Factors affecting Inventory Operations
Inventory management operations are increasingly being outsourced to third party service providers, thereby ensuring that the investments and costs in managing the inventories are reduced. This is a welcome trend provided the companies focus on overseeing and reviewing both inventory management as well as inventory operations periodically to ensure proper controls are maintained and processes […]
Logistics Service Providers keeps Supply Chain Moving
Procurement Logistics, Manufacturing Logistics and Finished Goods Logistics functions are managed by different independent departments in a company. Though the duties of the departments involve common activities like transportation etc., however, the processes and nature of logistics functions are specific to each function besides the requirements and sensitivities of delivery times, schedules, etc. With the […]
BPM – The Problem with IT as a Solution
Although IT has been touted by the experts as being the bedrock of next generation of business in which large organizations encompassing several thousand employees will seamlessly operate in a global environment, there are a good number of issues to be resolved. However, as we have already seen the merits of using IT solutions as […]
Components of a Project Plan
Introduction This article details the high level project plan and the components of the same. A project is a success if it meets the objectives of time, cost, technical and business. Project objectives are defined in the preliminary project scope statement. As mentioned above, a project is deemed complete if the project objectives have been […]
Facility Layout – Objectives, Design and Factors Affecting the Layout
Introduction For an organization to have an effective and efficient manufacturing unit, it is important that special attention is given to facility layout. Facility layout is an arrangement of different aspects of manufacturing in an appropriate manner as to achieve desired production results. Facility layout considers available space, final product, safety of users and facility […]
“Knowledge should be shared. It only grows by sharing.”
This phrase finds its importance in today’s highly competitive and economically turbulent business world. Unless knowledge is shared among employees, it doesn’t take an organization anywhere. It’s important to share and manage it, in order to foster innovative thinking, develop and train employees and evolve into an ever-growing company.
Like it’s important to share knowledge within the organization, it’s equally important to determine what to share with whom. Not all details can be shared with everyone. This means that it is absolutely necessary to decide knowledge sharing rules and regulations, so that it can be used effectively and appropriately.
So, how do you think, knowledge is shared and distributed within an organization? What it takes to ensure its effective allocation and circulation? How do you automate the access and sharing of information?
Implementing knowledge-driven decision support system is one of the best ways to capture, process and store and share knowledge among employees. The information can be easily accessed by the user to resolve a variety of problems, issues or concerns.
Before the development of knowledge-driven DSS, employees with high intellect had to perform knowledge-intensive tasks. An expert in a particular area would know how to approach a problem and go about it. Similarly, knowledge-based DSS asks relevant questions, offers suggestions and gives advice to solve a problem. The only difference is that it’s automated and speeds up the whole process.
A knowledge-driven DSS
It’s an integration of computerized business intelligence tools and technologies customized to the needs and requirements of an organization. So, the focus is on
A computer-based reasoning system is similar to any other type of decision support system when it comes to their architecture. But it turns into a knowledge-drive decision support system when artificial intelligence technologies, management expert systems, data mining capabilities and other communication mechanisms are integrated.
Before we dig deeper, let’s learn about few important terms and concepts used alongside knowledge-drive decision support system. This will help gain an in-depth understanding of such support systems.
It’s important to be familiar with technical jargons that experts in this field use, in order to gain a deeper understanding of knowledge-driven DSS.
A knowledge-driven DSS is different from conventional systems in the way knowledge is extracted, processed and presented. The former attempts to emulate human reasoning while the latter responses to an even in a predefined manner. The main characteristics of knowledge-driven decision support systems are:
Knowledge-driven decision support systems are expert systems that are developed when decision-making cannot be supported using traditional methods. A knowledge-driven DSS project goes through various stages and can be difficult to manage. It’s important to be committed to monitor the development of a knowledge-driven DSS.
Development Stages
It’s important to monitor project development throughout very closely. It’s a collective effort of knowledge engineers, domain experts, DSS analysts, users and programmers. And a project manager keeps track of the scope, time, quality and budget, to ensure optimum allocation of resources and creation of a quality product. A project manager is a person responsible accomplishing the pre-decided objectives of a project.
Here are few examples of successful and popular knowledge-driven decision support systems:
Before, data mining systems came into existence, businesses had statisticians studying data. They would look at the data, formulate a hypothesis and carry out a test to approve or disapprove it. But a data mining software doesn’t need to establish a hypothesis to be approved or disapproved. Rather it works in ‘discovery mode’ and looks for patterns.
There are two types of data mining models that can be deployed:
There are a large number of tools and techniques used to extract/mine data. Which technique is to be used depends on the type of data to be extracted.
Case-based reasoning tools are used to determine the distance between or relationship among various components. A problem solved using this tool goes through 5 stages:
Fuzzy query and analysis is a data mining tool follows the mathematical concept for ‘fuzzy logics – the logic of uncertainty’ to determine results that are close to a particular criterion. Users can then pick one, depending upon his or her understanding.
As the same suggests, this helps analysts visualize complex relationships in multi-dimensional data. The benefit is that this tool graphically represents relationships among components from different perspectives. Statistical tools, such as regression, classification or cluster analysis are a part of this tool.
Similar to linear programming models, genetic algorithms conduct random experiments by selecting the genes (variables whose values are to be identified) and their values at random to find the fitness function. The software will also combines and mutates genes to find optimized value.
Knowledge extraction is the process of identifying relationships between various components or symptoms. It’s about making the best use of data. Data mining or knowledge creation proceeds through a number of stages:
Now you know what data mining is and how knowledge is extracted from data collection and analysis, let’s take a look at few data mining tools that companies are using.
Whenever you decide to develop or buy a knowledge-driven decision support system software application, it’s important to consider following criteria:
Knowledge-driven decision support systems help businesses solve problems and make decisions. However, a caution should be used when employing it. It doesn’t outsmart human intellect; rather it aids decision making.
Your email address will not be published. Required fields are marked *
