1.2 Problem Solving: The Most Important Skill in Information Systems
An example of a problem solving that instead exploits an opportunity is a brick and mortar business, such as a furniture store, that sees an opportunity to increase sales by adding the ability to sell online. An IS professional exploits that opportunity by determining and designing the best option for selling online. Designing a solution to the opportunity facing the furniture store is considered “problem solving.”
Related to problem solving, employers have indicated the main capabilities expected of all IS graduates. 1 These include the ability to:
Improve Organizational Processes
Exploit Opportunities Created by Technology Innovations
Understand and Address Information Requirements
Identify and Evaluate Solution and Sourcing Alternatives
Design and Manage Enterprise Architecture
Secure Data and Infrastructure
Understand, Manage, and Control IT Risks
The capabilities in the list above may be somewhat unfamiliar to you right now, but recognize that the ones that have been italicized require problem-solving skills. Therefore, regardless of the IS classes that an IS graduate may take when completing a degree, over half of the capabilities employers expect of graduates involve the ability to solve problems in a technology context. For example, improving organizational processes means that an IS professional needs to first understand what is wrong with an organizational process, and then design a solution. Similarly, exploiting an opportunity means that an IS professional must understand the opportunity, then design a solution with a technology that takes advantage of the opportunity. Understanding and addressing information requirements means that an IS professional needs to understand what is wanted from stakeholders, and then meet those wants through a solution that the professional designs. Lastly, identifying and evaluating solutions and sourcing alternatives means that an IS professional first understands a problem to be solved, and then thoughtfully selects the best way to solve the problem, which will include alternatives such as either building custom software from scratch or buying existing software that other software companies have already created.
The job placement statistics for IS graduates provide further evidence of the importance of problem-solving skills. According to a 2019 job index report sponsored by AIS and Temple University, the leading job categories for graduates include the following: 2
Computer Systems Analyst
The primary responsibility of professionals working in IT Consulting and Computer Systems Analyst jobs is to solve problems for organizations using technology. Together, these two job categories represent over one-fourth of the jobs in IS. Other job roles in IS also require significant problem-solving skills, even if those skills aren't considered a primary responsibility. Consider the role of a software developer. They might think of their job as merely writing code, but in reality, they are asked to do far more than this by providing solutions to important organizational problems. For example, they may be asked to solve the business problem of not having web-based payment options for customers, or they may be asked to solve the problem of expensive and inefficient public transportation (think of Uber as a solution), or they may be asked to solve the problem of a sales team that has no means of accessing organizational data when they are away from the office (see Vignette 1.2 for an example of a problem that was solved in a university setting).
Any time a developer is required to build a solution to a problem, they first have to research it and determine what they need to do. In other words, they have to solve the problem conceptually before they can physically implement its solution. That is precisely what this book teaches IS professionals to do: solve problems conceptually before implementing them physically.
One type of organization all students are familiar with is a university, and one task they are all familiar with is registering for classes. Today, most students enjoy the relative ease of course registration. It involves an electronic list of courses available, and often it reveals the number of seats still available in a given section of a course. Adding and dropping a class can be as easy as clicking a button.
However, it wasn’t always so simple. Students used to fill out cards for classes that they wanted to register for and then have to wait in lines for each class to submit their cards. Imagine the number of headaches this caused: needing to be in a physical location to submit your card, finding out the class you wanted was full when you got to the front of the line, selecting another class, and starting the process all over again. Moving class registration online wasn’t just a technical task (e.g., programming a website); it was also a way to remove a lot of pain points for a lot of people—it was solving a problem. Just as IS professionals have solved a registration problem for universities, IS professionals today help organizations address an ever-evolving list of problems.
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Home » Management Information Systems » Systems Approach to Problem Solving
Systems Approach to Problem Solving
The systems approach to problem solving used a systems orientation to define problems and opportunities and develop solutions. Studying a problem and formulating a solution involve the following interrelated activities:
- Recognize and define a problem or opportunity using systems thinking.
- Develop and evaluate alternative system solutions.
- Select the system solution that best meets your requirements.
- Design the selected system solution.
- Implement and evaluate the success of the designed system.
1. Defining Problems and Opportunities
Problems and opportunities are identified in the first step of the systems approach. A problem can be defined as a basic condition that is causing undesirable results. An opportunity is a basic condition that presents the potential for desirable results. Symptoms must be separated from problems. Symptoms are merely signals of an underlying cause or problem.
Symptom: Sales of a company’s products are declining. Problem: Sales persons are losing orders because they cannot get current information on product prices and availability. Opportunity: We could increase sales significantly if sales persons could receive instant responses to requests for price quotations and product availability.
2. Systems Thinking
Systems thinking is to try to find systems, subsystems, and components of systems in any situation your are studying. This viewpoint ensures that important factors and their interrelationships are considered. This is also known as using a systems context, or having a systemic view of a situation. I example, the business organization or business process in which a problem or opportunity arises could be viewed as a system of input, processing, output, feedback, and control components. Then to understand a problem and save it, you would determine if these basic system functions are being properly performed.
The sales function of a business can be viewed as a system. You could then ask: Is poor sales performance (output) caused by inadequate selling effort (input), out-of-date sales procedures (processing), incorrect sales information (feedback), or inadequate sales management (control)? Figure illustrates this concept.
3. Developing Alternate Solutions
There are usually several different ways to solve any problem or pursue any opportunity. Jumping immediately from problem definition to a single solution is not a good idea. It limits your options and robs you of the chance to consider the advantages and disadvantages of several alternatives. You also lose the chance to combine the best points of several alternative solutions.
Where do alternative solutions come from/ experience is good source. The solutions that have worked, or at least been considered in the past, should be considered again. Another good source of solutions is the advice of others, including the recommendations of consultants and the suggestions of expert systems. You should also use your intuition and ingenuity to come up with a number of creative solutions. These could include what you think is an ideal solution. The, more realistic alternatives that recognize the limited financial, personnel, and other resources of most organizations could be developed. Also, decision support software packages can be used to develop and manipulate financial, marketing, and other business operations. This simulation process can help you generate a variety of alternative solutions. Finally, don’t forget that “doing nothing” about a problem or opportunity is a legitimate solution, with its own advantages and disadvantages.
4. Evaluating Alternate Solutions
Once alternative solutions have been developed, they must be evaluated so that the best solution can be identified. The goal of evaluation is to determine how well each alternative solution meets your business and personal requirements. These requirements are key characteristics and capabilities that you feed are necessary for your personal or business success.
If you were the sales manager of a company, you might develop very specific requirements for solving the sales-related information problems of your salespeople. You would probably insist that any computer-based solution for your sales force be very reliable and easy to use. You might also require that any proposed solution have low start-up costs, or have minimal operating costs compared to present sales processing methods.
Then you would develop evaluation criteria and determine how well each alternative solution meets these criteria. The criteria you develop will reflect how you previously defined business and personal requirements. For example, you will probably develop criteria for such factors as start-up costs, operating costs, ease of use, and reliability. Criteria may be ranked or weighted, based on their importance in meeting your requirements.
5. Selecting the Best Solution
Once all alternative solutions have been evaluated, you can being the process of selecting the best solution. Alternative solutions can be compared to each other because they have been evaluated using the same criteria.
Alternatives with a low accuracy evaluation (an accuracy score less than 10), or a low overall evaluation (an overall score less than 70) should be rejected. Therefore, alternative B for sales data entry is rejected, and alternative A, the use of laptop computers by sales reps, is selected.
6. Desingning and Implementing Solution
Once a solution has been selected, it must be designed and implemented. You may have to depend on other business end users technical staff to help you develop design specifications and an implementation plan. Typically, design specifications might describe the detailed characteristics and capabilities of the people, hardware, software, and data resources and information system activities needed by a new system. An implementation plan specifies the resources, activities, and timing needed for proper implementation. For example, the following items might be included in the design specifications and implementation plan for a computer-based sales support system:
- Types and sources of computer hardware, and software to be acquired for the sales reps.
- Operating procedures for the new sales support system.
- Training of sales reps and other personnel.
- Conversion procedures and timetable for final implementation.
7. Post Implementation Review
The final step of the systems approach recognizes that an implemented solution can fail to solve the problem for which it was developed. The real world has a way of confounding even the most well-designed solutions. Therefore, the results of implementing a solution should be monitored and evaluated. This is called a postimple-implemented. The focus of this step is to determine if the implemented solution has indeed helped the firm and selected subsystems meet their system objectives. If not, the systems approach assumes you will cycle back to a previous step and make another attempt to find a workable solution.
- Role of a Systems Analyst in Organizations
- The factors which lead to the success and failure of MIS in an organization
- Data storage in Management Information Systems (MIS)
- IT Tools and Technologies for Managers
- Integration of Blockchain and Enterprise Resource Planning Systems
- Types of Human Resource Information Systems (HRIS)
- The necessity and importance of Systems Design in Management Information System (MIS)
- Types of Systems
- Cost Accounting Information System
- The Concept of Systems
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Caribbean Secondary Education Certificate - Information Technology/Steps in Problem-Solving
- 1.1 Problem Definition
- 1.2 Problem Analysis
- 1.3 Generating possible Solutions
- 1.4 Analyzing the Solutions
- 1.5 Selecting the best Solution(s)
- 1.6.1 Reference
Steps in Problem Solving
The Problem Solving process consists of a sequence of sections that fit together depending on the type of problem to be solved. These are:
- Problem Definition.
- Problem Analysis.
- Generating possible Solutions.
- Analyzing the Solutions.
- Selecting the best Solution(s).
Planning the next course of action (Next Steps)
The process is only a guide for problem solving. It is useful to have a structure to follow to make sure that nothing is overlooked. Nothing here is likely to be brand new to anyone, but it is the pure acknowledgement and reminding of the process that can help the problems to be solved.
The normal process for solving a problem will initially involve defining the problem you want to solve. You need to decide what you want achieve and write it down. Often people keep the problem in their head as a vague idea and can so often get lost in what they are trying to solve that no solution seems to fit. Merely writing down the problem forces you to think about what you are actually trying to solve and how much you want to achieve. The first part of the process not only involves writing down the problem to solve, but also checking that you are answering the right problem. It is a check-step to ensure that you do not answer a side issue or only solve the part of the problem that is most easy to solve. People often use the most immediate solution to the first problem definition that they find without spending time checking the problem is the right one to answer.
The next step in the process is often to check where we are, what is the current situation and what is involved in making it a problem. For example, what are the benefits of the current product/service/process? And why did we decide to make it like that? Understanding where the problem is coming from, how it fits in with current developments and what the current environment is, is crucial when working out whether a solution will actually work or not. Similarly you must have a set of criteria by which to evaluate any new solutions or you will not know whether the idea is workable or not. This section of the problem solving process ensures that time is spent in stepping back and assessing the current situation and what actually needs to be changed.
After this investigation, it is often good to go back one step to reconfirm that your problem definition is still valid. Frequently after the investigation people discover that the problem they really want to answer is very different from their original interpretation of it.
Generating possible Solutions
When you have discovered the real problem that you want to solve and have investigated the climate into which the solution must fit, the next stage is to generate a number of possible solutions. At this stage you should concentrate on generating many solutions and should not evaluate them at all. Very often an idea, which would have been discarded immediately, when evaluated properly, can be developed into a superb solution. At this stage, you should not pre-judge any potential solutions but should treat each idea as a new idea in its own right and worthy of consideration.
Analyzing the Solutions
This section of the problem solving process is where you investigate the various factors about each of the potential solutions. You note down the good and bad points and other things which are relevant to each solution. Even at this stage you are not evaluating the solution because if you do so then you could decide not to write down the valid good points about it because overall you think it will not work. However you might discover that by writing down its advantages that it has a totally unique advantage. Only by discovering this might you choose to put the effort in to develop the idea so that it will work.
Selecting the best Solution(s)
This is the section where you look through the various influencing factors for each possible solution and decide which solutions to keep and which to disregard. You look at the solution as a whole and use your judgement as to whether to use the solution or not. In Innovation Toolbox, you can vote using either a Yes/No/Interesting process or on a sliding scale depending on how good the idea is. Sometimes pure facts and figures dictate which ideas will work and which will not. In other situations, it will be purely feelings and intuition that decides. Remember that intuition is really a lifetimes experience and judgement compressed into a single decision.
By voting for the solutions you will end up with a shortlist of potential solutions. You may want to increase the depth in the analysis of each idea and vote again on that shortlist to further refine your shortlist.
You will then end up with one, many or no viable solutions. In the case where you have no solutions that work, you will need to repeat the generation of solutions section to discover more potential solutions. Alternatively you might consider re-evaluating the problem again as sometimes you may not find a solution because the problem definition is not well defined or self-contradictory.
This section of the process is where you write down what you are going to do next. Now that you have a potential solution or solutions you need to decide how you will make the solution happen. This will involve people doing various things at various times in the future and then confirming that they have been carried out as planned. This stage ensures that the valuable thinking that has gone into solving the problem becomes reality. This series of Next Steps is the logical step to physically solving the problem.
The Problem Solving Process
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Home » Online Programs » Technology » Master of Science in Management Information Systems » How Can Management Information Systems Solve Problems in the Real World?
How Can Management Information Systems Solve Problems in the Real World?
- Published On: March 22, 2022
Management information systems (MIS) can refer to individual information systems, an organizational department and a field of study. MIS addresses organizational information management needs, supports effective decision-making and solves complex problems in all instances and applications. Organizations rely on MIS and related information technology (IT) systems more than ever in today’s data-driven business environment.
What Is MIS?
As noted above, MIS can have different meanings according to the application. Smartsheet explains that “management information systems (or information management systems) are tools used to support processes, operations, intelligence, and IT. MIS tools move data and manage information.” Numerous information systems have evolved from the early stages of MIS and its applications.
MIS can also refer to a functional area of an organization. In this context, TechTarget defines MIS as “the department controlling hardware and software systems used for business-critical decision-making within an enterprise.” But, generally, many functions of MIS as an organizational department have been absorbed into the broader scope of information systems and IT management and operations.
In all applications of the concept, MIS is concerned with storing, organizing and analyzing information to automate and improve organizational processes. As a result, MIS connects business users with the knowledge to make effective decisions and optimize operations.
What Is Involved in MIS as a Field of Study?
Contemporary study of MIS is wide-ranging, encompassing the many information systems, tools and organizational functions that intersect with information management. MIS study involves every aspect of the lifecycle and use of information, from data collection and storage to analysis and translation into usable insight for decision-makers.
The online Master of Science (MS) in MIS degree program from Lamar University reflects this comprehensive approach to the field of study. Coursework focuses on dedicated information systems used for processes like:
- Customer relationship management (CRM)
- Supply chain management (SCM)
- Sales force management
- Database management
- Project management
Lamar’s MS in MIS program emphasizes enterprise resource planning (ERP) software systems. Such systems aim to integrate the information management systems of all core business processes.
This focus can streamline and ensure consistency of data management across an organization. ERP systems can also improve controlled, secure access to data and seamless collaboration between departments, especially given vast advancements in cloud-based enterprise solutions.
But, again, modern MIS applications focus on turning data into actionable insight. Hence, students in Lamar’s MIS online program study how to use business analytics and business intelligence (BI) tools to inform data-driven decision-making . This highlights how the study of MIS centers around developing information systems management expertise and learning how to apply it to solve real-world business problems.
What Kinds of Business Problems Can MIS Help Solve?
With the expertise gained from this study, MIS graduates are prepared to tackle all types of business problems. With knowledge of both enterprise systems and function-specific information systems, MIS graduates can apply their data-driven problem-solving skills across industries, sectors and business processes.
For instance, businesses are currently struggling with supply chain disruptions. Through integrating SCM, project management and other systems into a comprehensive ERP system, MIS experts can facilitate the cross-departmental collaboration needed to overcome supply chain challenges. MIS professionals further help decision-makers use analytics and BI tools to drill down into data in order to identify and remediate supply chain inefficiencies and problems.
In the marketing arena, analytics tools help professionals conduct market research, analyze trends, identify audiences and design data-informed, targeted campaigns. BI and analytics tools draw from MIS to provide ongoing, timely data on marketing strategy performance, helping marketers improve strategy to optimize marketing campaigns.
Moreover, eCommerce has blurred the line between marketing, sales and customer relationship management. Providing excellent customer service throughout these processes relies on accurate, shared data, seamless customer handoffs in overlapping virtual spaces and integrated information systems. With expertise in CRM, sales force management and ERP systems , MIS professionals play a central role in implementing and managing integrated, customer-centric eCommerce systems.
Scaling a business can also present numerous challenges. One aspect of this is selecting the right information management solutions to meet a growing organization’s present and projected needs. MIS professionals guide this selection, integrating in-house systems, cloud-based services and enterprise-level systems according to forecasted growth and organizational change.
Plus, software solutions grounded in MIS provide the real-time insight needed for organizational agility. Business decision-makers rely on these tools for up-to-date information and reports on internal and external business conditions. Predictive and prescriptive analytics help business users understand what is likely to happen and what they should do about it, which informs how leaders anticipate, respond and proactively adapt to changing conditions.
MIS and the many intersecting systems and analytics tools involved with information management form the foundation of data-driven business models. Through the comprehensive study of these systems and their applications, MIS professionals can help find solutions to modern organization’s complex business problems.
Learn more about Lamar University’s online Master of Science in Management Information Systems program .
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- About Problem Solving
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Case studies, problem solving related topics.
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- Problem Solving
What is Problem Solving?.
Quality Glossary Definition: Problem solving
Problem solving is the act of defining a problem; determining the cause of the problem; identifying, prioritizing, and selecting alternatives for a solution; and implementing a solution.
- The problem-solving process
- Problem solving resources
Problem Solving Chart
The Problem-Solving Process
In order to effectively manage and run a successful organization, leadership must guide their employees and develop problem-solving techniques. Finding a suitable solution for issues can be accomplished by following the basic four-step problem-solving process and methodology outlined below.
1. Define the problem
Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes .
The sections below help explain key problem-solving steps. These steps support the involvement of interested parties, the use of factual information, comparison of expectations to reality, and a focus on root causes of a problem. You should begin by:
- Reviewing and documenting how processes currently work (i.e., who does what, with what information, using what tools, communicating with what organizations and individuals, in what time frame, using what format).
- Evaluating the possible impact of new tools and revised policies in the development of your "what should be" model.
2. Generate alternative solutions
Postpone the selection of one solution until several problem-solving alternatives have been proposed. Considering multiple alternatives can significantly enhance the value of your ideal solution. Once you have decided on the "what should be" model, this target standard becomes the basis for developing a road map for investigating alternatives. Brainstorming and team problem-solving techniques are both useful tools in this stage of problem solving.
Many alternative solutions to the problem should be generated before final evaluation. A common mistake in problem solving is that alternatives are evaluated as they are proposed, so the first acceptable solution is chosen, even if it’s not the best fit. If we focus on trying to get the results we want, we miss the potential for learning something new that will allow for real improvement in the problem-solving process.
3. Evaluate and select an alternative
Skilled problem solvers use a series of considerations when selecting the best alternative. They consider the extent to which:
- A particular alternative will solve the problem without causing other unanticipated problems.
- All the individuals involved will accept the alternative.
- Implementation of the alternative is likely.
- The alternative fits within the organizational constraints.
4. Implement and follow up on the solution
Leaders may be called upon to direct others to implement the solution, "sell" the solution, or facilitate the implementation with the help of others. Involving others in the implementation is an effective way to gain buy-in and support and minimize resistance to subsequent changes.
Regardless of how the solution is rolled out, feedback channels should be built into the implementation. This allows for continuous monitoring and testing of actual events against expectations. Problem solving, and the techniques used to gain clarity, are most effective if the solution remains in place and is updated to respond to future changes.
You can also search articles , case studies , and publications for problem solving resources.
Innovative Business Management Using TRIZ
Introduction To 8D Problem Solving: Including Practical Applications and Examples
The Quality Toolbox
Root Cause Analysis: The Core of Problem Solving and Corrective Action
One Good Idea: Some Sage Advice ( Quality Progress ) The person with the problem just wants it to go away quickly, and the problem-solvers also want to resolve it in as little time as possible because they have other responsibilities. Whatever the urgency, effective problem-solvers have the self-discipline to develop a complete description of the problem.
Diagnostic Quality Problem Solving: A Conceptual Framework And Six Strategies ( Quality Management Journal ) This paper contributes a conceptual framework for the generic process of diagnosis in quality problem solving by identifying its activities and how they are related.
Weathering The Storm ( Quality Progress ) Even in the most contentious circumstances, this approach describes how to sustain customer-supplier relationships during high-stakes problem solving situations to actually enhance customer-supplier relationships.
The Right Questions ( Quality Progress ) All problem solving begins with a problem description. Make the most of problem solving by asking effective questions.
Solving the Problem ( Quality Progress ) Brush up on your problem-solving skills and address the primary issues with these seven methods.
Refreshing Louisville Metro’s Problem-Solving System ( Journal for Quality and Participation ) Organization-wide transformation can be tricky, especially when it comes to sustaining any progress made over time. In Louisville Metro, a government organization based in Kentucky, many strategies were used to enact and sustain meaningful transformation.
Quality Improvement Associate Certification--CQIA
Certified Quality Improvement Associate Question Bank
Lean Problem-Solving Tools
Problem Solving Using A3
NEW Root Cause Analysis E-Learning
Making the Connection In this exclusive QP webcast, Jack ReVelle, ASQ Fellow and author, shares how quality tools can be combined to create a powerful problem-solving force.
Adapted from The Executive Guide to Improvement and Change , ASQ Quality Press.
Jul 16, 2021
Information Technology Problem Solving — The 6 Principles of Scientific Problem Solving
This paper will explain a scientific approach to problem solving. Although it is written to address Information Technology related problems, the concepts might also be applicable in other disciplines. The methods, concepts, and techniques described here is nothing new, but it is shocking how many “problem solvers” fail to use them. In between I will include some real-life examples. Why do problem solvers guess in stead of following a scientific approach to problem solving? Maybe because it feels quicker? Maybe a lack of experience in efficient problem solving? Or maybe because it feels like hard work to do it scientifically? Maybe while you keep on guessing and not really solving, you generate more income and add some job security? Or maybe because you violate the first principle of problem solving: understand the problem. Principle #1. Understand the *real* problem. Isn’t it obvious that before you can solve, you need to understand the problem? Maybe. But, most of the time the solver will start solving without knowing the real problem. What the client or user describe as “The Problem” is normally only the symptom! “My computer does not want to switch on” is the symptom. The real problem could be that the whole building is without power. “Every time I try to add a new product, I get an error message” is the symptom. Here the real problem could be “Only the last 2 products I tried to add gave a ‘Product already exists’ error”. Another classic example: “Nothing is working”… You start your investigation by defining the “real problem”. This will entail asking questions (and sometimes verify them), and doing some basic testing. Ask the user questions like “when was the last time it worked successfully?”, “How long have you been using the system?”, “Does it work on another PC or another user?”, “What is the exact error message?” etc. Ask for a screen-print of the error if possible. Your basic testing will be to ensure the end-to-end equipment is up and running. Check the user’s PC, the network, the Web Server, Firewalls, the File Server, the Database back-end, etc. Best-case you will pint-point the problem already. Worst-case you can eliminate a lot of areas for the cause of the problem. A real life example. The symptom according to the user: “The system hangs up at random times when I place orders”. The environment: The user enters the order detail on a form in a mainframe application. When all the detail is completed, the user will tab off the form. The mainframe then sends this detail via communication software to an Oracle Client/Server system at the plant. The Oracle system will do capacity planning and either returns an error or an expected order date back to the mainframe system. This problem is quite serious, because you can loose clients if they try to place orders and the system does not accept them! To attempt to solve this problem, people started by investigating: 1) The load and capacity of the mainframe hardware 2) Monitoring the network load between the mainframe and the Oracle system 3) Hiring consultants to debug the communication software 4) Debugging the Oracle capacity planning system After spending a couple of months they could not solve the problem. The “Scientific Problem Solver” was called in. It took less than a day and the problem was solved! How? The solver spends the day at the user to see what the “real problem” was. It was found that the problem only occurs with export orders. By investigating the capture screen and user actions, it was found that with export orders the last field on the form is always left blank and the user did not tab off this field. The system was not hanging, it waited for the user to press “tab” another time. Problem solved. It can be noted that the “Scientific Problem Solver” had very limited knowledge of the mainframe, of the order capturing system, of the communication software, and of the Oracle capacity planning system. And this brings us at Principle#2. Principle #2. Do not be afraid to start the solving process, even if you do not understand the system. How many times have you heard “I cannot touch that code, because it was developed by someone else!”, or “I cannot help because I am a HR Consultant and that is a Finance problem”? If you washing machine does not want to switch on, you do not need to be an Electrical Engineer, Washing Machine Repair Specialist, Technician, or whatever specialist to do some basic fault finding. Make sure the plug is working. Check the trip-switch, etc. “I have never seen this error before” should not stop you from attempting to solve. With the error message and an Internet Search engine, you can get lots of starting points. In every complex system there are a couple of basic working principles. System A that reads data from System B can be horribly complex (maybe a Laboratory Spectrometer that reads data from a Programmable Logic Computer via an RS-232 port). But, some basics to test for: Does both systems have power? Is there an error message in the event log on one of these systems? Can you “ping” or trace a network packet from the one system to the other? Try a different communication cable. Search the internet for the error message. Once you have established what the problem is, you need to start solving it. Sometimes the initial investigation will point you directly to the solution (switch the power on; replace the faulty cable, etc). But, sometimes the real problem is complex in itself, so the next principle is to solve it simple. Principle #3. Conquer it simple. Let’s start this section with a real-life example. Under certain conditions, a stored procedure will hang. The stored procedure normally takes about an hour to run (when it is not hanging). So, the developer tried to debug. Make some changes and then wait another hour or so to see if the problem is solved. After some days the developer gave up and the “Problem Solver” took over. The “Problem Solver” had to his disposal the knowledge under witch conditions the stored procedure would hang. So, it was a simple exercise to make a copy of the procedure, and then with this copy to strip all unnecessary code. All parameters were changed with hard-coded values. Bits of code were executed at a time and the result-sets were then again hard-coded into the copy of the procedure. Within 3 hours the problem was solved. An infinite-loop was discovered. What the “Problem Solver” did, was to replicate the problem and at the same time tried to isolate the code that caused the problem. In doing so, the complex (and time consuming) stored procedure became something fast and simple. If the problem is inside an application, create a new application and try to simulate the problem inside the new application as simple as possible. If the problem occurs when a certain method for a certain control gets called, then try to only include this control in the empty application and call that method with hard-coded values. If the problem is with embedded SQL inside a C# application, then try to simulate the SQL inside of a Database Query tool (like SQL*Plus for Oracle, Query Analyzer for SQL Server, or use the code in MS Excel via ODBC to the database). The moment you can replicate the problem in a simple way, you are more than 80% on your way to solve it. If you do not know where in the program the problem is, then use DEBUG. Principle #4. Debug. Most application development tools come standard with a debugger. Weather it is Macromedia Flash, Microsoft Dot Net, Delphi, or what ever development environment there will be some sort of debugger. If the tool does not come standard with a debugger, then you can simulate one. The first thing you want to do with the debugger is to determine where the problem is. You do this by adding breakpoints at key areas. Then you run the program in debug mode and you will know between which breakpoints the problem occurred. Drill down and you will find the spot. Now that you know where the problem is, you can “conquer it simple” Another nice feature of most debuggers includes the facility to watch variables, values, parameters, etc. as you step through the program. With these values known at certain steps, you can hard-code them into your “simplified version” of the program If a development tool does not support debugging, then you can simulate it. Put in steps in the program that outputs variable values and “hello I am here” messages either to the screen, to a log file, or to a database table. Remember to take them out when the problem is resolved… you don’t want your file system to be cluttered or filled up with log files! Principle #5. There is a wealth of information on the database back-end that will help to solve a problem. The “Problem Solver” was called to help solve a very tricky problem. A project was migrating system from a mainframe to client-server technology. All went well during testing, but when the systems went live, all of a sudden there were quite a few, and quite random “General Protection Faults”. (The GPF-error was the general error trap in Windows 95 and 98). It was tried to simplify the code, debugging was attempted, but it was impossible to replicate. In the LAB environment, the problem would not occur! Debugging trace messages to log files indicated that the problem occurred very randomly. Some users experienced it more than others, but eventually all users will get them! Interesting problem. The “Problem Solver” solved this after he started to analyze the database back-end. Not sure if it was by chance or because he systematically moved in the right direction because of a scientific approach. Through tracing what is happening on the back-end level, it was found that all these applications were creating more-and-more connections to the database. Every time a user starts a new transaction another connection was established to the database. The sum-total of the connections were only released when the application was closed. As the user navigated to new windows inside the same application, more and more connections are opened, and after a specific number of connections, the application will have enough and then crash. This was a programming fault in a template that was used by all the developers. The solution was to first test if a cursor to the database is already open, before opening it again. How do you trace on the back-end database what is happening? The main database providers have GUI tools that help you to trace or analyze what queries are fired against the database. It will also show you when people connect, disconnect, or were unable to connect because of security violations. Most databases also include some system dictionary tables that can be queried to get this information. These traces can sometimes tell ‘n whole story of why something is failing. The query code you retrieve from the trace can be help to “simplify the search”. You can see from the trace if the program makes successful contact with the database. You can see how long it takes for a query to execute. To add to Principle#2 (do not be afraid to start…); you can analyze this trace information, even though you might not know anything about the detail of the application. Remember though that these back-end traces can put a strain on the back-end resources. Do not leave them running for unnecessary long. Principle #6. Use fresh eyes. This is the last principle. Do not spend too much time on the problem before you ask for assistance. The assistance does not have to be from someone more senior than you. The principle is that you need a pair of fresh eyes for a fresh perspective and sometimes a bit of fresh air by taking a break. The other person will look and then ask a question or two. Sometimes it is something very obvious that was missed. Sometimes just by answering the question it makes you think in a new directions. Also, if you spend hours looking at the same piece of code, it is very easy to start looking over a silly mistake. A lot of finance balancing problems get solved over a beer. It could be a change of scenery, and/or the relaxed atmosphere that will pop out the solution. Maybe it is the fresh oxygen that went to the brain while walking to the pub. Maybe it is because the problem got discussed with someone else. Conclusion After reading this paper, the author hope that you will try these the next time you encounter a problem to solve. Hopefully by applying these six principles you will realize the advantages they bring, rather than to “guess” your way to a solution. Source by Andre Maakal
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How Effective Managers Use Information Systems
- Steven L. Alter
Advances in computer-based information technology in recent years have led to a wide variety of systems that managers are now using to make and implement decisions. By and large, these systems have been developed from scratch for specific purposes and differ significantly from standard electronic data processing systems. Too often, unfortunately, managers have little say […]
Advances in computer-based information technology in recent years have led to a wide variety of systems that managers are now using to make and implement decisions. By and large, these systems have been developed from scratch for specific purposes and differ significantly from standard electronic data processing systems. Too often, unfortunately, managers have little say in the development of these decision support sysems; at the same time, non-managers who do develop them have a limited view of how they can be used. In spite of these drawbacks, the author found that a number of the 56 systems he studied are successful. And the difference between success and failure is the extent to which managers can use the system to increase their effectiveness within their organizations. Thus, the author suggests that this is the criterion designers and managers should jointly ascribe to in exploiting the capabilities of today’s technologies.
What can managers realistically expect from computers other than a pile of reports a foot deep dumped on their desks every other week?
Everyone knows, for instance, that computers are great at listing receivables. But what about all the promises and all the speculations over the past few decades about the role of the computer in management? While there have been advances in basic information retrieval, processing, and display technologies, my recent study of 56 computerized decision support systems confirms the common wisdom that very few management functions have actually been automated to date and all indications are that most cannot be.
Instead, my findings show what other researchers have reported: applications are being developed and used to support the manager responsible for making and implementing decisions, rather than to replace him. In other words, people in a growing number of organizations are using what are often called decision support systems to improve their managerial effectiveness. 1
Unfortunately my research also bore out the fact that while more and more practical applications are being developed for the use of decision makers, three sizable stumbling blocks still stand in the way of others who might benefit from them.
First, managers and computer users in many organizations are familiar with only a few of the types of systems now in use. As a result, different types of innovative systems have often been conceived and nurtured by internal or external “entrepreneurs,” not by the system users or their superiors.
Second, and closely related to my first finding, these entrepreneurs tend to concentrate on technical characteristics. Too often, this myopia means that they fail to anticipate the ways in which such systems can be used to increase the effectiveness of individuals in organizations.
Finally, highly innovative systems—the very ones management should find most useful—run a high risk of never being implemented, especially when the impetus for change comes from a source other than the potential user.
Quite simply, my purpose in this article is to discuss, without getting into the technology involved, the high potential of a variety of decision support systems, the challenges and risks they pose to managers and implementers, and a wide range of strategies to meet these challenges and risks.
Types of Decision Support Systems
While there are many ways to categorize computer systems, a practical one is to compare them in terms of what the user does with them:
- Retrieves isolated data items.
- Uses as a mechanism for ad hoc analysis of data files.
- Obtains prespecified aggregations of data in the form of standard reports.
- Estimates the consequences of proposed decisions.
- Proposes decisions.
- Makes decisions.
As Exhibit I indicates, EDP reporting systems usually perform only the third function in this list of operations, which I have organized along a dimension from “data-orientation” to “model-orientation.” Hence, unlike the EDP user who receives standard reports on a periodic basis, the decision support system user typically initiates each instance of system usage, either directly or through a staff intermediary.
Exhibit I Comparison of Uses, Purposes, and Characteristics of EDP Systems vs. Decision Support Systems
Although decision-oriented reporting systems often grow out of standard EDP systems, I will concentrate on seven distinct types, briefly describing one example of each type.
Incidently, it is interesting to note that external consultants developed the systems cited in my second, fifth, and seventh examples, while those of the first, third, and sixth were the creations of people acting as internal entrepreneurs through staff roles; only the fourth system was developed on direct assignment by the user. This same pattern of initiation of innovative systems by people other than the users was present in many of the 56 systems.
1. Retrieval only—a shop floor information system.
In order to help production foremen improve the percentage yield on a newly developed 50—stage process for manufacturing micro-circuits, the management of one company has installed an on-line, shop floor information system. Operators submit daily piecework reports, which include yield, release date, identification of the person who does the work, and so on. The foremen then juggle this information to obtain productivity data by operation, operator, machine, and lot.
Thus they are able to use the system in a number of ways. They can monitor work flow, pinpoint yield problems, and settle day-to-day questions such as who worked on which lot when, and which operators are ahead of or behind schedule, or below standards. The foremen have 13 standard commands by which they can retrieve the data stored in the system and display them on a cathode ray tube terminal. The commands permit them to tailor reports to their needs.
2. Retrieval and analysis—a portfolio analysis system.
Before advising clients or making authorized trading decisions, the portfolio managers at a bank I studied use an on-line system to analyze individual portfolios. The managers can bypass time-consuming manual methods and obtain up-to-date and clearly organized portfolio information in either graphic or tabular form.
Depending on the situation, a manager can inspect both individual portfolios and groups of portfolios from different viewpoints—for example, rank them in different ways, obtain breakdowns by industry or risk level, and so on. With this kind of flexibility, the bank’s portfolio managers make more effective use of a vast amount of information, most of which had existed prior to the system, but had been accessible only through tedious manual analysis.
3. Multiple data bases plus analysis—sales information systems.
Greater flexibility was also the reason that two consumer products companies and one manufacturing company I looked at developed sales information systems which are quite similar. Standard EDP functions were too inflexible to produce ad hoc sales analysis reports in a timely and cost-effective manner for those in the companies’ marketing and planning areas. In each case, information extracted from the EDP systems is now maintained separately in order to have it handy and, in two instances, to be able to analyze it in conjunction with externally purchased proprietary data bases and models.
Basically, each system is a vehicle by which a staff man or group tries to help decision makers. Their modus operandi is incremental: identify a problem; bring the current system and existing expertise to bear on it; develop a solution in the form of an analysis or additional system module; and incorporate the results into an expanded version of the system.
4. Evaluating decisions using an accounting model—a source-and-application-of-funds budget.
To expedite operational decision making and financial planning over a two-year horizon, an insurance company is using an on-line, source-and-application-of-funds budget system. Inputs are projections of future business levels in various lines of insurance and investment areas, plus assumptions concerning important numbers such as future money-market rates. The output is a projected overall cash flow by month.
An investment committee uses the model to allocate funds across investment areas and to minimize the amount of cash left idle in banks. The committee compares projected cash flows based on different allocation decisions; the decisions that it actually adopts are those that produce adequate projected cash flows and that are acceptable to the various groups in the company.
Actually, the system is an accounting definition of the company. There is no question about the accuracy of the relationships in the model, so the only way projected results can be in error is if estimates of business activity levels or money market rates are incorrect.
5. Evaluating decisions using a simulation model—a marketing decision system.
In order to provide a more rational basis for repetitive marketing decisions, a consumer products company uses a model that relates levels of advertising, promotions, and pricing to levels of sales for a particular brand. The model was developed in a team setting by reconciling an analysis of historical brand information with an individual’s subjective feelings concerning the effects on sales of various levels and types of advertising and other marketing actions.
The model was validated by tracking its accuracy in predicting sales based on the competitive actions that were taken. Unlike the accounting model I just mentioned, this is a simulation model in which some of the most important relationships are estimates at best. For instance there simply is no rule by which it is possible to predict sales with certainty based on advertising levels. In fact, this was the heart of the issue in developing the model.
Even though it has turned out to be useful for prediction, much of the value of the model lies in the company’s improved understanding of the market environment.
6. Proposing decisions—optimization of raw materials usage.
Another consumer products company, faced with short-run supply problems for many of its raw materials, has developed an optimization model to solve the mathematical puzzle of choosing and balancing among various product recipes.
The inputs to the model include a series of different recipes for many products, short-run supply levels for raw materials, and production requirements for finished products. The output is the choice of recipes that maximizes production using existing supplies. When the short-run supply situation shifts, the model can be revised and a new set of recipes chosen.
The system has had a major impact on the way managers view allocation policy. Initially, they considered allocating scarce raw materials to products by setting priorities among products. The model showed that it was more advantageous to start with production requirements and then allocate scarce resources by optimizing the mix of product recipes.
7. Making decisions—an insurance renewal rate system.
As an outgrowth of an overhaul of its group insurance information system, an insurance company has developed a system to eliminate part of the clerical burden associated with renewal underwriting and to help assure that rate calculations are consistent and accurate.
Instead of calculating renewal rates by hand, underwriters fill out coded input sheets for the system, which calculates a renewal rate based on a series of standard statistical and actuarial assumptions. Since these assumptions might or might not apply to a particular policy, the underwriters review documentation accompanying the policies and decide whether the standard calculations are applicable. If they are not, the coding sheet is modified in an appropriate manner and resubmitted.
In effect, the system makes the decision in completely standard situations, while the underwriter decides whether the situation is standard and, if not, what adjustments are required. As a result, the underwriters can concentrate on the substance of their jobs rather than the related clerical chores.
Spectrum of possibilities
These seven systems represent a wide range of approaches in supporting decisions. The first one helps production foremen by simply providing rapid access to historical information such as who worked on what lot, and when the work was done. But the foremen must decide what should be done once they have the information. At the other extreme, the system supporting the underwriters virtually makes the decision in some cases. Between the two extremes, analysis systems and model-oriented systems help people organize information and also facilitate and formalize the evaluation of proposed decisions.
Although managers in most large companies have used budgeting or planning systems similar to the source-and-application-of-funds model I mentioned, the spectrum of possibilities for other kinds of decision support systems is surprisingly wide. Obviously, some of these systems are of no particular use in many settings. Still, their variety suggests that most companies should have a number of genuine opportunities for applying the concept of computerbased support for decision making.
Motives of Managers
What do decision support systems do that actually helps their users? What is their real impact? In my survey, answers to these questions proved elusive in many cases since the users valued the systems for reasons that were completely different from initial ideas of what the systems were to accomplish. In fact, a wide range of purposes exists for these systems. While many decision support systems share the goals of standard EDP systems, they go further and address other managerial concerns such as improving interpersonal communication, facilitating problem solving, fostering individual learning, and increasing organizational control.
Such systems can affect interpersonal communication in two ways: by providing individuals with tools for persuasion and by providing organizations with a vocabulary and a discipline which facilitates negotiations across subunit boundaries.
Tools of persuasion
Standard texts on systems analysis totally overlook the personal use of decision support systems as tools of persuasion. But consider the following “offensive” (persuading someone else to do something) uses to which various companies have put these systems:
- The manager of a chemical plant was attempting to meet output goals (quantities by product) that were being set by a marketing group. Unfortunately, the group was setting goals without much consideration of raw material shortages under which the plant was operating. The plant had been using a model to calculate production mixes.
At one point, it occurred to the plant manager that he could use this model to investigate whether marketing was setting goals that resulted in poor plant utilization and made him appear inefficient. As he ran the model under a series of different production mix goals, it became clear that this was the case, and he used the results to persuade marketing to change his plant’s production mix.
- A data retrieval and manipulation system first received wide exposure in a transportation company when a number of the company’s top executives used it to develop a good quantitative rationale for a proposed merger. With the system, it was possible to explore and manipulate a large data base of information on the industry.
Although the merger was not approved, management thought that the system helped it put up a good fight.
- The management of a shipping company found that a system it used in consolidating and fine-tuning strategic investment plans also helped it negotiate with banks. The banks and other sources of financing seemed to be uniformly impressed by the careful computer-based analysis on which management based its financing requests. The resulting edge in credibility was small, but, in management’s opinion, noticeable.
Now that we have seen illustrations of the offensive tools of persuasion, let us turn to examples of the “defensive” (persuading someone that the user has done a good job) uses of these systems:
- When asked whether he ever made direct use of a case tracking system, the head of an adjudication group in a government regulatory agency said that he remembered only one instance. This was when he spent a lunch hour generating a report to make his group’s recent performance appear as favorable as possible in spite of some unfortunate delays and problems that made the standard report look bad.
- The new president of a large conglomerate used a one-year budgeting model to leam about the budgeting choices that existed, as well as to help him discount what people in various areas claimed concerning their own budgetary needs.
- The class scheduler of a training school for a company’s service personnel had found his job frustrating because it was always difficult to justify the budget on explicit grounds.
With a model that generated optimal training schedules, the scheduler could protect himself very easily by saying: “Using these assumptions concerning attrition, acceptable peak-time shortfalls, and other considerations, this is the best budget. If you (the budget cutter) would like me to change these assumptions, I would be glad to generate a new budget. What level of shortfall do you suggest?” Thus the system not only helped the scheduler make decisions, but also helped him defend them.
- Many people suspected that a new product venture in a consumer company might not be worthwhile, but no one knew exactly why. When a risk analysis was carried out with a model, the reason became clear: the venture had a very substantial downside risk. In addition to sealing the decision, the analysis provided an understandable response to the people who had proposed the venture.
A cynic might contend that the people in these situations were taking advantage of or abusing the systems. A more practical conclusion is that these systems simply serve to improve managers’ effectiveness in their organizations by helping them communicate with other people. My point is that much of the benefit of many of the decision support systems in my sample was of this sort.
Aids to communication
Decision support systems also help managers negotiate across organizational units by standardizing the mechanics of the process and by providing a common conceptual basis for decision making.
During my survey, managers frequently commented that consistent definitions and formats are important aids to communication, especially between people in different organizational units such as divisions or departments. In a number of instances, the development of these definitions and formats was a lengthy and sometimes arduous task that was accomplished gradually over the course of several years, but which was also considered one of the main contributions of the systems.
For example, one of the purposes of some of the model-oriented systems in my sample was to estimate beforehand the overall result of decisions various people were considering separately, by filtering these decisions through a single model. In these cases, the system became an implicit arbiter between differing goals of various departments. Instead of arguing from their own divergent viewpoints, marketing, production, and financial people could use the model to demonstrate the effect of one group’s proposals on another group’s actions and on the total outcome. As a result, issues were clarified and the negotiation process expedited.
The production foremen I mentioned earlier noted the same kind of facilitation. It helped them in work-scheduling discussions and problem investigations by providing immediate access to “objective” information about “who did what, when, and how well on any production lot in the shop.”
Value to user
Although the implementers of a number of the successful systems I studied found it necessary to go through the motions of presenting a cost/benefit rationale which attributed a dollar value to personal effectiveness, they didn’t believe these numbers any more than anyone else did. Management usually decided to proceed on the basis that the proposed system seemed to make sense and would likely have a beneficial impact on the way people interacted and/ or made decisions.
Monetary savings are obviously a very important and worthwhile rationale for developing computer systems, but it should be clear at this point that the EDP-style assumption that systems should always be justified in these terms does not suffice in the area of decision support systems.
Equally obvious, there is a definite danger in developing a system simply because someone thinks it makes sense, especially if that someone is not the direct user of the system. In fact, the systems I cited as my first, second, and fifth examples began this way and encountered resistance until they were repositioned as something that users would want in order to become more effective.
Again, the general problem here is a common tendency for technical people to concentrate on the “technical beauty” of a system or idea and to assume that nontechnical people will somehow see the light and will be able to figure out how to use the system in solving business problems. This sort of overoptimism was present in the history of almost every unsuccessful system in the sample.
The message is clear: try to take advantage of the creativity of technical experts, but be sure that it is channeled toward real problems. The challenge, of course, is how to accomplish both of these goals. There are a number of ways, which I shall now discuss.
Patterns of Development
Despite the common wisdom that the needs of users must be considered in developing systems and that users should participate actively in implementing them, the users did not initiate 31 of the 56 systems I studied and did not participate actively in the development of 38 of the 56.
The results, illustrated in Exhibit II, are not surprising. Intended users neither initiated nor played an active role in implementing 11 of the 15 systems that suffered significant implementation problems. Conversely, there were relatively few such problems in 27 of the 31 systems in which the users had a hand in initiating and/or played an active role in implementing.
Exhibit II Systems Resisted by Users
But it would be wrong to infer from these findings that systems should be avoided totally, if intended users neither initiate them nor play an active role in their implementation. For one thing, 14 of the 25 systems I studied in which this was the pattern were ultimately successful. More important, many of the genuinely innovative systems in my sample, including 5 of the 7 that I described earlier, exhibited this pattern.
On the other hand, many of the systems initiated by users do little more than mechanize existing practices. While such mechanization can be very beneficial, and while I’m certainly not suggesting that major innovations must come from outside sources, the real challenge is to be able to use insights regardless of their source.
One way to do this is to devise an implementation strategy to encourage user involvement and participation throughout the development of the systems regardless of who originated the concept. Examples of successful strategies follow.
Impose gracefully: Marketing and production managers in a decentralized company did not relish the extra work (format changes and data submission requirements) needed for a yearly budgeting system, which top management was installing. Initially, they were especially unenthusiastic because they thought the system would not really help them.
So at every stage the designers made a point of developing subsystems to provide these middle managers with sales and materials usage information that had never been available. This quid pro quo worked well; instead of seeing the system as a total imposition, the manager saw it as an opportunity for them to take part in something which would be beneficial to them.
Run a dog and pony show: Central planning personnel in two companies designed systems for budgeting and financial analysis. In one company, the system never caught on despite lengthy training demonstrations for divisional staff and other potential users. These individuals seemed enthusiastic about the system’s possibilities, but never really used it unless corporate planning people did all the work for them.
In contrast, the training program for the system in the other company fostered immediate and active involvement. In order to attend the workshops, people were required to bring their own financial analysis problems. They learned to use the system by working on these problems. When the workshops ended, many users were enthusiastic: not only did they know how to use the system, but they had also proved to themselves that it could help them.
Use a prototype: Two ever-present dangers in developing a system are creating a large, expensive one that solves the wrong problem or creating one that some people in the organization cannot live with.
Either can happen, not only when the system is designed without consulting the user and affected parties, but also when there is no one having enough experience with the particular kind of system under consideration to clearly visualize its strengths and weaknesses before it is built.
Implementers of a number of systems in my sample avoided these traps by building small prototypes, which gave the users something specific to react to. As a result, the large-scale version could be developed with a realistic notion of both what was needed and what would fly in the organization. A similar approach, also successful, was simply to build systems in small pieces that could be used, changed, or discarded easily.
Hook the user with the responsibility: Each new module or application developed as an outgrowth of one of the three sales information systems I mentioned earlier goes through three stages. The first stage consists of general, uncommitted discussions of any current problem areas with which user groups are concerned. Following research by the management science staff, the second stage is a brief formal problem statement written in conjunction with the user group. In addition to describing the problem, this statement goes over the methodology and resources that will be required to respond to it. The third stage is a formal request for authorization of out-of-pocket expenses.
Sell the system: In one of the companies I studied, a marketing analysis group used a direct selling procedure to convince people of the merits of a sales forecasting system. The pitch was very simple: they compared manual monthly forecasts for one year with the system’s forecasts. The system’s forecasts proved to be more accurate in ten months out of twelve, with less error overall than the manual ones. The system was adopted.
In another company, management had a real-time system installed for monitoring the largely automatic production of an inexpensive consumer item in order to minimize material loss due to creeping maladjustments in machine settings. During the initial installation, the implementation team discovered suspected, but previously unsubstantiated, cheating by piecework employees; more pieces were leaving many machines than were entering them. Discreet hints were dropped that the monitor had to be checked because it was registering “impossible” results. The employees were sold on the new system: they knew very well that it worked.
Despite extensive experience with EDP, many organizations have used no more than one or two of the seven types of decision support systems I have illustrated here.
One reason for this is that justifying such systems can be difficult: quantifying the impact of replacing ten clerks with one computer is one thing, while quantifying the impact of improved individual effectiveness of line personnel is quite a different thing. Another reason is that implementation can be tricky: many of the ideas come from people other than the users.
Nevertheless, developing a decision support system makes sense when it becomes clear that a fundamental change may be needed in the way decisions are reached and implemented. Often, the process of defining the system is every bit as valuable as the system produced.
My final point is that the concept of decision support systems itself can help managers in understanding the role of computers in their organizations. As the name implies, data processing systems systematize and expedite the mechanics of carrying on business activities by processing masses of data automatically. On the other hand, the decision-oriented extensions of these systems help people make and communicate decisions concerning administrative and/or competitive tactics and strategy.
The decision support systems I have discussed go one step further. Instead of starting as extensions of existing data processing systems, many decision support systems are built from scratch for the sole purpose of improving or expediting a decision making process. The underlying philosophy is that the use of computers to help people make and communicate decisions is every bit as legitimate and worthwhile as the use of computers to process masses of data.
There is evidence that this viewpoint has caught on to a certain degree and is becoming more widely accepted. The implication is not that all organizations should get on the bandwagon, but rather that managers should be aware of the opportunities and challenges in this area and should attempt to assess whether their organizations should move in this direction.
1. Steven Alter, “A Study of Computer Aided Decision Making in Organizations,” Ph.D. thesis, Sloan School of Management, MIT, 1978.
- SA Mr. Alter is assistant professor of quantitative business analysis at the Business School of the University of Southern California. His research and consulting concern long-range planning methods and the development and use of computer systems in organizations.
1.2Problem Solving: The Most Important Skill in Information Systems · Improve Organizational Processes · Exploit Opportunities Created by Technology Innovations.
A Systems Approach to Problem Solving, describes and gives examples of the steps involved in using a systems approach to solve business problems. A. The
Recognize and define a problem or opportunity using systems thinking. · Develop and evaluate alternative system solutions. · Select the system solution that best
Problem Definition · Problem Analysis · Generating possible Solutions · Analyzing the Solutions · Selecting the best Solution(s) · Planning the next
setting, in the design of information systems. Keywords: Problem solving; Information evaluation; Decision making; Information support systems.
MIS addresses organizational information management needs, supports effective decision-making and solves complex problems in all instances and
The Problem-Solving Process · Differentiate fact from opinion · Specify underlying causes · Consult each faction involved for information · State the problem
Zoom meeting/ Google Meet - You are using it for you daily live classes and meetings. · YouTube - Where you go and learn things. · Telegram/Facebook/Whatsapp/
Problem solved. It can be noted that the “Scientific Problem Solver” had very limited knowledge of the mainframe, of the order capturing system, of the
Following research by the management science staff, the second stage is a brief formal problem statement written in conjunction with the user group. In addition