UNIT – 4

Simon's decision making model

Simon's decision making model holds four phases [1]:

1. Intelligence phase

Consist on surveying the environment for situations that demand decisions. It

implies an identification of the problem(s), the collection of information and the
establishment of goal and evaluative criteria.
2. Design phase

Involves delineating and analyzing various courses of action for the problem
identified in the intelligence phase. It implies an enumeration of a combination of
feasible alternatives and their evaluation on the basis of the criteria established in
the intelligence phase.
3. Choice phase

Involves selecting the best alternative.

4. Monitor phase (also called review or implementation)

Designed to insure the proper execution of choice.

PROJECT PLANNING
In planning phase, plan is made and strategies are set, taking into consideration the
company policies, procedures and rules.

Planning provides direction, unifying frame work, performance standards, and helps to
reveal future opportunities and threats.

In Planning, the following steps are followed.

 The Objectives of the projects in definite words

 Goals and stages intermediate to attain the final target

 Forecast and means of achieving goals i.e., activities.

  Organization resources-financial, managerial and operational-to carry out activities
and to determine what is feasible and what is not.

 Alternatives-individual courses of action that will allow accomplishing goals.

 For consistency with company’s policies

 An alternative which is not only consistent with its goals and concept but also one
that can be accomplished with the evaluated resources.

 Decision on a Plan

Forward Planning

 Planner starts from the initial event and builds up the events and activities logically
and sequentially until the end event is reached.

 What event comes next?

 What are dependent events?

 What events can take place concurrently?

Backward Planning

 The planner starts with the end event, and arranges the events and activities until
the initial event is reached.

 The planner asks himself “if we want to achieve this, what events or activities should
have taken place?
Combined Planning

 Combination of both forward planning and backward planning.

 At any stage the planner may need to traverse the network back and forth several
times until it is found to be satisfactory.

 Questions of the Planner

  What event or events must be completed before the particular event can start?

 What event or events follow this?

 What activities can be accomplished simultaneously

Resource Classification

 Manpower

 Material

 Machine

 Time

 Money

PROJECT SCHEDULING

 Scheduling is the allocation of resources

 Resources in conceptual sense are time & energy but in practical sense are the time,
manpower, equipment applied to material.

 Scheduling is the process of formalizing the planned functions, assigning the

starting and completion dates to each activity which proceeds in a logical sequence
and in an orderly and systematic manner.
In Scheduling, the following steps are followed.

 Detailed control information is to be calculated.

 Timings to events & activities are assigned

 Consideration must be given to resources generally concerned with those resources

whose availability is limited and which there by impose a constraint on the project.
Important ones are skilled, technical and supervisory manpower and capital
investment

 Resource Allocation
PROJECT CONTROLLING
This phase is carried during the execution of the project.

The difference between the scheduled performance and actual performance are reviewed
once the project starts.

 Project control is established to determine deviations from the basic plan, to

determine the precise effect of these deviations on the plan, and to replan and
reschedule to compensate for the deviations.

 Controlling, the following steps are followed.

 The Standards and targets are established and targets are generally exposed in
terms of time.

 Performance is measured against the standards set down in the first step.

 The Deviations from the standard are identified.

MATHEMATICAL PROGRAMMING

Mathematical programming is one of the most important techniques

available for quantitative decision making. The general purpose of mathematical
programming is finding an optimal solution for allocation of limited resources to
perform competing activities. The optimality is defined with respect to important
performance evaluation criteria, such as cost, time, and profit. Mathematical
programming uses a compact mathematical model for describing the problem of
concern. The solution is searched among all feasible alternatives. The search is
executed in an intelligent manner, allowing the evaluation of problems with a large
number of feasible solutions.

What is Linear Programming?

Linear programming (LP) or Linear Optimisation may be defined as the problem of
maximizing or minimizing a linear function which is subjected to linear constraints. The
constraints may be equalities or inequalities. The optimisation problems involve the
calculation of profit and loss. Linear programming problems are an important class of
optimisation problems, that helps to find the feasible region and optimise the solution in
order to have the highest or lowest value of the function.
Linear programming is the method of considering different inequalities relevant to a
situation and calculating the best value that is required to be obtained in those conditions.
Some of the assumption taken while working with linear programming are:

 The number of constraints should be expressed in the quantitative terms

 The relationship between the constraints and the objective function should be linear
 The linear function (i.e., objective function) is to be optimised

Components of Linear Programming

The basic components of the LP are as follows:

 Decision Variables
 Constraints
 Data
 Objective Functions

Characteristics of Linear Programming

The following are the five characteristics of the linear programming problem:
Constraints – The limitations should be expressed in the mathematical form, regarding the
resource.
Objective Function – In a problem, the objective function should be specified in a
quantitative way.
Linearity – The relationship between two or more variables in the function must be linear.
It means that the degree of the variable is one.
Finiteness – There should be finite and infinite input and output numbers. In case, if the
function has infinite factors, the optimal solution is not feasible.
Non-negativity – The variable value should be positive or zero. It should not be a negative
value.
Decision Variables – The decision variable will decide the output. It gives the ultimate
solution of the problem. For any problem, the first step is to identify the decision variables.

Inventory Management

Inventory management is a step in the supply chain where inventory and stock quantities
are tracked in and out of your warehouse.

The goal of inventory management systems is to know where your inventory is at any given
time and how much of it you have in order to manage inventory levels correctly.
Some companies may opt to scan in inventory via a barcode scanner to increase efficiency
along pick routes and accuracy.

Unlike an ERP system, an inventory management system focuses on one supply chain
process. They often come with the ability to integrate with other software systems – point of
sale, channel management, shipping – so you can build a personalized integration stack to
the needs of your unique business.

What is ABC Analysis?

ABC analysis is a type of inventory categorization method in which inventory is divided

into three categories, A, B, and C, in descending value. A has the highest value items, B is
lower value than A, and C has the lowest value.

Inventory management and optimization in general is critical for business to help keep
their costs under control. ABC analysis works towards this goal by letting management
focus most of their attention on the few highest value goods (the A-items) and not on the
many low value, trivial goods (the C-items).

ABC Analysis Rules

ABC analysis may be seen to share similar ideas as the Pareto principle, which states that
80% of overall consumption value comes from only 20% of items. Plainly, it means that
20% of your products will bring in 80% of your revenues.

ABC analysis works by breaking it down in the following ways:

 A-items: 20% of all goods contribute to 70-80% of the annual consumption value of
the items
 B-items: 30% of all goods contribute to 15-25% of the annual consumption value of
the items
 C-items: 50% of all goods contribute only 5% of the annual consumption value of
the items.
MATERIAL REQUIREMENTS PLANNING (MRP)

Material requirements planning (MRP) is a production planning, scheduling,

and inventory control system used to managemanufacturing processes. Most MRP systems
are software-based, but it is possible to conduct MRP by hand as well.
An MRP system is intended to simultaneously meet three objectives:

 Ensure raw materials are available for production and products are available
for delivery to customers.
 Maintain the lowest possible material and product levels in store
 Plan manufacturing activities, delivery schedules and purchasing activities.

ARTIFICIAL INTELLIGENCE IN INFORMATION MANAGEMENT

Information management has changed from pure document management and

archiving into a real business enabler. Today’s intelligent information management
solutions offer ways to automate the time-consuming and often boring document-driven
processes within a business.

One of the key drivers in this automation is the use of artificial intelligence and machine
learning. AI and machine learning have the ability to reason and discover meaning as well
as learn from past experience. Moreover, artificial intelligence systems can easily churn
through lots of information to recognize patterns and categories in the data. That ability is
put to work to enable new ways to search, find, use and manage information, and add
automated workflows to document management processes.

But, artificial intelligence and machine learning mean very different things to different
organizations and people. To us, they mean something that really drives the development of
the industry but is at the same time very simple and easy for the end users to use and
benefit from. Our objective is to allow customers to intelligently find and manage the
critical data they need to make smart decisions.

Meaning of Knowledge Based Expert System:

An expert system is the highest form of management computing office automation which

allows the communication and manipulation of documents.

Decision support systems aid in problem solving by allowing for manipulation of data and

model

Expert systems go beyond traditional manipulation of this type as they allow experts to

‘teach’ computers about their fields so that the system may support more of the decision

making process for less expert decision makers.

In this sense, an expert system is software that contains a knowledge base of facts and

relationships and has the ability to make inferences based on that knowledge base. An

expert system is a computer based information system in which knowledge is represented

in data, in which the processing of the knowledge is directed primarily by computer

programs.

Expert systems represent one of the most advanced facts of information technology. That

is, they aid people in some of the most complex and least understood human information
handling tasks, i.e., decision making, problem solving, diagnosis and learning. They do this

by storing a large amount of factual information on a subject area, together with lines of

reasoning employed by human experts in that area.

Most of this material is supplied to the program at the time it is written, but it also has

facilities for adding to this base of information as it is applied in new situations. The subject

expertise is provided initially by interviews and observations of successful PR actioners of

the subject.