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Casual Articles - Decision Support Systems(DSS) - Is it becoming a Decision Making Systems(DMS)?
Creative Writing is something that everyone is capable of. And we can all improve our creative writing – and our enjoyment of it - in a great number of ways.One of the key factors to writing creatively and freely is confidence.It doesn’t matter how talented you are as a writer, if you lack the confidence to write and to explore your writing potential you simply won’t create as well or as often as you’re capable of creating.So here are 5 powerful tips to help you increase YOUR self-confidence as a creative writer –1. Believe you’re creative. If you didn’t believe you were capable of writing creatively at all, you wouldn’t even be trying, so you’re off to a great start. Now you can build on this core belief.One way of doing this is to spend some time visualizing how your life would be if you were wildly successful as a creative writer, whatever “wildly successful” means to you.Put yourself into this future visualization of yourself as strongly as possible. Ask yourself what beliefs you hold about yourself that have enabled you to be this creative, this successful. Then start bringing them into your life today.2. Use your senses. So much of the time we walk round virtually oblivious to the highly sensory world around us. We may as well stick cotton wool in our ears, a sock in our mouth and bag over our heads!It’s through our senses we connect with and experience the world. Take some time to go somewhere new and practice using your senses. Concentrate on each of your senses one at a time, what you’re really seeing, hearing, smelling, tasting and
The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules.
These systems provide-
Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledge based (management) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component-
- Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
You see, by presenting collateral, you are balancing out the fact that you are unemployed and have lost your steady income. Essentially, when you apply for a loan for unemployed and offer your home as collateral, you are basically telling the lender, ok if I default on this loan, you can have my home. The equity within your home is how the value of the loan is determined. Basically, you will not ever get more than your home is worth in fair market value, or the price that the home would likely sell for if placed on the market. A home equity loan is one of the loan types that many people seeking loans for unemployed look for.For the unemployed, a home equity loan is the best resource, which is also cheaper than other sources of finance. In general, a lender will have the understanding that a borrower would not risk their home, thus have a better chance of retrieving the money loaned. If you were to take a loan for unemployed and default on the loan, you would lose your home, which no person wants in life. Therefore, the lenders have a security net when providing loans for unemployed, with a home equity loan, in the fact that you are putting your own home at risk, if you fail to make timely payments.A home equity loan and how the loan is used should be carefully considered. A major determining factor of just how the money is used and how quickly, is how long you could be unemployed. If you are only temporarily unemployed such as with a season position or temporary lay off, you know that you will have a steady income streaming in shortly. Therefore, you can spend the money as necessary, making sure you have enough to ma
- The role played by DSS in the process of decision making
- The changes coming in scenario about the role of DSS in decision making.
Decision Support Systems (DSS) are a class of computerized information system that supports decision-making activities. DSS are interactive computer-based systems and subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to complete decision process tasks.
Decision Support Systems have evolved over the last 25 years from inflexible mainframe systems, to isolated PC tools, to client/server data dippers, and now to high-performance and extensible enterprise decision-support applications, often involving the organization’s intranet. At the same time, the relationship between the IT Department and users has evolved from stormy to co-operative.
The huge umbrella of decision support systems (DSS) has long provided a welcome gathering spot for those interested in building software applications based on a mixture of models, data analysis, and powerful interfaces. DSS attracts practitioners, scholars and students from a range of fields including information systems, operations research/management science, computer science, psychology and other business disciplines.
The problem: There has been a virtual revolution in terms of spreadsheet based management science and operations management courses that seems to have stuck in business schools. Spreadsheets have evolved into a quite capable platform for end-user decision support modeling.
For example, within Microsoft Excel, this evolution has resulted in the inclusion of Solver for optimization, Pivot Tables, database connectivity, numerous mathematical and statistical functions and the Visual Basic for Applications (VBA) programming language.
The problem is coming from this picture where instead of using management skills for making decision, managers are very much dependent on DSS tools for making decisions. It might be more crucial when new managers will have lack of management skills and they will totally dependent on DSS tools.
So, we can make questions:
- What are the reasons behind that managers are depending so much on DSS tools?
- What should be the optimized ratio of using desktops and management skills for decision making?
- A decision-maker: an individual or group charged with making a particular decision.
- A set of inputs to the decision-making process: data, numerical or qualitative models for interpreting that data, historical experience with similar data sets or similar decision-making situations, and various kinds of cultural and psychological norms and constraints associated with decision-making
- The decision-making process itself: a set of steps, more or less well-understood, for transforming the inputs into outputs in the form of decisions,
- A set of outputs from the decision-making process, including the decisions themselves and (ideally) a set of criteria for evaluating decisions produced by the process against the set of needs, problems or objectives that occasioned the decision-making activity in the first place.
- As soon as we look at this model, we realize that talking about decision support systems outside of a particular domain of decision-making is not particularly useful.
If we considered only the timeframe in which a given decision has to be made and the risks and constraints associated with the decision-making process, we would recognize that there is a great deal of qualitative and quantitative difference between governmental agencies, not-for-profit (NFP) organizations, and commercial firms. Put simply, commercial decisions, in the aggregate, have the shorter timeframes and higher associated risks (including extinction) than either public sector or not-for-profit decisions, and as such would presumably require the most assistance from information technology.
For this reason alone, this essay limits its scope to commercial decision support systems: IT infrastructure designed to support the decision-making processes in publicly-held and private firms that compete in open markets for customers, revenue and market share.
How do DSS environments support decision-making? DSS environments support the generic decision-making model above in a number of ways:
- In decision preparation, DSS environments provide data required as input to the decision-making process. This is all about data mart and data warehousing environments do today.
- In decision structuring, DSS environments provide tools and models for arranging the inputs in ways that make sense to frame the decision. These tools and models are not pivot tables and other aspects of data presentation found in query tools. They are actual decision making tools, like fault tree analysis, Bayesian logic and model-based decision-making based on things like neural networks.
- In context development, DSS environments again provide tools, and provide the mechanisms for capturing information about a decision’s constituencies (who’s affected by this decision), outcomes and their probabilities, and other elements of the larger decision making context.
- In decision-making, DSS environments may automate all or part of the decision-making process and offer evaluations on the optimal decision. Expert systems and artificial intelligence environments purport to do this, but they work only in very limited cases.
- In decision propagation, DSS environments take the information gathered about constituencies and dependencies and outcomes and drive elements of the decision into those constituencies for action.
- In decision management, DSS environments inspect outcomes days, weeks and months after decisions to see if (a) the decision was implemented/propagated and (b) if the effects of the decision are as expected.
- Pick the class of decision-making processes to focus on,
- Narrow the range of inputs, the range of activities and the differences in models and methods,
- Most importantly, to understand where technology ceases to play any meaningful role in decision-making, and where policy becomes the determinant of the quality and quantity of decisional effectiveness.
- Database
- Database management system
- Data directory ( A database must contain data about the tables & all other objects)
- Query facility
Typical information that a decision support application might gather and present would be:
- Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts.
- The consequences of different decision alternatives, given past experience in a context that is described.
- Projected revenue figures based on new product sales assumptions.
The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules.
These systems provide-
Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledge based (management) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component-
- Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
If an organization lacks a mission statement, it is worthwhile to at least try to draft one. Even if it does not yield an acceptable final draft, the exercise will be rewarding for the hard work which must go into figuring out the company’s direction and putative purpose.Mission statements can help companies determine their proper market niche, suggest new fields for the company to conquer, and even serve as a constraint, indicating, perhaps only implicitly, enterprises in which they should refrain from participating. Without the mission statement, getting sidetracked gets easier. Mission statements can further the cause of sound planning in other ways, too. The act of devising a statement forces management to take a hard look at its external threats and opportunities and at its internal competencies. If this part of the planning process had been neglected, drafting a mission statement will perhaps focus attention on such potential weak areas.For startup companies and businesses in transition, a mission statement is critical. A mission statement is a charter that defines the basic business in which the enterprise will engage, the types of products it will make or the services it will provide, the markets it will serve, and perhaps how the company will conduct its affairs -- in other words, its purpose. Some managers may tend to regard its preparation as an academic exercise in verbal hairsplitting that offers little guidance in making hard decisions. Indeed, some mission statements are so broad and vague as to recall the prophecies of ancient oracles; others are mere public-relations floss. But if done well, a missio
The problem is coming from this picture where instead of using management skills for making decision, managers are very much dependent on DSS tools for making decisions. It might be more crucial when new managers will have lack of management skills and they will totally dependent on DSS tools.
So, we can make questions:
- What are the reasons behind that managers are depending so much on DSS tools?
- What should be the optimized ratio of using desktops and management skills for decision making?
- A decision-maker: an individual or group charged with making a particular decision.
- A set of inputs to the decision-making process: data, numerical or qualitative models for interpreting that data, historical experience with similar data sets or similar decision-making situations, and various kinds of cultural and psychological norms and constraints associated with decision-making
- The decision-making process itself: a set of steps, more or less well-understood, for transforming the inputs into outputs in the form of decisions,
- A set of outputs from the decision-making process, including the decisions themselves and (ideally) a set of criteria for evaluating decisions produced by the process against the set of needs, problems or objectives that occasioned the decision-making activity in the first place.
- As soon as we look at this model, we realize that talking about decision support systems outside of a particular domain of decision-making is not particularly useful.
If we considered only the timeframe in which a given decision has to be made and the risks and constraints associated with the decision-making process, we would recognize that there is a great deal of qualitative and quantitative difference between governmental agencies, not-for-profit (NFP) organizations, and commercial firms. Put simply, commercial decisions, in the aggregate, have the shorter timeframes and higher associated risks (including extinction) than either public sector or not-for-profit decisions, and as such would presumably require the most assistance from information technology.
For this reason alone, this essay limits its scope to commercial decision support systems: IT infrastructure designed to support the decision-making processes in publicly-held and private firms that compete in open markets for customers, revenue and market share.
How do DSS environments support decision-making? DSS environments support the generic decision-making model above in a number of ways:
- In decision preparation, DSS environments provide data required as input to the decision-making process. This is all about data mart and data warehousing environments do today.
- In decision structuring, DSS environments provide tools and models for arranging the inputs in ways that make sense to frame the decision. These tools and models are not pivot tables and other aspects of data presentation found in query tools. They are actual decision making tools, like fault tree analysis, Bayesian logic and model-based decision-making based on things like neural networks.
- In context development, DSS environments again provide tools, and provide the mechanisms for capturing information about a decision’s constituencies (who’s affected by this decision), outcomes and their probabilities, and other elements of the larger decision making context.
- In decision-making, DSS environments may automate all or part of the decision-making process and offer evaluations on the optimal decision. Expert systems and artificial intelligence environments purport to do this, but they work only in very limited cases.
- In decision propagation, DSS environments take the information gathered about constituencies and dependencies and outcomes and drive elements of the decision into those constituencies for action.
- In decision management, DSS environments inspect outcomes days, weeks and months after decisions to see if (a) the decision was implemented/propagated and (b) if the effects of the decision are as expected.
- Pick the class of decision-making processes to focus on,
- Narrow the range of inputs, the range of activities and the differences in models and methods,
- Most importantly, to understand where technology ceases to play any meaningful role in decision-making, and where policy becomes the determinant of the quality and quantity of decisional effectiveness.
- Database
- Database management system
- Data directory ( A database must contain data about the tables & all other objects)
- Query facility
Typical information that a decision support application might gather and present would be:
- Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts.
- The consequences of different decision alternatives, given past experience in a context that is described.
- Projected revenue figures based on new product sales assumptions.
The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules.
These systems provide-
Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledge based (management) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component-
- Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
According to UK research a far greater number of couples without children rent privately than they do within the social sector. This could be because they are unable to secure social housing, or it could be that this particular segment of households is more transient and find private rental accommodation more appropriate for their needs. To explore further it is probably worth looking in a little more detail at some recent survey data to understand the demographics of household types in the rental sector.Couples without dependent child (or children); 6% in the social sector, 28% in the private sector.Couples with dependent child (or children); 24% in the social sector, 7% in the private sector.Single parent with dependent child (or children); 44% in the social sector, 8% in the private sector.Other multiple person households; 3% in the social sector, 27% in the private sector.One person households; 23% in the social sector, 30% in the private sector. (Note that the above data is based on a UK survey of the rental sector for those below 30 years of age)Looking at this data it would suggest that the dominant area for social housing is young people with children, conversely the private sector is dominated by those without children. This view is further supported by the average tenure of a rental property, based on the view that those with children move home less frequently. Research data found on average tenure for the UK rental sector: Private housing sector; 91% of properties rented for 3 years or less; 58% for one year or less.Social ho
For this reason alone, this essay limits its scope to commercial decision support systems: IT infrastructure designed to support the decision-making processes in publicly-held and private firms that compete in open markets for customers, revenue and market share.
How do DSS environments support decision-making? DSS environments support the generic decision-making model above in a number of ways:
- In decision preparation, DSS environments provide data required as input to the decision-making process. This is all about data mart and data warehousing environments do today.
- In decision structuring, DSS environments provide tools and models for arranging the inputs in ways that make sense to frame the decision. These tools and models are not pivot tables and other aspects of data presentation found in query tools. They are actual decision making tools, like fault tree analysis, Bayesian logic and model-based decision-making based on things like neural networks.
- In context development, DSS environments again provide tools, and provide the mechanisms for capturing information about a decision’s constituencies (who’s affected by this decision), outcomes and their probabilities, and other elements of the larger decision making context.
- In decision-making, DSS environments may automate all or part of the decision-making process and offer evaluations on the optimal decision. Expert systems and artificial intelligence environments purport to do this, but they work only in very limited cases.
- In decision propagation, DSS environments take the information gathered about constituencies and dependencies and outcomes and drive elements of the decision into those constituencies for action.
- In decision management, DSS environments inspect outcomes days, weeks and months after decisions to see if (a) the decision was implemented/propagated and (b) if the effects of the decision are as expected.
- Pick the class of decision-making processes to focus on,
- Narrow the range of inputs, the range of activities and the differences in models and methods,
- Most importantly, to understand where technology ceases to play any meaningful role in decision-making, and where policy becomes the determinant of the quality and quantity of decisional effectiveness.
- Database
- Database management system
- Data directory ( A database must contain data about the tables & all other objects)
- Query facility
Typical information that a decision support application might gather and present would be:
- Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts.
- The consequences of different decision alternatives, given past experience in a context that is described.
- Projected revenue figures based on new product sales assumptions.
The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules.
These systems provide-
Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledge based (management) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component-
- Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
They say that in literature everything has already been said (written). If you want to write a novel, you should differentiate on style rather than on anything else.And this is not less true in business. Our “Style Compass” seems familiar with elements from the model of Myers-Briggs and also with the Competing Values Framework. And perhaps with many other concepts.Recently I found another reference with similarities; the so-called Left-Hand/Right-Hand model being the brainchild of Professor John Donovan of MIT. This model is about managing or leading innovations:...Its main tenet is that all organizational functions can be divided into two categories-the standard quotidian efforts at status quo maintenance and risk minimization, and the riskier ventures into new fields or endeavours. The former is labelled the right-hand, while the latter is the left-hand. It is within this Left-Hand sector that innovation occurs, through the dual paths of employing revolutionary processes and leading customers. To delve into new territory, the organization must lead its customers, rather than solely listening and responding to their wants and concerns. (www.cellexchange.com/pdfs/InnovationandCollaboration.pdf)If you are interested in culture, values, personal preferences, organizational Style, etc, you should use what is close to what is already known in the organization. More important is that you use one concept from start to finish or even longer. Because the topic about managing innovation and stabilizing business success is of all times.If you are lucky to have ever produced a fil
- Pick the class of decision-making processes to focus on,
- Narrow the range of inputs, the range of activities and the differences in models and methods,
- Most importantly, to understand where technology ceases to play any meaningful role in decision-making, and where policy becomes the determinant of the quality and quantity of decisional effectiveness.
- Database
- Database management system
- Data directory ( A database must contain data about the tables & all other objects)
- Query facility
Typical information that a decision support application might gather and present would be:
- Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts.
- The consequences of different decision alternatives, given past experience in a context that is described.
- Projected revenue figures based on new product sales assumptions.
The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules.
These systems provide-
Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledge based (management) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component-
- Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
Stated in simple terms, strategy is a complete plan designed specifically for attaining the objectives of the firm. The objectives indicate what the firm wants to achieve; the strategy provides the design for achieving them. It is the strategy that decides the success at the business unit level, which in turn decides the total corporation’s success. The linkage between strategy and overall corporate success is indeed direct and vital. And in this linkage lies the significance of strategy.Since realizing the objectives is the purpose of strategy, it is only logical that strategy takes its direction and cue from the objectives of the firm. Strategy is not a nebulous idea. It is a well-outlined game plan. And there are definite ways of formulating it. Basically, the formulation of a strategy consists of two steps: Selecting the target market and assembling the marketing mix.The essence of the strategy of any firm can be grasped from the firm’s target market and its marketing mix. The target market shows to whom the firm intends to sell the products; the marketing mix shows how the firm intends to sell. Together, they constitute the strategy platform of the firm.To say that target market selection is a part of strategy development is an understatement. It does not fully bring out the import of the inseparable linkage between the two. When the selection of the target market is over, an important part of the strategy of the firm is already determined, defined and expressed. In effect, target market selection boils down to deciding- what parts of the market are we going to serve? What parts of the market do we choose
The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules.
These systems provide-
Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledge based (management) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component-
- Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
Knowledge Driven: - Knowledge-driven DSSs or 'knowledge base' are they are known, are a catch-all category covering a broad range of systems covering users within the organization setting it up, but may also include others interacting with the organization - for example, consumers of a business. It is essentially used to provide management advice or to choose products/services. The typical deployment technology used to set up such systems could be client/server systems, the web, or software running on stand-alone PCs.
Model Driven: - Model-driven DSSs are complex systems that help analyze decisions or choose between different options. These are used by managers and staff members of a business, or people who interact with the organization, for a number of purposes depending on how the model is set up - scheduling, decision analyses etc. These DSSs can be deployed via software/hardware in stand-alone PCs, client/server systems, or the web.
Document Driven: - Document-driven DSSs are more common, targeted at a broad base of user groups. The purpose of such a DSS is to search web pages and find documents on a specific set of keywords or search terms. The usual technology used to set up such DSSs is via the web or a client/server system. Examples:
Data Driven: - Most data-driven DSSs are targeted at managers, staff and also product/service suppliers. It is used to query a database or data warehouse to seek specific answers for specific purposes. It is deployed via a main frame system, client/server link, or via the web. Examples: computer-based databases that have a query system to check (including the incorporation of data to add value to existing databases.
Conclusion and further work: The challenge for any organization considering DSS environments is the most complex. Organizations that deploy DSS technologies, but do not enforce decision-making policy, cannot expect to derive significant returned business value from their DSS environments, since the ultimate value of a decision is in its implementation and management: areas that DSS environments cannot, by definition, support.
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