Netherlands
chapter we will explore how Operational Excellence can optimize care processes
and transform healthcare using these data. Among other, we will discuss how pro-
cess mining can be used in this regard.
13.2 Care Process
A basic care process consists of different steps but frequently follows a similar pat-
tern (Fig.13.1). First, a patient seeks physician consultation regarding symptoms.
Typically, further diagnostic or therapeutic decision-making is then needed to
decide on next steps in care. This could involve another consultation, a procedure,
or other additional steps added to the process. Follow-up consultation usually fol-
lows to close the loop on diagnosis, treatment, and management of the initial symp-
toms for which a patient sought care; this may be recurrent in complex or chronic
conditions, and numerous variations in this basic process are possible.
Of course, not every care process is the same. For any given process, an analysis
of the type of process and organization where the process takes place is essential to
be able to optimize it. Johnston and Clark (2008) use two criteria to distinguish
between different process types: [1] volume and [2] process variation and process
complexity (Fig.13.2) [5].
There can be variation in healthcare demand (what and how many of a given ser-
vice is asked for, at which time and place?), healthcare supply (what service, at what
quality can be offered, at which time and place?) and the service itself (is it delivered
according to the specications?). Complexity can be related both to the case (medical
complexity) and to the coordination of processes. Patients with multimorbidity, or
multiple chronic conditions, and super-utilizers, or frequent users of high-cost ser-
vices, are examples of complex cases. Such cases inherently involve many persons
and/or activities in care of the patient. This often results in a higher burden of care
coordination, making it difcult to streamline processes in an efcient and standard-
ized way. In the next chapter, the concepts of multimorbidity, or patients with multiple
chronic conditions, and super-utilizers of healthcare services will be explored further.
Due to the complexity and variation, it is difcult to predict what the demand
will be and how much capacity is available to meet the demand. For example, wait-
ing time for a patient is a symptom of a process where demand and supply are
mismatched. Managing waiting times is surprisingly complex, unless one accepts
high overcapacity. Moreover, even in simple waiting systems there is a non-linear
relationship between utilization rate of appointment capacity and waiting time. The
relationship between utilization rate and waiting becomes more linear when there
are more workstations and customers have no preference for one of them. For exam-
ple, a patient seeking an appointment at a practice with one general practitioner
tests Therapy
Fig. 13.1 The typical steps of a care process
H. J. Boersma et al.
could have a high waiting time due to more limited appointment capacity; in a prac-
tice with two general practitioners, the patient may choose the rst appointment
available, resulting in a reduced waiting time (see Fig.13.3). For the same utiliza-
tion of capacity this reduces waiting signicantly.
Strong focus on sociotechnical systems
and information processing
Strong focus o
Lean Six Sigm
and process
Capability Complex
Fig. 13.2 Volume-variety matrix adapted from Johnston etal. [5]
Mean waiting time in minutes
Utilization percentage
1 General Practitioner 2 General Practitioners
60 70 80 90
Fig. 13.3 Relationship between waiting time and utilization
13 Optimizing Care Processes withOperational Excellence & Process Mining
By optimizing the processes, the waiting time can be reduced, but to be able to
cope with the uncertainty of demand, which will always exist due to the nature of
healthcare, exibility of the resources is required. A low degree of exibility can
lead to a mismatch between supply and demand. Inexibility determines the adapt-
ability of the production system to changes in the chain of activities. There are three
types of inexibility [3]:
Technical inexibility: equipment can only be used in one way;
Economic inexibility: extra costs are incurred when capacity is used in a differ-
ent way to that originally intended; for example, an operating room designed for
certain operations can also be used for other operations, but then the equipment
must be changed, which leads to switching costs;
Staff inexibility: occurs due to limited knowledge, specialization, legal reasons,
working times and motivation.
13.3 Operational Excellence
The main goal of Operational Excellence (OE) is to enable any organization to excel
at the service it provides or product it produces. Within healthcare, OE is strongly
focused on optimizing the care process and creating (more) value for thepatient.
Operational Excellence uses the data from these processes to continuously analyze,
improve and control them. A process is dened as a specic ordering of work activi-
ties across time and space, with a beginning and an end, and clearly dened inputs
and outputs: a structure for action [6]. Processes are the structure by which an orga-
nization does what is necessary to produce value for its customers. The methods and
theories of OE are applicable in any health care setting by any type of healthcare
provider, including small general practitioners’ ofces or large multispecialty hos-
pitals with different departments, emergency rooms and operating rooms.
Operational Excellence works through the Dene, Measure, Analyze, Improve
and Control or DMAIC- Cycle as its continuous improvement framework to opti-
mize the care processes [7]. Data plays a very important in this cycle. At every step
of the cycle, process data is needed to perform actions. The phases within the
DMAIC are dened as [8]:
Dene by identifying, prioritizing and selecting the right project;
Measure key process characteristics the scope of parameters and their
Analyze by identifying key causes and process determinants;
Improve by changing the process and optimizing performance;
Control by sustaining the gain.
Operational Excellence has a wide range of optimization methods that can be
used to improve the care process. OE is best known for its popular methods of Lean
(Thinking), Six Sigma or the combination Lean Six Sigma (LSS). However, OE
also relies on sociotechnical systems (STS) and leadership to transform care pro-
cesses, which we will briey discuss at the end of this chapter. First, we will discuss
the basis methodologies of Lean, Six Sigma and Lean Six Sigma.
H. J. Boersma et al.
13.3.1 Lean Thinking
Lean (Thinking) is derived from the term ‘lean,’ introduced by Womack etal. who
published their book ‘The machine that changed world’ [9]. Focusing on car manu-
facturing, the report described how Japanese production methods were superior to
Western because they were able to produce cars efciently without losing quality.
This was in contrast to the mass production of cars then common in the West, which
was very effective in producing large volumes, but had a lot of rework needed.
Toyota, the rst company that successfully implemented ‘Lean Manufacturing’ and
to car production, was successful because of a deep business philosophy based on
its understanding of people and human motivation. They implemented quality
improvement methods and as a result created Operational Excellence. Toyota had
successfully enriched leadership, teams, and culture to create strategy, built supplier
relationships and maintained a learning organization [4].
The main purpose of using Lean is to eliminate waste in order to create more
value. The approach describes seven types of waste: overproduction; waiting;
unnecessary transport or conveyance; over processing or incorrect processing;
excess inventory and unnecessary movement and defects [10] (Table13.1). Later
publications added an eighth type of waste: unused human potential [4].
Table 13.1 Overview of all types of waste according to Lean Thinking and a short description
[3, 4]
Type of waste Brief description Healthcare examples
Overproduction Doing more than what is needed by
the patient or doing it sooner than
Blood tests being done weeks
before a consultation, so they are
not recent when needed
Waiting Waiting for the next event to occur
or next work activity
Patient waiting for an appointment
or doctors waiting for a lab result
Transportation Unnecessary movement of the
product in a system (patients,
specimens, materials)
Cardiac catheterization lab being
located far from the emergency
Overprocessing or
incorrect processing
Doing work that is not valued by the
patient; or the result of care quality
being dened in a way that is not
aligned with patient needs
Buying the newest surgery robots to
perform simple procedures with no
benet for the patient in terms of
quality or outcome
Inventory Excess inventory cost, for example,
due to added nancial costs, storage
and movement costs, spoilage, or
Buying all surgical equipment in the
same order of magnitude while not
all equipment is being used as
Motion Unnecessary movement by
employees in the system
Lab employees walking between lab
and their desk
Defects Time spent doing something
incorrectly, inspecting for errors, or
xing errors
Surgical cart missing an item
Human potential Waste and loss due to not engaging
employees, listening to their ideas,
or supporting their careers
Employees being overworked and
developing burnout
13 Optimizing Care Processes withOperational Excellence & Process Mining
13.3.2 Six Sigma
In this same period as Lean Thinking was gaining popularity, Six Sigma was intro-
duced. This approach was created at Motorola in the late 1980s [11]. Today, Six
Sigma is a technique used to improve processes not only for manufacturing, but also
for other sectors including healthcare. Six Sigma strategies seek to improve the
quality of the output of a process by identifying and removing the causes of defects
and minimizing variability in processes. It uses a set of quality management meth-
ods, mainly empirical, statistical methods; hypothesis testing is applied to empirical
data, in order to nd evidence for or against supposed causes of process problems.
It also creates a special infrastructure of people within the organization who are
experts in these methods. The term ‘six sigma’ comes from ultimate goal of this
method: having only 3.4 defective features per million opportunities. This means
that in a process 99.99966% of all opportunities to produce some feature of a part
are statistically expected to be free of defects.
13.3.3 Lean Six Sigma
Lean Six Sigma describes the integration of Lean and Six Sigma philosophies [12].
A combination of Lean and Six Sigma can provide an effective framework as both
are systematic approaches to facilitating process optimizations. Where Lean focuses
more on standardization and production ow leveling, Six sigma has an approach
where reduction of process variability is central. Because of this, Lean often has not
consistent (changing) performance metrics. By combining the two methodologies,
the more quantiable methodology of Six Sigma, such as statistical process control,
and the more cultural approach of Lean, such as Value stream mapping, a more
complete analysis of an organization can be made. Six Sigma’s focus on statistical
rigor and control of variation and Lean’s focus on reduction of non–value-added
activities both require data collection and analysis to improve performance. [13].
DMAIC cycles can be performed by anyone in the organization, if trained and
supported by leadership. Equipped with the skills to do so, healthcare professionals
can improve their own process and, consequently, have a sense of ownership of the
care process and its continuous improvement. This gives them an in-depth look on
their process, which helps them to Analyze and Improve the process. Because it is a
continuous improvement tool, the purpose is to keep measuring, also when the
improvement is completed. A dashboard is an effective method to continuously
visualize the process in real-time or close-to-real-time data.
One important process output is the access time, which is the number of days a
patient has to wait to get an appointment (Fig.13.4). When the access exceeds a
certain limit, action is taken. Visualization, even in this primitive form, thus keeps
health professionals attentive to indicators that are critical to a smooth care
H. J. Boersma et al.
Because these optimizations are done in a cyclic, continuous way, processes are
constantly changing and adapting. These changes are generally incremental and not
seen as transformative in themselves. However, by continuously changing elements
of the organization, Operational Excellence can transform entire organizations.
13.4 Process Mining
A more advanced technique that can be used in the context of DMAIC cycles is
process mining. Process mining extracts process knowledge from so-called event
logs which may originate from all kinds of software systems (Fig.13.5) [14].
The example event log shown in Fig. 13.6 contains the typical information
needed to perform process mining. Each event belongs to a single process case.
Events are related to activities. The “case id” and “activity” columns are essential
information for process mining. The “event id” can be used for ordering events
within a care process. This is needed in order to see causal dependencies between
events. An event log may also contain additional information, which can be used for
calculating performance properties of the process. For instance, the “resource” (per-
former of the event) and the “cost” attribute (cost of the activity) can be used for
discovering additional process knowledge. The table shown in Fig.13.2 contains 12
events for 2 cases. For case id “1”, subsequently the activities “First Visit”,
“Surgery”, “Second Visit”, “Radiotherapy”, “Chemotherapy” and “Evaluate” have
been performed. Here, the “First Visit” event has id “589,585”, is performed by
“John” at “05/04/2017”, and has cost “150”.
Week of the year
Week 42 of 2016 through Week 48 of 2017
Access time (in weeks)
Fig. 13.4 Visualization of access lead time to Orthopedics subspecialty outpatient clinics at the
Maastricht UMC+
13 Optimizing Care Processes withOperational Excellence & Process Mining
Process mining applies specialized mining algorithms to gain insights into how
process are actually executed based on stored event logs. So, where traditional mod-
eling techniques try to model a processor create a value stream map based on inter-
views with people working in the process, process mining makes use of stored data
to model and analyze these processes automatically and overcomes human limita-
tions in reconstructing complex processes. There are three main types of process
mining that can be distinguished: Discovery, Enhancement and Conformance [14].
events, e.g.,
Fig. 13.5 Basic objectives and types of process mining [14]
Caseid Eventid Properties
1 589585 05/04/2017 FirstVisit Joh
589586 08/04/2017 Surgery Henri 55
589590 10/04/2017 SecondVisit Joh
589593 16/04/2017 Radiotherapy Peter 200
589595 21/04/2017 ChemotherapySuzan 300
589601 28/04/2017 Evaluation Joh
2 748384 01/02/2018 FirstVisit Tom 150
748385 03/02/2018 Surgery Olivia 55
748386 10/02/2018 SecondVisit Tom 150
748400 16/02/2018 RadiotherapyPeter 200
748408 19/02/2018 Immunotherapy David 300
748412 22/02/2018 Evaluation Jack 175
Fig. 13.6 Example of an event log
H. J. Boersma et al.
Discovery Here, event logs are used to model the different steps that are taken
within a care process. From the example event log in Fig.13.6, the following pro-
cess model will be discovered by making use of process mining (Fig.13.7).
The discovered process model in Fig.13.7 represents the behavior of all (in this
case just two) cases. The model shows that cases have a rst visit, a surgery, a sec-
ond visit and then receive radiotherapy successively. Subsequently, a case receives
either chemotherapy or immunotherapy, before an evaluation is performed.
Discovering a process model by means of process mining can be very helpful in
the Measure phase of the DMAIC cycle to gain insights into how the care process
actually looks like. For care processes that are more complex than the one shown in
Fig.13.7, discovering a process model by means of process mining is less time-
consuming than modeling a care process “by hand” based on interviews. Moreover,
process mining will also shed light on less frequently executed process paths, which
are easily overlooked by practitioners modeling processes “by hand”.
Conformance Conformance checking is used to check whether the observed steps
in the event log conform to a desired care process (see Fig.13.6). In case there are
deviations between the desired situation and the event log, these are identied such
that they can be further analyzed. In the Analyze phase of the DMAIC cycle, one
might check to what extent processes comply with internal and external guidelines.
For example, for certain patient groups, standards may exist in the form of clinical
practice guidelines or protocols that can be translated to process models to be
adhered to. By making use of process mining, deviations from guidelines and pro-
tocols can subsequently be identied and quantied, after which the desirability of
deviations can be discussed. As part of the Improvement phase of the DMAIC cycle,
process improvements are generated implemented based on the results of the
Analyze phase leading to a new care process (model) to be adhered to [15].
Subsequently, process mining can be used once again during the Control phase.
Then, healthcare professionals or managers can check adherence to this new care
process (model) and identify deviations.
Enhancement This type of process mining extracts additional information from the
log and enriches a process model with additional perspectives (times, costs,,
resource usage, etc.). These enhancements facilitate a more in-depth measurement/
monitoring of the process (e.g. monitoring throughput times) during the Measure
and Control phase in the DMAIC cycle. For example, average throughput times
between the different steps might be automatically projected on the process model,
as illustrated in Fig.13.8.
First visitSurgerySecond visitRadiotherapy Chemotherap
Fig. 13.7 Care process discovered from example event log
13 Optimizing Care Processes withOperational Excellence & Process Mining
13.5 Sociotechnical Systems & Leadership
As mentioned earlier, Operational Excellence also entails, besides process optimi-
zation, a sociotechnical systems approach (STS). The before-mentioned methods of
improving processes are very powerful, but with more complex care processes that
are very unpredictable or that need a higher level of coordination because several
different professionals are involved, Operational Excellence relies on STS.Below,
we will also briey discuss the role of leadership in optimizing care processes with
Operational Excellence.
13.5.1 Sociotechnical Systems
Sociotechnical systems, also called socio-technique, offers tools for analyzing
which tasks within a care process should be performed by which people. Where
processes are unpredictable, the capabilities and exibility of people are needed,
socio-technique can help in dening these decisions and functions. Karasek’s Job
Demand Control Model (Fig.13.9) is one of these tools that can help to dene jobs
and tasks in a care process [16].
The higher the variability of a care process, the more exible and autonomous
the employee should be. Low exibility means reduced decision-making autonomy.
Passive jobs, where there not much variability can be performed by employees with-
out a lot of exibility and therefore more standardization and efciency (Lean Six
Sigma optimized processes). Active jobs, in the upper right quadrant, have high
demands but also high levels of control. These challenging jobs lead to active learn-
ing and motivation to develop new behavior patterns. High strain jobs, in the lower
right quadrant, have high demands and low control. These jobs have a high risk of
psychological strain and physical illness. Low Strain jobs can lead to waste of
human resources. Dening which tasks in care processes should be performed by
which people is therefore essential to be ensure that the process is able to cope with
the uncertainty of demand.
First visitSurgerySecond visitRadiotherapy
2.5 days
4.5 days
6 days
5 days
3 days
5 days
Fig. 13.8 Care process enriched with throughput times based on time-related logs in Fig.13.6
H. J. Boersma et al.
13.5.2 Leadership
Lastly, Operational Excellence requires a specic type of environment where people
want to experiment and try to improve the processes. To create such an environment,
leadership is needed. In times of change, such as in healthcare system transforma-
tions or even in small-scale process improvements, this is especially important.
Research found that leaders use six styles: commanding, visionary, afliative, dem-
ocratic, pacesetting and coaching [17]. The one that ts best for Operational
Excellence is the coaching style¸ which is dened by a leader who develops people
for the future and most importantly motivates employees to experiment. By encour-
aging employees to experiment, more DMAIC projects will be started and employ-
ees are not afraid to fail. The leader should be constantly stimulating their employees,
helping them improve performance, and develop long- term strengths.
13.6 Conclusion
In conclusion, Operational Excellence can help healthcare professionals and man-
agers in transforming healthcare organizations towards processes that create more
value for patients. Besides process optimization methods, Operational Excellence
also involves sociotechnical systems and is most successful with a leader who has a
coaching leadership style. Understanding the type of care process and organization
where Operational Excellence is implemented is important in order to choose the
right approach. Data is an essential part in the DMAIC cycle, which is central in
Operational Excellence. Process mining can help in this improvement cycle by
gaining insights into how care processes are actually performing and controlling
processes after an improvement has been implemented.
Low Strain
High Strain
of staff
Variability of demand
Fig. 13.9 Job Demand
Control Model adapted
from Karasek [16]
13 Optimizing Care Processes withOperational Excellence & Process Mining
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H. J. Boersma et al.
... In addition to discussing the benefits, these studies propose approaches to how mining techniques can be applied within the DMAIC model. Six out of 16 publications are of this type: (van Geffen and Niks 2013; Dogan and Gurcan 2018;van Kollenburg and Wouters 2018;Boersma et al. 2019;Dahlin et al. 2019;Gupta et al. 2019). In the paragraphs below, we discuss these works in more detail. ...
... Finally in the Control phase, using process mining as an established approach in the daily management of operations gives the opportunity to sustain the improvements. Boersma et al. (2019) discuss how the discovery, conformance and enhancement techniques of process mining can be used in the DMAIC cycle in the healthcare domain. For the initial phases, they show the efficiency of process mining techniques over qualitative methods, such as interviews. ...
Full-text available
Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management.
This study aims to identify the impact of commercial issues (CI), financial issues (FI), and corporate affairs (CA) on operational excellence (OE) of the mining industry. A purposive sample of size 321 was collected from Indian mining executives with more than ten years of exposure to the mining field. Factors are identified and confirmed with the use of CFA. The Structural Equation Modeling technique was then applied to understand the unique as well as the complex relationships between FI, CI, CA, and OE. The results indicate that all three issues, CI, FI, and CA have an influence on OE in the Indian mining industry. Among the variables of the issues considered in this study, Marketing Products, and Size & Quality of Products (from CI); Scale of Economies (from FI); Risk Management (from CA) and Transportation & Machine Operation (from OE) are the highest influencing variables. Business managers of the mining industry will be more vigilant and aware of those indirect variables such as Marketing Products, Size & Quality of Products, Scale of Economies, Risk Management, etc. which can influence operational excellence apart from major influencing variables such as Transportation & Machine operations and Production Scheduling, etc. The study has its limitations in sampling, the timing of sample collection, and their mode. The samples were collected from only massively-deposited large mines. This is the first study in the mining industry to evaluate the impact of these 3-issues on operational excellence. The originality of this research lies in testing the CI, FI, and CA of the mining industry with OE, which is completely new to this field.
Poliklinieken van ziekenhuizen zijn een essentieel en omvangrijk element van het Nederlandse zorgsysteem. Om met gelimiteerde capaciteit een gewenst niveau van zorg te kunnen leveren, is capaciteitsmanagement cruciaal. Er bestaat echter geen one size fits all-blauwdruk van capaciteitsmanagement voor de polikliniek. In de zorg is een bepaalde mate van voorspelbare en onvoorspelbare onzekerheid waardoor poliklinieken continu beslissingen moeten nemen over het gebruik van hun personeel, ruimten en materialen om doelstellingen te behalen. Dit hoofdstuk helpt zorgprofessionals en zorgmanagers met deze beslissingen door handvatten te bieden voor optimale keuzes over servicegraad, servicestrategie, aggregatieniveau, planningstermijn, taakdifferentiatie en planningssysteem. Uitkomsten worden samengebracht in een eigen, uniek capaciteitsplan dat aangepast kan worden in de toekomst of wanneer de situatie verandert.
Hospital systems are under constant pressure to provide quality care despite limited resources. However, traditional capacity management in hospitals is often not effective enough. One reason for this is the variability and uncertainty in the healthcare field that has to be managed. Another reason is the observation that hospitals are open loop systems, meaning they do not use feedback to determine if their output has achieved the desired goal of input. They do not observe the output of their processes controlled by them and use this information to take action. In hospital systems, there are few efficient planning systems or decision support systems to help administrators take decisions. This is different in other industries, where complex planning systems with the help of Artificial Intelligence, or AI as it is referred to, are often being used. This research chapter analyses the issues and possibilities for hospitals to incorporate AI into their capacity management and become intelligent systems in which operations and processes are regulated by feedback (closed loop system) and, more specifically, discusses the recent research of the authors on this topic, where Artificial Intelligent (Multi-Agent System) methods in combination with real-time coordination were described and implemented in the Aravind Eye Hospital (AEH) in India.
The industrial engineering and management principles are predominantly used in different application domains, but the healthcare sector has a potential scope in adopting them. The applications of various industrial engineering aspects such as Toyota Lean System, six sigma, reliability, quality assurance, and process capability tools and techniques into the healthcare sector have been compiled in this work. Industrial engineering concepts have been widely used in manufacturing and the service sector to improve reliability, efficiency, productivity, quality, and safety of systems, with considerable focus in healthcare systems. This work comprehends the various works of applications of industrial engineering to address the efficiency and effectiveness problems in healthcare industry. This work serves as a ready reference not only for the healthcare practitioners but also for various journal authors, reviewers, and editors, as it gives an up-to-date access to works of various facets of industrial engineering applications in the healthcare domain.
The purpose of this paper is to explore operation influencing factors of mining. To collect gaps of study and to form a thematic representation of principal influencing factors and their unique influencing factors. Articles were collected from different sources from 1974 to 2019 consisting of research articles, technical papers, expert blogs, working papers and conference papers covering various disciplines from psychology, human resource, finance and economics to mining engineering. Mining operation influencing factors were noted down. Four massively deposed mines were visited to observe the sequence of mining process. The field experts were also consulted to identify factors influencing their respected industry. Gaps were observed while comparing with the reviewed articles and opinions of field experts. Finally, senior experts were consulted to identify unique factors from the final list prepared and a framework of seven thematic categories consisting of unique factors was formulated. A total of 197 sub-factors were collected from literature review and 2 sub-factors from Indian Mining experts during field study. These 199 sub-factors were initially categorised as 48 factors and one more factor was collected from Indian field experts. Finally, these 49 factors were thematically represented as principal factors and termed as operation, marketing and management, human resource, finance, resource and utility, corporate affairs and corporate social responsibility and environment. This study can be very helpful in the direction of different qualitative and quantitative studies, as the factors and sub-factors groups are identified. The paper fulfils an identified need to provide a holistic review for understanding and documenting principal factors, unique factors and sub-factors those influence mining operation, profitability or sustainability issues of mines.
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Current economic crisis raises the constant demand for profitable solutions that allow organizations to gain competitive advantage. For this reason, more and more companies search for management methodologies that allow them to improve their products and/or service characteristics, perfect their processes, decrease costs, improve the capital's profitability and costumers' satisfaction. This have been attempted through Lean Management and Six Sigma integrated approaches in their managerial and production processes in which, Lean focus mainly on the waste elimination, using simple and visual techniques whenever possible and Six Sigma on the control and processes variability reduction, using statistical tools for this purpose. The present article proposes a Lean Six Sigma (LSS) project management improvement model supported by the DMAIC cycle and integrating an enlarged and adapted set of statistical tools, given the nature of the project management main variables and the involved processes. The proposed model was tested in a Portuguese telecommunication company context which project management processes system are based on Project Management Institute (PMI) standards. The model allowed identifying company's main project management problems and associated causes and the selection of the causes to be first attended. The proposed model also permitted to systematically address the actions and solutions to be implemented in order to keep, in the long run, the continuous improvement of the project management processes in the organization.
Erfolg ist kein Zufall. Die Autoren zeigen, wie es Porsche schaffte, zu einem Vorzeigeunternehmen der deutschen Wirtschaft zu werden. Weder Wirtschaftsflaute noch Börsenabsturz gingen dem Unternehmen an die Substanz - Lean Thinking sei Dank.
Aircraft-controls firm combines strategies to improve speed, flexibility, and quality.
In order to improve the quality and logistics of care, health care organizations should deal with uncertainty of demand and supply, inflexibility of the health care organization and its capacity and organizational complexity. What is needed is integrated process management and standardization to improve quality and safety of care, patient logistics and working conditions of staff. This paper tries to fuse (and in that way better understand) the concept of six sigma with the guide to the expression of uncertainty in measurement in relation to the concept of target engineering.