operation management and operation strategyRohit Kumar
Operational management refers to the administration of business practices to create the highest level of efficiency possible within an organization. It involves planning, organizing, and overseeing manufacturing processes, supply chain functions, and other business operations. The key aspects of operational management include:
- Planning - Determining the most effective and efficient ways to use resources to produce goods and services. This includes processes like capacity planning, production planning, etc.
- Organizing - Establishing an organizational structure and assigning responsibilities to ensure smooth workflow and operations.
- Leading - Guiding employees and work teams to achieve operational goals through effective leadership and communication.
- Controlling - Monitoring operations and making corrections to address issues like quality control, inventory management, and
This document discusses hybrid process models that combine both declarative and imperative modeling techniques. It notes that different parts of a process may be more or less flexible, so a hybrid approach can model this better than a single paradigm. Examples of hybrid approaches include mixing Petri nets with declarative constraints, or having flexible regions within an otherwise rigid workflow. The document also discusses evaluating hybrid modeling through human modeling experiments and automated discovery techniques using event logs. It suggests that while hybrid models seem promising, more work is needed to determine how best to apply different techniques.
The document discusses several prescriptive software development models:
1. The waterfall model is a linear sequential model and was one of the earliest prescriptive models proposed.
2. Variations of the waterfall model include the V-model and incremental model, which allow for some iteration and incremental delivery of features.
3. Evolutionary models like prototyping and the spiral model combine iterative development with controlled aspects of waterfall, producing prototypes and incremental releases to manage risk.
The document discusses various software production process models, including traditional waterfall models, iterative models like the spiral model, and agile methodologies. Waterfall models involve sequential phases from requirements to maintenance but lack flexibility. Iterative models divide the process into increments with feedback between phases. Agile methods like Scrum, Extreme Programming, and Smart emphasize rapid, incremental delivery, automating processes, and customer involvement. The choice of model depends on factors like requirements volatility, team experience, and project priorities.
Making Model-Driven Verification Practical and Scalable: Experiences and Less...Lionel Briand
The document discusses experiences and lessons learned from making model-driven verification practical and scalable. It describes several projects collaborating with industry partners to develop model-based solutions for verification. Key challenges addressed include achieving applicability for engineers, scalability to large systems, and developing solutions informed by real-world problems. Lessons learned emphasize the importance of collaborative applied research, defining problems in context, and validating solutions realistically.
The document discusses several process models for software development projects, including code and fix, waterfall, incremental/iterative, spiral, rapid application development (RAD), and concurrent development models. Each model has advantages and disadvantages depending on factors like project size, requirements stability, and team expertise. Combinations of models may also be suitable in some cases.
This chapter discusses software development processes, project planning, and effort estimation. It introduces several key concepts:
- Software development processes involve a series of steps and activities that produce intended outputs. Common process models include waterfall, iterative development, and agile methods.
- Project planning involves tracking progress, organizing personnel, and estimating effort and schedule. Tools like Gantt charts, histograms, and expenditure tracking can be used.
- Effort estimation methods include expert judgment, algorithmic techniques like COCOMO II, and machine learning approaches. Estimates should be refined repeatedly as uncertainty decreases over the project lifecycle.
Use Case: Airbus and Process Mining TechnologyCelonis
Within the framework of its digital transformation, Airbus is using Celonis software to gain a better understanding of its processes to maximize improvement. In this session we will discuss how our ERP Solution Center is organized to answer all the organization’s business needs for process mining. We will also provide you with an overview of business area uses for Celonis at Airbus and provide a concrete use case of process improvement at the operation level.
Presenters:
Mr. Gildas Lavergne, Head of ERP Solution Center Technologies, Airbus
Ms. Xiaowei Jiang, Product Owner of Process Mining Solutions, Airbus
operation management and operation strategyRohit Kumar
Operational management refers to the administration of business practices to create the highest level of efficiency possible within an organization. It involves planning, organizing, and overseeing manufacturing processes, supply chain functions, and other business operations. The key aspects of operational management include:
- Planning - Determining the most effective and efficient ways to use resources to produce goods and services. This includes processes like capacity planning, production planning, etc.
- Organizing - Establishing an organizational structure and assigning responsibilities to ensure smooth workflow and operations.
- Leading - Guiding employees and work teams to achieve operational goals through effective leadership and communication.
- Controlling - Monitoring operations and making corrections to address issues like quality control, inventory management, and
This document discusses hybrid process models that combine both declarative and imperative modeling techniques. It notes that different parts of a process may be more or less flexible, so a hybrid approach can model this better than a single paradigm. Examples of hybrid approaches include mixing Petri nets with declarative constraints, or having flexible regions within an otherwise rigid workflow. The document also discusses evaluating hybrid modeling through human modeling experiments and automated discovery techniques using event logs. It suggests that while hybrid models seem promising, more work is needed to determine how best to apply different techniques.
The document discusses several prescriptive software development models:
1. The waterfall model is a linear sequential model and was one of the earliest prescriptive models proposed.
2. Variations of the waterfall model include the V-model and incremental model, which allow for some iteration and incremental delivery of features.
3. Evolutionary models like prototyping and the spiral model combine iterative development with controlled aspects of waterfall, producing prototypes and incremental releases to manage risk.
The document discusses various software production process models, including traditional waterfall models, iterative models like the spiral model, and agile methodologies. Waterfall models involve sequential phases from requirements to maintenance but lack flexibility. Iterative models divide the process into increments with feedback between phases. Agile methods like Scrum, Extreme Programming, and Smart emphasize rapid, incremental delivery, automating processes, and customer involvement. The choice of model depends on factors like requirements volatility, team experience, and project priorities.
Making Model-Driven Verification Practical and Scalable: Experiences and Less...Lionel Briand
The document discusses experiences and lessons learned from making model-driven verification practical and scalable. It describes several projects collaborating with industry partners to develop model-based solutions for verification. Key challenges addressed include achieving applicability for engineers, scalability to large systems, and developing solutions informed by real-world problems. Lessons learned emphasize the importance of collaborative applied research, defining problems in context, and validating solutions realistically.
The document discusses several process models for software development projects, including code and fix, waterfall, incremental/iterative, spiral, rapid application development (RAD), and concurrent development models. Each model has advantages and disadvantages depending on factors like project size, requirements stability, and team expertise. Combinations of models may also be suitable in some cases.
This chapter discusses software development processes, project planning, and effort estimation. It introduces several key concepts:
- Software development processes involve a series of steps and activities that produce intended outputs. Common process models include waterfall, iterative development, and agile methods.
- Project planning involves tracking progress, organizing personnel, and estimating effort and schedule. Tools like Gantt charts, histograms, and expenditure tracking can be used.
- Effort estimation methods include expert judgment, algorithmic techniques like COCOMO II, and machine learning approaches. Estimates should be refined repeatedly as uncertainty decreases over the project lifecycle.
Use Case: Airbus and Process Mining TechnologyCelonis
Within the framework of its digital transformation, Airbus is using Celonis software to gain a better understanding of its processes to maximize improvement. In this session we will discuss how our ERP Solution Center is organized to answer all the organization’s business needs for process mining. We will also provide you with an overview of business area uses for Celonis at Airbus and provide a concrete use case of process improvement at the operation level.
Presenters:
Mr. Gildas Lavergne, Head of ERP Solution Center Technologies, Airbus
Ms. Xiaowei Jiang, Product Owner of Process Mining Solutions, Airbus
The document discusses different software development life cycle models, including traditional waterfall models, prototyping models, agile models like XP and Scrum, and process modeling approaches. Traditional models like waterfall are document-driven and plan-heavy, while agile models emphasize rapid iteration, customer feedback, and working software over documentation. There is no single best model, as each project requires a customized approach. Process modeling can help define a project workflow but cannot account for all real-world aspects of software development.
The document discusses various approaches for selecting a project methodology, including whether to build a system in-house or outsource it. It covers the waterfall model, spiral model, prototyping, and incremental delivery. The key aspects addressed are identifying project characteristics and risks to determine the most appropriate software process model. Structured versus agile approaches are weighed in terms of balancing requirements specification with delivery speed.
2011, A POLICY BASED GOVERNANCE FRAMEWORK FOR CLOUD SERVICE PROCESS ARCHITEC...MingXue Wang
This document proposes a software architecture for sharing business processes across multiple tenants in a cloud environment. The key elements are:
- An architectural style called Service-oriented Process Architecture (SPA) that extends SOA principles to allow runtime governance of shared business processes.
- A policy framework that models business rules and policies as metadata for customizing processes on-the-fly based on coordination protocols.
- An aspect-oriented policy (AOP) enhancement that extends the policy framework to support additional customization through aspects modeled as policies.
- An evaluation case study that demonstrates expressing business policies and shows the coordination framework enables on-the-fly process customization for tenants.
A discrete Event Simulation Model of Asphalt Paving Operations, Ramzi Labban ...CCT International
The process of building a simulation model is one of the toughest and time-consuming part of the entire process.
An alternative method and a new approach for creating construction simulation models are provided in the in the presentation above which was presented at the Winter Simulation Conference 2013 in Washington D.C.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers several topics: issues in DSS modeling like identifying problems and variables; categories of models like optimization, simulation, and predictive models; trends like using web tools for modeling; static vs dynamic analysis; decision making under certainty, risk, and uncertainty; and techniques like sensitivity analysis, what-if analysis, and goal analysis. Simulation is described as imitating reality to conduct experiments, and advantages include time compression while disadvantages include lack of optimal solutions.
The term process model is used in various contexts. For example, in business process modeling the enterprise process model is often referred to as the business process model.
1) Umicore implemented a global SAP template to standardize business processes across its 86 industrial sites in order to facilitate growth, exchange of best practices, and global visibility.
2) The global template included developing a Business Process Library documenting standardized processes on SharePoint, implementing a single SAP instance, and establishing centralized master data governance.
3) By taking a clustered rollout approach using the standardized processes and template, Umicore was able to accelerate implementations and reduce costs through economies of scale and shared development efforts.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers various categories of DSS models including optimization, simulation, and predictive models. It also describes static and dynamic analysis, decision making under certainty, risk, and uncertainty. Different modeling approaches like mathematical modeling, simulation, and heuristics are explained.
The document discusses different prescriptive process models for software engineering projects. It describes the waterfall model as the oldest and most basic sequential model. Incremental process models like the incremental model and RAD model deliver functionality in increments to get early user feedback. Evolutionary models like prototyping and the spiral model are iterative and allow for changes through repeated prototype revisions or spiral loops of risk analysis, development and validation.
This document discusses different software process models including waterfall, prototyping, incremental development, spiral, RAD, and V-models. It explains the key stages and benefits and limitations of each model. The document emphasizes that each model tries to provide a framework for software development but that borrowing from multiple models may be necessary. Real-life examples like Windows development are given to illustrate using the spiral model.
Continuous improvement methods summary by the sig rev052914Richard Platt
Summary of Continuous Improvement Methods - since it was originally an Excel file and saved into Adobe Acrobat, you will need to view it at 200X magnification in order to read it legibly
The document provides an overview of various software engineering process models including waterfall, rapid prototyping, incremental, evolutionary, spiral, and agile models like XP. It discusses the main characteristics, advantages, and disadvantages of each model. It also covers the Rational Unified Process (RUP) in detail including its iterative nature, use case driven approach, architecture centricity, and use of UML. Finally, it discusses process improvement frameworks like the Capability Maturity Model (CMM).
Performance modeling provides important insights for capacity planning and system sizing without costly full-scale testing. While sophisticated mathematical modeling was common in the past, today's complex systems are difficult to model formally and existing tools are outdated. However, minimal modeling with common-sense approximations using metrics like resource usage per transaction and hardware capacity can still be useful. Keeping even informal models in mind helps performance engineers understand systems, but complex systems benefit from documenting models. Reviving the art of performance modeling can add value to modern continuous performance testing approaches.
In this project, we investigated the use of association rules to extract useful knowledge from raw ontological data. To this end, we proposed an approach to pass from graph representation to transactional data. Then, we used different technological solutions to improve the performance of frequent item-sets extraction such as the FP-growth algorithm, and Hadoop. Check our code on Github: https://github.com/8-chems/OntologyMiner
1) The document discusses current practices regarding model-based context-aware adaptation (CAA) in industry based on a survey of 33 IT practitioners.
2) While respondents recognized the benefits of CAA, they found that models and context-awareness are not fully incorporated into daily work due to complexity, effort required, and lack of easy-to-use tools.
3) For CAA to be more widely adopted, the survey found that stakeholders need complete tool and framework support to facilitate incorporating models and context into the development process.
The document discusses several software development life cycle (SDLC) methodologies including waterfall, incremental, spiral, scrum/agile, rapid application development, and prototyping. Each methodology takes a different approach such as linear vs iterative processes, emphasis on planning vs flexibility, and when they are best applied based on factors like requirements stability, budget, and team experience.
This document discusses processes and process models. It defines a process as an organized set of activities that transforms inputs to outputs. Process models are simplified descriptions of processes from a particular perspective. There are different types of process models including coarse-grain activity models, which provide an overall picture of a process' context and activities, and fine-grain activity models, which provide more detailed views of specific processes. The document uses the requirements engineering process as an example, providing coarse-grain and spiral models of this software process.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
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Similar to BPM 2014 - The Automated Discovery of Hybrid Processes
The document discusses different software development life cycle models, including traditional waterfall models, prototyping models, agile models like XP and Scrum, and process modeling approaches. Traditional models like waterfall are document-driven and plan-heavy, while agile models emphasize rapid iteration, customer feedback, and working software over documentation. There is no single best model, as each project requires a customized approach. Process modeling can help define a project workflow but cannot account for all real-world aspects of software development.
The document discusses various approaches for selecting a project methodology, including whether to build a system in-house or outsource it. It covers the waterfall model, spiral model, prototyping, and incremental delivery. The key aspects addressed are identifying project characteristics and risks to determine the most appropriate software process model. Structured versus agile approaches are weighed in terms of balancing requirements specification with delivery speed.
2011, A POLICY BASED GOVERNANCE FRAMEWORK FOR CLOUD SERVICE PROCESS ARCHITEC...MingXue Wang
This document proposes a software architecture for sharing business processes across multiple tenants in a cloud environment. The key elements are:
- An architectural style called Service-oriented Process Architecture (SPA) that extends SOA principles to allow runtime governance of shared business processes.
- A policy framework that models business rules and policies as metadata for customizing processes on-the-fly based on coordination protocols.
- An aspect-oriented policy (AOP) enhancement that extends the policy framework to support additional customization through aspects modeled as policies.
- An evaluation case study that demonstrates expressing business policies and shows the coordination framework enables on-the-fly process customization for tenants.
A discrete Event Simulation Model of Asphalt Paving Operations, Ramzi Labban ...CCT International
The process of building a simulation model is one of the toughest and time-consuming part of the entire process.
An alternative method and a new approach for creating construction simulation models are provided in the in the presentation above which was presented at the Winter Simulation Conference 2013 in Washington D.C.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers several topics: issues in DSS modeling like identifying problems and variables; categories of models like optimization, simulation, and predictive models; trends like using web tools for modeling; static vs dynamic analysis; decision making under certainty, risk, and uncertainty; and techniques like sensitivity analysis, what-if analysis, and goal analysis. Simulation is described as imitating reality to conduct experiments, and advantages include time compression while disadvantages include lack of optimal solutions.
The term process model is used in various contexts. For example, in business process modeling the enterprise process model is often referred to as the business process model.
1) Umicore implemented a global SAP template to standardize business processes across its 86 industrial sites in order to facilitate growth, exchange of best practices, and global visibility.
2) The global template included developing a Business Process Library documenting standardized processes on SharePoint, implementing a single SAP instance, and establishing centralized master data governance.
3) By taking a clustered rollout approach using the standardized processes and template, Umicore was able to accelerate implementations and reduce costs through economies of scale and shared development efforts.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers various categories of DSS models including optimization, simulation, and predictive models. It also describes static and dynamic analysis, decision making under certainty, risk, and uncertainty. Different modeling approaches like mathematical modeling, simulation, and heuristics are explained.
The document discusses different prescriptive process models for software engineering projects. It describes the waterfall model as the oldest and most basic sequential model. Incremental process models like the incremental model and RAD model deliver functionality in increments to get early user feedback. Evolutionary models like prototyping and the spiral model are iterative and allow for changes through repeated prototype revisions or spiral loops of risk analysis, development and validation.
This document discusses different software process models including waterfall, prototyping, incremental development, spiral, RAD, and V-models. It explains the key stages and benefits and limitations of each model. The document emphasizes that each model tries to provide a framework for software development but that borrowing from multiple models may be necessary. Real-life examples like Windows development are given to illustrate using the spiral model.
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Performance modeling provides important insights for capacity planning and system sizing without costly full-scale testing. While sophisticated mathematical modeling was common in the past, today's complex systems are difficult to model formally and existing tools are outdated. However, minimal modeling with common-sense approximations using metrics like resource usage per transaction and hardware capacity can still be useful. Keeping even informal models in mind helps performance engineers understand systems, but complex systems benefit from documenting models. Reviving the art of performance modeling can add value to modern continuous performance testing approaches.
In this project, we investigated the use of association rules to extract useful knowledge from raw ontological data. To this end, we proposed an approach to pass from graph representation to transactional data. Then, we used different technological solutions to improve the performance of frequent item-sets extraction such as the FP-growth algorithm, and Hadoop. Check our code on Github: https://github.com/8-chems/OntologyMiner
1) The document discusses current practices regarding model-based context-aware adaptation (CAA) in industry based on a survey of 33 IT practitioners.
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3) For CAA to be more widely adopted, the survey found that stakeholders need complete tool and framework support to facilitate incorporating models and context into the development process.
The document discusses several software development life cycle (SDLC) methodologies including waterfall, incremental, spiral, scrum/agile, rapid application development, and prototyping. Each methodology takes a different approach such as linear vs iterative processes, emphasis on planning vs flexibility, and when they are best applied based on factors like requirements stability, budget, and team experience.
This document discusses processes and process models. It defines a process as an organized set of activities that transforms inputs to outputs. Process models are simplified descriptions of processes from a particular perspective. There are different types of process models including coarse-grain activity models, which provide an overall picture of a process' context and activities, and fine-grain activity models, which provide more detailed views of specific processes. The document uses the requirements engineering process as an example, providing coarse-grain and spiral models of this software process.
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BPM 2014 - The Automated Discovery of Hybrid Processes
1. The Automated Discovery
of Hybrid Processes
Fabrizio M. Maggi
University of Tartu
Tijs Slaats*
IT University of
Copenhagen
Exformatics
Hajo A. Reijers
VU University of
Amsterdam
2. Overview
• Hybrid Process Models
• Discovering Hybrid Process Models
• Evaluation
• Future Work + Conclusion
4. Imperative Process Models
• Flow-oriented
• Well-suited to rigid processes
• In a model with no flow nothing can happen
• Adding flow allows for additional possible
behaviors
• Common in academia and industry
6. Declarative Process Models
• Constraint-oriented
• Well-suited to flexible processes
• In an unconstrained model anything can
happen
• Adding constraints limits behavior
• Still a novelty in industry
8. Hybrid Process Models
• Different parts of the same process
may be more or less flexible.
• Modeling a flexible process imperatively,
or a strict process declaratively, often
leads to incomprehensible models.
• Mixing of paradigms on the sub-process level:
– Pockets of flexibility in workflow services [Sadiq et al.]
– Flexibility as a Service (FAAS) [Aalst et al.]
10. Process Discovery
• Current discovery techniques:
– Mining Petri-nets / Flowcharts
• Alpha miner, Heuristic Miner, ILP miner, …
– Mining Declarative constraints
• Declare miner
• But what if the log contains both flexible and rigid
parts?
– Imperative miners tend to blow-up on flexible logs
– Declarative miners will need to find many constraints to
model the strict parts of the process and will often have
trouble finding all of them (resulting in processes with low
precision)
• Solution: Hybrid Process Discovery!
11. Hybrid Process Discovery
Context
analysis
Clustering
(based on
context analysis)
Clustering
(association rule
mining)
Standard
Process
Discovery
Declare
Discovery
String Edit
Distance
15. Future Work
• Proper plugin for Prom.
• Visualization of resulting hybrid model.
• Further evaluation on real cases.
• Further refinement of the heuristics used in
the approach, for example the thresholds
used for determining if an event is structured
or unstructured.
16. Conclusion
• We offer the first automated approach for
discovering hybrid process models.
• Using the approach on existing logs gives
encouraging results: in particular for semi-structured
logs the discovered models
become more readable.
• Plenty of room for future work in an exciting
new angle on process mining.