Part 2 Service Operations Management
Contents
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Service Operations Management
Rationale
Operations Management is an area of business that is concerned with the production of goods and services, and involves the responsibility of ensuring that business operations are efficient and effective. It is also the management of resources, the distribution of goods and services to customers, and the analysis of queue systems.
Operations also refers to the production of goods and services, the set of value-added activities that transform inputs into many outputs.[1] Fundamentally, these value-adding creative activities should be aligned with market opportunity (see Marketing) for optimal enterprise performance.
- International Journal of Operations & Production Management
- Production Scheduling and Planning Portal
- Advanced-Planning Systems Problem Structures, Advanced-Planning-Software.
- Chartered Management Institute
Learning Objectives and Outcomes
This is a non-taught unit designed for self-directed study by those intending to enhance their professional or managerial competence, knowledge, understanding, and skills in service operations management.
Knowledge
At the completion of this unit students will understand
1. the design and delivery of service within an organisation
2. the nature of service quality and how organisations might improve the quality of their services
3. the concepts that may assist in organisation's planning, control and use of resources
4. how organisations can measure and improve their performance
5. how service organisations can formulate and implement a service strategy
6. have acquired detailed information of the issues facing, and the activities of a number of service organisations
7. have understood the link between operations, marketing, human resource and strategic management
8. the relationship between service and manufacturing operations management
Skills
At the completion of this unit students will be able to:
1. apply the role of service operations and its relationship with other organisational functions
2. demonstrate the necessary skills to be able to consider the other approaches to service operations management
3. apply service operations productivity; its measurement and relationship with other operational functions
4. relate management theory with practice
Today's Videos
- Connect with us on http://www.youtube.com/finntrack
- Google's Playlists
Teaching and Learning Resources
Phase 1: Exploring Services
Tutorials
Readings
Customer service is the provision of service to customers before, during and after a purchase.
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According to Jamier L. Scott. (2002),[1] “Customer service is a series of activities designed to enhance the level of customer satisfaction – that is, the feeling that a product or service has met the customer expectation." Its importance varies by product, industry and customer; defective or broken merchandise can be exchanged, often only with a receipt and within a specified time frame. Retail stores will often have a desk or counter devoted to dealing with returns, exchanges and complaints, or will perform related functions at the point of sale. Customer service may be provided by a person (e.g., sales and service representative), or by automated means called self-service. Examples of self service are Internet sites. However, In the Internet era, a challenge has been to maintain and/or enhance the personal experience while making use of the efficiencies of online commerce. |
Writing in Fast Company, entrepreneur and customer systems innovator Micah Solomon has made the point that "Online customers are literally invisible to you (and you to them), so it's easy to shortchange them emotionally. But this lack of visual and tactile presence makes it even more crucial to create a sense of personal, human-to-human connection in the online arena."[2]
Customer service is normally an integral part of a company’s customer value proposition. In their book Rules to Break and Laws to Follow, Don Peppers and Martha Rogers, Ph.D. write that "customers have memories. They will remember you, whether you remember them or not." Further, "customer trust can be destroyed at once by a major service problem, or it can be undermined one day at a time, with a thousand small demonstrations of incompetence."[3]
From the point of view of an overall sales process engineering effort, customer service plays an important role in an organization's ability to generate income and revenue.[4] From that perspective, customer service should be included as part of an overall approach to systematic improvement.
Some have argued[5] that the quality and level of customer service has decreased in recent years, and that this can be attributed to a lack of support or understanding at the executive and middle management levels of a corporation and/or a customer service policy. To address this argument, many organizations have employed a variety of methods to improve their customer satisfaction levels, and other KPIs.
A service system (or customer service system, CSS) is a configuration of technology and organisational networks designed to deliver services that satisfy the needs, wants, or aspirations of customers.
- National Tsing-hua University (Taiwan) Service Science Institute
- Singapore Management University (Singapore) Service System & Solutions
- Technion (Israel) Service Engineering
- Stevens Institute of Technology (USA) Service Segments, Specializations, and Foundations
- UC Merced (USA) Service Science for Undergraduates
- University at Buffalo, The State University of New York (USA) Service Systems Engineering Graduate Major
- Michigan Tech (USA) Service Systems Engineering Undergraduate Major
- UC Berkeley (USA) The Information and Services Economy: Section 17: Service Systems (ISE notes)
- UC Berkeley (USA) The Information and Service Design Program (service research and instructional program)
- U Toronto (Canada) Service Science Introduction
- North Carolina State University (USA) SSME
- San Jose State University (USA) Service Science Program
- San Jose State University (USA) Service Systems Management
- Karlstads University (Sweden) Service Science
- University of Sydney (Australia) Service Science
- Peking University (China) Service Science and Engineering
- Technion (Israel) Mandelbaum's Service Engineering of Service Networks (aka Service Systems)
- Networked Europe Software and Services Initiative (NESSI)
- Adaptable and Autonomic Service Systems
- Canfora and Di Penta's Architecting, Analysing, and Testing Service-Oriented Systems (aka Service Systems)
- Capacity Management in Stochastic Service Systems
- Tien and Berg's A Case for Service Systems Engineering
- KIT Karlsruhe Institute of Technology — Scientific Alliance eOrganisation
- Sorenson's Architecture-Based Enterprise Systems
- Service Management Software Systems
- Service Science Management and Engineering
- SSME
- Measuring the contribution of financial services to the economy: a report on a meeting of the Financial Statistics Users' Group
- Product Life-Extension and the Service or Functional Economy
- Rebounding Industry and Services Driving Bangladesh's Economy
Phase 2: Thinking Out of the Box: Operations Strategy
Tutorials
- The Front Office
- The Back Office
- Internet Strategies
- Environmental Strategies
- Service Quality
- The Experience Economy
Readings
To look at what operations strategy is, we would break the word into the two separate forming words: operations and strategy. Both these words act as antitheses to the other, see Slack and Lewis(2002). Where operations deals with the functions and procedures involved in the day-to-day processes of manufacturing goods and products, strategy deals with the direction and scope of an organisation over a long period of time on how they deliver to their clients. It turns out that the name itself holds information about the broad subject that tends to bind together routine process management, i.e., operations managements with a foresight of things forming up way ahead in the future.
In this chapter, we will focus on providing the reader with a proper definition of operations strategy and the difference between operations strategy and operations management. Furthermore, we will also shed light on how the market tends to stear the strategic focus of operations. At the end of this chapter, the user would have significant knowledge of:
- What operations strategy is?
- What the difference between operations strategy and operations management is?
- How markets govern the strategic focus of operations?
- How operations strategy helps a market mature and evolve?
Read More ...
In the field of computer networking and other packet-switched telecommunication networks, the traffic engineering term quality of service (QoS) refers to resource reservation control mechanisms rather than the achieved service quality. Quality of service is the ability to provide different priority to different applications, users, or data flows, or to guarantee a certain level of performance to a data flow. For example, a required bit rate, delay, jitter, packet dropping probability and/or bit error rate may be guaranteed. Quality of service guarantees are important if the network capacity is insufficient, especially for real-time streaming multimedia applications such as voice over IP, online games and IP-TV, since these often require fixed bit rate and are delay sensitive, and in networks where the capacity is a limited resource, for example in cellular data communication.
A network or protocol that supports QoS may agree on a traffic contract with the application software and reserve capacity in the network nodes, for example during a session establishment phase. During the session it may monitor the achieved level of performance, for example the data rate and delay, and dynamically control scheduling priorities in the network nodes. It may release the reserved capacity during a tear down phase.
A best-effort network or service does not support quality of service. An alternative to complex QoS control mechanisms is to provide high quality communication over a best-effort network by over-provisioning the capacity so that it is sufficient for the expected peak traffic load. The resulting absence of network congestion eliminates the need for QoS mechanisms.
In the field of telephony, quality of service was defined in the ITU standard X.902 as "A set of quality requirements on the collective behaviour of one or more objects". Quality of service comprises requirements on all the aspects of a connection, such as service response time, loss, signal-to-noise ratio, cross-talk, echo, interrupts, frequency response, loudness levels, and so on. A subset of telephony QoS is Grade of Service (GOS) requirements, which comprises aspects of a connection relating to capacity and coverage of a network, for example guaranteed maximum blocking probability and outage probability.[1]
QoS is sometimes used as a quality measure, with many alternative definitions, rather than referring to the ability to reserve resources. Quality of service sometimes refers to the level of quality of service, i.e. the guaranteed service quality. High QoS is often confused with a high level of performance or achieved service quality, for example high bit rate, low latency and low bit error probability.
An alternative and disputable definition of QoS, used especially in application layer services such as telephony and streaming video, is requirements on a metric that reflects or predicts the subjectively experienced quality. In this context, QoS is the acceptable cumulative effect on subscriber satisfaction of all imperfections affecting the service. Other terms with similar meaning are the Quality of Experience (QoE) subjective business concept, the required "user perceived performance",[2] the required "degree of satisfaction of the user" or the targeted "number of happy customers". Examples of measures and measurement methods are Mean Opinion Score (MOS), Perceptual Speech Quality Measure (PSQM) and Perceptual Evaluation of Video Quality (PEVQ). See also subjective video quality.
- History
- Key qualities of traffic
- Applications
- Obtaining QoS
- Overprovisioning
- QoS mechanisms
- Protocols that provide quality of service
- QoS solutions
- Quality of service procedures
- End-to-end quality of service
- Quality of service circumvention
- Doubts about quality of service over IP
- Standards activity
- BSSGP
- Best-effort
- Class of Service
- Deep packet inspection (DPI)
- Grade of service (GOS)
- Mean Opinion Score (MOS)
- Mobile QoS
- Network neutrality
- Quality of Experience (QoE)
- Series of tubes
- Streaming media
- Subjective video quality
- Tiered Internet
- Traffic shaping
- QPPB
- Application service architecture
- Notes
- References
- QoS Tutorial
- Quality of Service Networking
- Network QoS
- Quality of Service on Microsoft TechNet
- Technical, commercial, and regulatory challenges of QoS by XiPeng Xiao (Google Books)
The term Experience Economy was first described in a book published in 1999 by B. Joseph Pine II and James H. Gilmore, titled "The Experience Economy". In it they described the experience economy as the next economy following the agrarian economy, the industrial economy, and the most recent service economy. This concept had been previously researched by many other authors (see History of the Concept).
Pine and Gilmore argue that businesses must orchestrate memorable events for their customers, and that memory itself becomes the product - the "experience". More advanced experience businesses can begin charging for the value of the "transformation" that an experience offers, e.g. as education offerings might do if they were able to participate in the value that is created by the educated individual. This, they argue, is a natural progression in the value added by the business over and above its inputs [1].
Although the concept of the experience economy was born in the business field, it has crossed its frontiers to tourism, architecture, nursing, urban planners and other fields.
Experience economy is also considered as main underpinning for customer experience management.
Within the hospitality management academic programs in the US and Europe, Experience Economy is often shortened to Exponomy, and is of increasing focus.
Phase 3: Capacity Management
Tutorials
Readings
Capacity management - the meaning of capacityThe capacity of a production unit (e.g. machine, factory) is its ability to produce or do that which the customer requires. In production and operations management, three types of capacity are often referred to:
| Potential Capacity | The capacity that can be made available to influence the planning of senior management (e.g. in helping them to make decisions about overall business growth, investment etc). This is essentially a long-term decision that does not influence day-to-day production management |
| Immediate Capacity | The amount of production capacity that can be made available in the short-term. This is the maximum potential capacity - assuming that it is used productively |
| Effective Capacity | An important concept. Not all productive capacity is actually used or usable. It is important for production managers to understand what capacity is actually achievable. |
Read more ...
Capacity Management is a process used to manage information technology (IT). Its primary goal is to ensure that IT capacity meets current and future business requirements in a cost-effective manner. One common interpretation of Capacity Management is described in the ITIL framework [1] . ITIL version 3 views capacity management as comprising three sub-processes: business capacity management, service capacity management, and component capacity management (known as resource capacity management in ITIL version 2).
As the usage of IT Services change and functionality evolves, the amount of processing power, memory etc also changes. If it is possible to understand the demands being made currently, and how they will change over time, this approach proposes that planning for IT Service growth becomes easier and less reactive. If there are spikes in, for example, processing power at a particular time of the day, it proposes analysing what is happening at that time and make changes to maximize the existing infrastructure, for example, tune the application, or move a batch cycle to a quieter period.
These activities are intended to optimise performance and efficiency, and to plan for and justify financial investments. Capacity management is concerned with:
- Monitoring the performance and throughput or load on a server, server farm, or property
- Performance analysis of measurement data, including analysis of the impact of new releases on capacity
- Performance tuning of activities to ensure the most efficient use of existing infrastructure
- Understanding the demands on the Service and future plans for workload growth (or shrinkage)
- Influences on demand for computing resources
- Capacity planning – developing a plan for the Service
Capacity management interacts with the discipline of Performance Engineering, both during the requirements and design activities of building a system, and when using performance monitoring as an input for managing capacity of deployed systems.
See also
- Application Performance Management
- Capacity planning
- Computer Measurement Group (CMG)
- Network performance management
- Performance analysis
- Performance tuning
- ITIL
External links
- Information for Managing Waiting Time
- Service Management &Waiting Line Management
- Waiting Line Characteristics. Suggestions for Managing Queues. Suggestions for Managing Queues. Examples.
Phase 4: Adding Science to Art
Tutorials
- Analysing Processes
- Project Management
- New Service Development
- Site Selection For Services
- Performance Evaluation and Benchmarking with Data Envelopment Analysis
Readings
Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units (or DMUs). Non-parametric approaches have the benefit of not assuming a particular functional form/shape for the frontier, however they do not provide a general relationship (equation) relating output and input. There are also parametric approaches which are used for the estimation of production frontiers (see Lovell & Schmidt 1988 for an early survey). These require that the shape of the frontier be guessed beforehand by specifying a particular function relating output to input. One can also combine the relative strengths from each of these approaches in a hybrid method (Tofallis, 2001) where the frontier units are first identified by DEA and then a smooth surface is fitted to these. This allows a best-practice relationship between multiple outputs and multiple inputs to be estimated.
"The framework has been adapted from multi-input, multi-output production functions and applied in many industries. DEA develops a function whose form is determined by the most efficient producers. This method differs from the Ordinary Least Squares (OLS) statistical technique that bases comparisons relative to an average producer. Like Stochastic Frontier Analysis (SFA), DEA identifies a "frontier" on which the relative performance of all utilities in the sample can be compared: DEA benchmarks firms only against the best producers. It can be characterized as an extreme point method that assumes that if a firm can produce a certain level of output utilizing specific input levels, another firm of equal scale should be capable of doing the same. The most efficient producers can form a 'composite producer', allowing the computation of an efficient solution for every level of input or output. Where there is no actual corresponding firm, 'virtual producers' are identified to make comparisons" (Berg 2010).
- DEA Zone, A comprehensive website on Data Envelopment Analysis
- Full Bibliography of DEA- Part1 , Full bibliography of DEA with more than 4000 journal articles - Part 1
- Full Bibliography of DEA- Part2 , Full bibliography of DEA with more than 4000 journal articles - Part 2
- DEA software, The DEA software (Performance Improvement Management Software)
- OR Notes by J E Beasley DEA
- COOPER-framework: A unified process for non-parametric projects , COOPER-framework: A unified process for non-parametric projects
- DEA Online Software, Solve your DEA models with a professional software
- [2], European Workshop on Efficiency and Productivity Analysis
- [3], Group on Efficiency and Productivity Analysis that is active within EURO
- [4], Journal of Productivity Analysis, Kluwer Publishers
- Body of Knowledge on Infrastructure Regulation - Principles of using efficiency measures for yardstick regulation
- WebdeA, Solve your DEA models with a free software (beta version).
- A Data Envelopment Analysis (DEA) Home Page
- Product/Service Development Cycle
Project management is the discipline of planning, organizing, securing and managing resources to bring about the successful completion of specific project goals and objectives. It is sometimes conflated with program management, however technically that is actually a higher level construction: a group of related and somehow interdependent engineering projects.
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A project is a temporary endeavour, having a defined beginning and end (usually constrained by date, but can be by funding or deliverables),[1] undertaken to meet unique goals and objectives,[2] usually to bring about beneficial change or added value. The temporary nature of projects stands in contrast to business as usual (or operations),[3] which are repetitive, permanent or semi-permanent functional work to produce products or services. In practice, the management of these two systems is often found to be quite different, and as such requires the development of distinct technical skills and the adoption of separate management.
The primary challenge of project management is to achieve all of the engineering project goals[4] and objectives while honouring the preconceived project constraints.[5] Typical constraints are scope, time, and budget.[1] The secondary—and more ambitious—challenge is to optimise the allocation and integration of inputs necessary to meet pre-defined objectives.
- Guidelines for Managing Projects from the UK Department for Business, Enterprise and Regulatory Reform (BERR)
- Max Wideman's "Open Source" Comparative Glossary of Project Management Terms
- Open Source Project Management manual
An Innovative Model of Service Development
KonSi Software for Data Envelopment Analysis and Benchmarking 75 units
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Resources
