Question

A united Nation's report analyzing population growth across the world has determined that the world population will be close to 10 billion people bt 2075. This means that the food we grow and eat will need to be done faster. The development of the agriculture sector is considered vital to our economy, and as the world is very much driven by the technology, it is useful to find out how Decision Support System (DSS) can help to address this concern.

a. Identify 3 types of decision that can be supported by the DSS based on scenario above. Provide an example for each type of decision.

b. Explain how the organization is able to gain competitive advantage against its competitors by applying DSS.

c. Briefly explain 3 possible risk in implementing a DSS project above.

d. based on your answer in (a). suggest an intelligent development technique that can be applied to support the decision-making. Justify the suggestion.

e. Using appropriate example. discuss how DSS can benefit from the intergration with an emerging technology or artificial intelligent technique.

f. what are he challenges in designing and implementing such system in (d)?

Answer 1

Answer:-

a)ans

. Identify 3 types of decision that can be supported by the DSS based on scenario above. Provide an example for each type of decision.

• Communication-driven DSS
  Most communications-driven DSSs are targetted at internal teams, including partners. Its purpose are to help conduct a meeting, or for users to collaborate. The most common technology used to deploy the DSS is a web or client server. Examples: chats and instant messaging softwares, online collaboration and net-meeting systems
• Data-driven DSS
  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.
• Document-driven DSS
  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 are via the web or a client/server system

b)ans

Simple decisions can be made immediately without much consideration. But complex decisions are given more time and thought because they directly hit the bottom line of a business. Decision support system carry out the decision making process in a structured manner and helps decision makers identify the best acceptable solution for a specific problem. Let’s take an in-depth look at how a decision support system helps businesses remain at the forefront of competition:

• Shorter Decision Making Cycle: Time plays a substantial role no matter which industry you operate in and what level you work at. Since inception, all categories of decision support systems are intended towards simplifying things and saving time. A DSS helps a business in quickly making an effective decision by analyzing its pros and cons. The time taken in studying data and comparing the possible courses of actions is significantly reduced. The decision time cycle gets shorter, allowing businesses to act speedily in a given situation, ultimately reducing the time-to-market.
• Increased Data Accuracy: Businesses had several versions of truth before DSS came into existence. In fact, the problem still sustains for those who haven’t started using any type of decision support system. The reason is that analysis and interpretation of data involves human bias. Since each evaluator thinks differently, there can never be a universal truth. A decision support system analyzes data without any bias and presents it in its accurate form. This enhances the possibility of improved decision making.
• Strategic Significance: Decision Support System changes the way businesses operate. An important concept that brings to light the role of computerized decision making is “value chain management”. A decision support system takes into account economical factors and past and current trends to determine the costs and profits and the overall value. It provides different outcomes or courses of actions that are economically distinct and offer different values. In short, it notifies the decision maker of the best course of action that creates higher value at the lower cost. Definitely, a business gains competitive advantage when it is successful and when the value it creates surpasses the cost incurred on it.
• Direction and Nimbleness: Moving in the right direction as quickly as possible helps businesses stay ahead of the competition. In fact, this is what keeps organizations agile and proactive. It’s essential for them to quickly respond to market changes. Manual processing of data takes longer; thus, defeats the whole idea of nimbleness. A decision support system, using the available information, presents projected revenue figures and expected market changes in the times ahead. For example, a real estate company relies on a DSS to decide how to set the prices for each apartment so that maximum of them are sold and the profits reach the uppermost limit. In today’s global marketplace, it’s crucial that companies move in the right direction, from the beginning.
• Reduced Cost of Decision Making: The deployment of a decision support system dramatically reduces the cost of gathering, sorting, processing and analyzing data. Managers have a growing awareness that automated decision support is no longer an exclusive territory. In fact, the cost of information storage, hardware and computer and internet technology is falling considerably. This means the cost of distributing decision-making technology even to the lower levels of hierarchy is decreasing. The application of DSS will no longer be restricted to the certain departments or hierarchical levels. The faster decision making at all levels of management will help businesses stand apart from the pack.

c)ans

In general, user expectations, budget, architecture, project requirements, priorities, technology understanding, and database size all impact our assessment of project scope. Management commitment, schedules, staff training, project manager skills, organization structures, and procedures all impact project structure.

DSS projects have various levels of risk associated with them. When DSS projects have ambiguous objectives and low structure, the projects have higher levels of risk of failure because the costs and scope of work of the project are harder to define. Also, because the objectives of the project are ambiguous, it can be difficult to assess the return on the investment. DSS projects with a higher degree of structure and more clearly defined objectives generally are lower risk and more likely to be successfully completed. Also, more detailed planning is possible for projects with specific objectives. The scope of a DSS project in terms of the number of users served and the size of databases developed also impacts the risk of the assessed projects. Small DSS projects in terms of scope or dollar expenditures tend to be lower risk than large projects. Reducing project risk is one reason we use pilot projects. Finally, the sophistication of the technology and the experience of the developers using the technology influences the overall project failure risk. Innovative decision support inherently has moderate to high failure risk.

If project scope and structure are considered in a 2X2 matrix, then low scope and high structure projects have low risk. High scope and low structure projects have high levels of risk. The other two situations generally have more moderate levels of implementation risk. See Figure 1 "Project Implementation Risk Matrix".

Project Implementation Risk Matrix Low High Project Structure High High Risk Moderate Risk Moderate Risk Low Risk Low

According to Asuret (http://asuret.com), "some research estimates that over half of all IT projects are over-budget, late, or don't deliver the expected scope." We want to reduce this risk. Good project management reduces or mitigates some of Adelman's risk factors during implementation. Some micro level risk factors can be assessed prior to the start of a project and hence potentially managed. During project implementation, a good project manager continually monitors risk factors. A project manager is responsible for the overall on-time, on-budget delivery of the project and s/he should manage the risks.

d)ans one technique that is good

A Systematic Approach for Making Decisions

In real-life business situations, decisions can often fail because the best alternatives are not clear at the outset, or key factors are not considered as part of the process. To stop this happening, you need to bring problem-solving and decision-making strategies together to clarify your understanding.

A logical and ordered process can help you to do this by making sure that you address all of the critical elements needed for a successful outcome.

Working through this process systematically will reduce the likelihood of overlooking important factors. Our seven-step approach takes this into account:

1. Create a constructive environment.
2. Investigate the situation in detail.
3. Generate good alternatives.
4. Explore your options.
5. Select the best solution.
6. Evaluate your plan.
7. Communicate your decision, and take action.

e)ans

Decision Support Systems (DSS) and Artificial Intelligence to find and evaluate markets for prospective products. In addition to determining these markets, DSS will then be used to predict the success of the market and other product lines that can be brought to those markets, making companies more successful in the structuring of marketing models and product lines.

[keywords] Decision support system; artificial intelligence; marketing; DSS; marketing model; 1ST; IS; global marketing

Introduction

Information Systems Technology (1ST) is becoming a more prominent part of global marketing. With the aid of 1ST, companies can become competitive in all phases of customer relations (Ives and Learmonth, 1984). The use of information technology for finding markets is expanding, enabling companies to keep up with prospective markets in today's dynamic economy. 1ST accomplishes this feat by helping marketing departments determine targets for their products and charting the most effective way to cover the largest market in the shortest amount of time. They also enable marketing to establish trends so that new products

f)ans

• Difficulty in Quantifying All the Data: A decision support system majorly relies on quantifiable data. Consequently, it’s difficult to analyze intangible or indefinable data. In reality, some values cannot be very specific and defined in numbers. Even though a DSS may quantify some of these aspects, the end result must be duly considered by the decision makers. They must use their own judgment when making the final decision.
• Unaware of Assumptions: As a decision maker, you may not be fully aware of the assumptions that a decision support system has considered when analyzing data for a specific problem. Making decision without considering uncontrollable factors may prove to be dangerous. A decision maker must realize that a computerized DSS is only a supporting tool. You must consider an unstructured or partially structured situation in-depth and analyze the limitations and assumptions.
• System Design Failure: Decision support systems are designed to the specific needs of a decision maker. If you don’t know what you want a DSS to do or how it should help you, it will be difficult to design a system that fits your needs. And when you use a vague DSS, the results generated are not what you’re looking for. Such situations may arise because of system design failure.
• Difficulty in Collecting All the Required Data: As a decision maker, you must realize that it’s not possible to capture all of the related data mechanically. While some data is difficult to record, some cannot be recorded at all. Therefore, the value presented by a DSS may not be 100% true.
• Lack of Technology Knowledge in Users: Although decision support systems have become much simpler over the years, many decision makers still find it difficult to use. Lack of technological knowledge remains an issue.