Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
MODEL- DRIVEN DSS
includes system that use accounting, financial models, and representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data warehousing, geographical information.
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
MODEL- DRIVEN DSS
includes system that use accounting, financial models, and representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data warehousing, geographical information.
Management Information System (MIS) is a planned system of collecting, storing, and disseminating data in the form of information needed to carry out the functions of management. A Management Information System is an information system that evaluates, analyzes, and processes an organization's data to produce meaningful and useful information based on which the management can take right decisions to ensure future growth of the organization.
MIS, describe Management , information and System , introduction of MIS, definition of MIS , Types of MIS, Implementation of MIS in banking sector, Advantages of MIS, Issues in MIS.
Management Information System (MIS) is a planned system of collecting, storing, and disseminating data in the form of information needed to carry out the functions of management. A Management Information System is an information system that evaluates, analyzes, and processes an organization's data to produce meaningful and useful information based on which the management can take right decisions to ensure future growth of the organization.
MIS, describe Management , information and System , introduction of MIS, definition of MIS , Types of MIS, Implementation of MIS in banking sector, Advantages of MIS, Issues in MIS.
Waterfall Model in SDLC system development life Cycle this model is used to developed software according to the requirement of the Users.... in any business this model is using commonly
Decision Support Systems: Concept, Constructing a DSS, Executive Information ...Ashish Hande
Decision Support Systems: Concept, Constructing a DSS,
Executive Information System, (EIS), Artifical Intelligence
System (AIS), knowledge Based Expert System (KBES),
Enterprise Management System (EMS), Decision Support
Management System (DSMS).
This PPT Covers the following topics:
Decision Making as a Component of Problem Solving, Problem Solving Factors, Characteristics of a DSS, Example of DSS, Integration of TPS, MIS, Web-Based Decision Support Systems, Components of a DSS, Advantages and Disadvantages of Modeling, Group Decision Support System, Executive Support System, Characteristics of ESS.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. Decision Support Systems Concept
• DSS are interactive computer-based systems, which help
decision makers utilize data and models to solve unstructured
problems (Scott Morton, 1971).
• Decision support systems couple the intellectual resources of
individuals with the capabilities of the computer to improve the
quality of decisions. It is a computer-based support system for
management decision makers who deal with semi-structured
problems (Keen and Scott Morton, 1978).
Content-free expression
• There is no universally accepted definition of DSS
Umbrella term vs. narrow definition (specific technology)
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3. What is a DSS
• The term DSS refers to systems which support
the process of decision making.
• DSS may be defined as a “what-if” approach
that uses an information system to assist
management in formulating policies and
projecting the likely consequences of
decisions.
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4. • DSS supports the decision making process
rather than automating the decision making
process.
• DSS allows the decision maker to retrieve data
and test alternative solutions during the
process of decision making .
• DSS may be considered as an extension of
MIS.
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5. Objectives of DSS
• To save time and effort in decision making process.
• To help in processing the collected data and in producing a suggested
solution to a problem.
• To provide sophisticated and fast analysis of vast amount of data and
information.
• To provide support for decision maker at all management levels mainly in
semi-structured or unstructured situation by bringing together human
judgement and computerized information.
• To promote learning, which leads to new demands and refinement of
application.
• To provide efficient and effective solution of every complex problem.
• To check the impact of changes on the proposed solution with help of
“what-if” analysis
• To use the goal seeking analysis to find the value of the inputs necessary
to achieve a desired level of output .
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6. Characteristics of a DSS
• Provide rapid access to information
• Handle large amounts of data from different sources
• Provides report and preparation flexibility
• Offer both textual and graphical orientation
• Support drill-down analysis
• Perform complex, sophisticated analysis and comparison using
advanced s/w
• Support optimization, satisfying and heuristic approaches.
• Goal seeking analysis
• Simulation
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7. Capabilities of DSS
• Support for problem solving Phase
• Support for different decision frequencies
• Support for different problem structures
• Support for various decision-making levels
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8. Difference between DSS and MIS
MIS DSS
• MIS focuses on structured tasks and • DSS focuses on semi-structured
routine . tasks which require managerial
judgements
• MIS emphasis on data storage
• DSS emphasis on data
• In MIS, data is often accesses
manipulation
indirectly by managers
• In DSS, data is directly by managers
• MIS puts reliance on computer
experts • DSS puts reliance on manager’s
own judgement.
• MIS places emphasis on efficiency
of decision • DSS places emphasis on
effectiveness of decision
• MIS provides tactical information to
top management to take decisions. • DSS provides strategic information
• MIS are regular and recurring • The need for DSS can be irregular.
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9. Types of DSSs
• Data -oriented
• Model-oriented DSS
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11. Data -oriented DSS
• DSS- database plays a vital role in structure of DSS, It provide the
data retrieval, analysis and presentation. Very often data from TPS
are collected in data warehouse and this data can be analyzed
with use of online analytical processing and datamining. Data
warehouse integrate multiple databases and other information
sources into a single repository or access point that is suitable for
direct querying, analysis or processing., whereas the datamining
(data mining is the process of finding correlations or patterns
among dozens of fields in large relational databases.) is refers to
extracting or ‘mining ‘ knowledge from large amount of data.
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12. File Drawer Systems
• They are the simplest type of DSS
• Can provide access to data items
• Data is used to make a decision
• ATM Machine
Use the balance to make transfer of funds
decisions
13. Data Analysis Systems
• Provide access to data
• Allows data manipulation capabilities
• Airline Reservation system
No more seats available
Provide alternative flights you can use
Use the info to make flight plans
14. Analysis Information Systems
• Information from several files are combined
• Some of these files may be external
• We have a true “data base”
• The information from one file, table, can be
combined with information from other files
to answer a specific query.
15. Model based DSSs
• Model based DSSs use some type of model to
perform ‘what-if’ and other kind of analysis
to make decisions. These systems include
activities such simulation, optimizing
scenario .this type of DSSs usually are
developed by persons with management
science background.
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16. Accounting Models
• Use internal accounting data
• Provide accounting modeling capabilities
• Can not handle uncertainty
• Use Bill of Material
– Calculate production cost
– Make pricing decisions
17. Representational Model
• Can incorporate uncertainty
• Uses models to solve decision problem using
forecasts
• Can be used to augment the capabilities of
Accounting models
• Use the demand data to forecast next years
demand
• Use the results to make inventory decisions.
18. Optimization Systems
• Used to estimate the effects of different
decision alternative
• Based on optimization models
• Can incorporate uncertainty
Assign sales force to territory
Provide the best assignment schedule
19. Suggestion Systems
• A descriptive model used to suggest to the
decision maker the best action
• A prescriptive model used to suggest to the
decision maker the best action
• May incorporate an Expert System
• Use the system to recommend a decision
• Ex: Applicant applies for personal loan
20. Information from Datamining
• Association: correlation relationship
• Sequence: time series analysis
• Classification and prediction
• Clustering
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21. Elements of DSS
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22. Elements of DSS
• USERS
• Databases
• DSS software- DBMS, MMS(Model
Management Software)
• Model base: Optimization models, Forecasting
Models and Sensitivity analysis models
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24. Working of DSS
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25. Applications of DSS
• DSSs support unstructured and semi-
structured decisions
• DSS support different decision frequencies.
• DSS support different problems structures
• DSS support various decision making levels
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26. Advantages of DSS
• As DSS reduces the time and efforts in collecting and analysis of
data for different sources, a large number of alternatives can be
evaluated .
• As modelling and forecasting is made easy by DSS, managers gets
more insights into business processes. Thus, it enables a thorough
quantitative analysis in a very short time. Even major changes in a
scenario can be evaluated objectively in a timely manner.
• DSS makes it possible to explain to others the basis for arriving at a
particular conclusion.
• DSS allows the decision makers to interact in a natural manners due
to the careful design of the interface.
• Cost saving
• Improve managerial effectiveness
• Support of individual/ groups
• Use and control rests with the user and not with the EDP dept.
• Flexible and adaptive.
• Improve the effectivenessKrof the LMTSOM, Thapar
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27. Tools of DSSs
• Material Requirement Planning (MRP) is method for ordering and
maintaining materials stocks cost effectively.
• Linear Programming: PERT, CPM
• Queuing Theory
• Descriptive statistics
• Correlation analysis
• Variance analysis
• Network analysis
• Transportation problems
• Maximum flow or distance
• Regression analysis
• Multi dimensional scaling
• Dynamic programming
• Probability theory
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28. GROUP DSS
• The DSS approach has resulted in better decision making for
all levels of individual users.
• However, Many DSS approaches and techniques are not
suitable for a group decision-making environment.
• Although not all workers and managers are not involved in
committee meetings and group decision- making sessions,
some tactical and strategic-level managers spend more than
half their decision-making time in group settings.
• Such managers need assistance with group decision making.
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29. • A Group Support System( GSS), Also called a
group decision support system and a
computerized collaborative work system,
consists of most of the elements in DSS, plus
s/w to provide effective support in group
decision-making settings
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30. Characteristics of a GDSS that
Enhance Decision Making
• Special Design
• Ease of use
• Flexibility
• Decision-Making Support
• Anonymous Input
• Reduction of Negative Group Behavior
• Parallel Communications
• Automated recordkeeping
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31. GSS software
• GSS s/w, often called groupware or workgroup
s/w, helps in joint group scheduling,
communication, and management.
• Eg s/w from Autodesk is helping design the
1776’ tall Freedom Tower to replace the twin
towers of World Trade Center in New York city
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32. In addition to groupware, GSSs use
a number of tools
• Email and Instant Messaging
• Videoconferencing
• Group Scheduling
• Project Management
• Document sharing
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33. GSS Alternatives
• Decision room
• The local area decision network
• Teleconferencing
• Wide area decision network
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36. Components of GDSS
• Hardware
• Software
• People
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37. GDSS software includes
• Electronics questionnaires
• Electronics brainstorming tool
• Idea organiser
• Tools for voting and setting priorities.
• Policy formulation tool
• Group dictionaries
•
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38. Example of DSS in Accounting system
• Budgeting models
• Cost analysis model
• Break even analysis
• Evaluation of funds and investment
• Cash and funds flow model for budegting
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39. • Manoratty na sidhayanti karayani, Suptasay
sihangasay mukhe mriga na nivsanti
• Hardwork is the key of Success. Never
compromised with hardwork,
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