Pharma Business Solutions

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Pharma Business Solutions

• Recom Systems Limited

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Competition in Pharma

• To stay competitive and increase profitability,
  manufacturers need to address complex challenges
• Regulatory compliance
• Top product quality
• Time to Market
• Global manufacturing competition
• Optimization of Supply Chain
• Satisfaction of local requirements
• Optimized ROI of equipment systems

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Process Analytical Technology (PAT)
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MANUFACTURING
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Data Collection

• Data feed through forms
• Data feed through interfaces
• Data cleaning
• Data formatting
• Data Storage
• Past Data Storage and integration

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Defining Business Requirements

• Reports Definition
• Frequency of Reports Definition
• Login Control
• Information Control
• Reporting formats definition
• Software and hardware available
• Software and hardware required
• Gap Analysis

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Designing System Architecture

• Creating Draft System Architecture
• Customer Presentation
• Customer Feedback
• Designing Solution Architecture
• Solution Variants
• Generating Requirement Compliance
• Gap Analysis

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Agreement Document

• Agreement Document Definition
• Non-Disclosure Agreement
• Pricing of Services and Payment Schedule
• Inclusion of Service Tax and VAT/Sales Tax
• Change Management
• Change Pricing Mechanism
• Commercial terms and conditions
• Jurisdiction decision

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Detailed Block Design

• Detailed Block Diagram design taking all customer
  requirements
• Creating Systems Modules
• Creating Operating Systems Requirements
• Operating System License Definition and pricing
• Data Base Server Definition and User Licensing
• Database Server Pricing
• Hardware Requirements and hardware Pricing
• Networking Requirements and Network Pricing

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Data Base Design

• Architecture of Database
• Tables Design
• Stored Procedures Design
• Triggers Design
• Interfaces Design
• Design of Data Integration Applications
• Design of Reporting Applications
• Security of Database
• Logins and passwords management
• Web interfaces and applications design
• Data Downloading from web database

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Business Intelligence Applications

• Generating Multi Dimensional Cubes
• Generating Multiple Dimensions
• Dimensional Analysis
• KPIs
• Business Intelligence Analyzing Algorithms
• Formula Storage
• Reports Generation

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Systems Deployment

• Lab tested systems are deployed on customers
  servers
• Integration Customers existing systems
• Total System Integration
• System Beta Testing
• Gap analysis
• Design Modifications and Application Tuning
• Re-deployment of applications

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Customer Training

• Training of Customer staff
• Training of Customer Information Technology Staff
• Handover of Complete Systems

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Warranty and After Sales support

• Warranty of applications
• After sales support for 1 year
• Annual Maintenance Contract

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Information required at different management
levels
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Levels of Management Decision Making

• Strategic management
• Executives develop organizational goals,
  strategies, policies, and objectives
• As part of a strategic planning process
• Tactical management
• Managers and business professionals in
  self-directed teams
• Develop short- and medium-range plans, schedules
  and budgets
• Specify the policies, procedures and business
  objectives for their subunits

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Levels of Management Decision Making

• Operational management
• Managers or members of self-directed teams
• Develop short-range plans such as weekly
  production schedules

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Information Quality

• Information products whose characteristics,
  attributes, or qualities make the information
  more value
• Information has 3 dimensions
• Time
• Content
• Form

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Attributes of Information Quality
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Decision Structure

• Structured situations where the procedures to
  follow when a decision is needed can be specified
  in advance
• Unstructured decision situations where it is
  not possible to specify in advance most of the
  decision procedures to follow
• Semistructured - decision procedures that can be
  prespecified, but not enough to lead to a
  definite recommended decision

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Information Systems to support decisions
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Decision Support Trends

• Personalized proactive decision analytics
• Web-Based applications
• Decisions at lower levels of management and by
  teams and individuals
• Business intelligence applications

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Business Intelligence Applications
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Decision Support Systems

• DSS
• Provide interactive information support to
  managers and business professionals during the
  decision-making process
• Use
• Analytical models
• Specialized databases
• A decision makers own insights and judgments
• Interactive computer-based modeling
• To support semistructured business decisions

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DSS components
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DSS Model base

• Model base
• A software component that consists of models used
  in computational and analytical routines that
  mathematically express relations among variables
• Examples
• Linear programming models,
• Multiple regression forecasting models
• Capital budgeting present value models

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Management Information Systems

• MIS
• Produces information products that support many
  of the day-to-day decision-making needs of
  managers and business professionals
• Prespecified reports, displays and responses
• Support more structured decisions

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MIS Reporting Alternatives

• Periodic Scheduled Reports
• Prespecified format on a regular basis
• Exception Reports
• Reports about exceptional conditions
• May be produced regularly or when exception
  occurs
• Demand Reports and Responses
• Information available when demanded
• Push Reporting
• Information pushed to manager

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Online Analytical Processing

• OLAP
• Enables mangers and analysts to examine and
  manipulate large amounts of detailed and
  consolidated data from many perspectives
• Done interactively in real time with rapid
  response

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OLAP Analytical Operations

• Consolidation
• Aggregation of data
• Drill-down
• Display detail data that comprise consolidated
  data
• Slicing and Dicing
• Ability to look at the database from different
  viewpoints

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OLAP Technology
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Geographic Information Systems

• GIS
• DSS that uses geographic databases to construct
  and display maps and other graphics displays
• That support decisions affecting the geographic
  distribution of people and other resources
• Often used with Global Position Systems (GPS)
  devices

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Data Visualization Systems

• DVS
• DSS that represents complex data using
  interactive three-dimensional graphical forms
  such as charts, graphs, and maps
• DVS tools help users to interactively sort,
  subdivide, combine, and organize data while it is
  in its graphical form.

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Using DSS

• What-if Analysis
• End user makes changes to variables, or
  relationships among variables, and observes the
  resulting changes in the values of other
  variables
• Sensitivity Analysis
• Value of only one variable is changed repeatedly
  and the resulting changes in other variables are
  observed

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Using DSS

• Goal-Seeking
• Set a target value for a variable and then
  repeatedly change other variables until the
  target value is achieved
• How can analysis
• Optimization
• Goal is to find the optimum value for one or more
  target variables given certain constraints
• One or more other variables are changed
  repeatedly until the best values for the target
  variables are discovered

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Data Mining

• Main purpose is to provide decision support to
  managers and business professionals through
  knowledge discovery
• Analyzes vast store of historical business data
• Tries to discover patterns, trends, and
  correlations hidden in the data that can help a
  company improve its business performance
• Use regression, decision tree, neural network,
  cluster analysis, or market basket analysis

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Market Basket Analysis

• One of most common data mining for marketing
• The purpose is to determine what products
  customers purchase together with other products

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Executive Information Systems

• EIS
• Combine many features of MIS and DSS
• Provide top executives with immediate and easy
  access to information
• About the factors that are critical to
  accomplishing an organizations strategic
  objectives (Critical success factors)
• So popular, expanded to managers, analysts and
  other knowledge workers

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Features of an EIS

• Information presented in forms tailored to the
  preferences of the executives using the system
• Customizable graphical user interfaces
• Exception reporting
• Trend analysis
• Drill down capability

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Enterprise Interface Portals

• EIP
• Web-based interface
• Integration of MIS, DSS, EIS, and other
  technologies
• Gives all intranet users and selected extranet
  users access
• To a variety of internal and external business
  applications and services
• Typically tailored to the user giving them a
  personalized digital dashboard

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Enterprise Information Portal Components
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Knowledge Management Systems

• The use of information technology to help gather,
  organize, and share business knowledge within an
  organization
• Enterprise Knowledge Portals
• EIPs that are the entry to corporate intranets
  that serve as knowledge management systems

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Enterprise Knowledge Portals
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Case 2 Artificial IntelligenceThe Dawn of the
Digital Brain

• Numenta will translate the way the brain works
  into an algorithm that can run on a new type of
  computer
• The human brain does not work like a computer
• Intelligence, according to Hawkins, is pattern
  recognition

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Artificial Intelligence (AI)

• A field of science and technology based on
  disciplines such as computer science, biology,
  psychology, linguistics, mathematics, and
  engineering
• Goal is to develop computers that can simulate
  the ability to think, as well as see, hear, walk,
  talk, and feel

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Attributes of Intelligent Behavior

• Think and reason
• Use reason to solve problems
• Learn or understand from experience
• Acquire and apply knowledge
• Exhibit creativity and imagination
• Deal with complex or perplexing situations
• Respond quickly and successfully to new
  situations
• Recognize the relative importance of elements in
  a situation
• Handle ambiguous, incomplete, or erroneous
  information

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Domains of Artificial Intelligence
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Cognitive Science

• Based in biology, neurology, psychology, etc.
• Focuses on researching how the human brain works
  and how humans think and learn

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Robotics

• Based in AI, engineering and physiology
• Robot machines with computer intelligence and
  computer controlled, humanlike physical
  capabilities

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Natural Interfaces

• Based in linguistics, psychology, computer
  science, etc.
• Includes natural language and speech recognition
• Development of multisensory devices that use a
  variety of body movements to operate computers
• Virtual reality
• Using multisensory human-computer interfaces that
  enable human users to experience
  computer-simulated objects, spaces and worlds
  as if they actually exist

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Expert Systems

• ES
• A knowledge-based information system (KBIS) that
  uses its knowledge about a specific, complex
  application to act as an expert consultant to end
  users
• KBIS is a system that adds a knowledge base to
  the other components on an IS

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Expert System Components

• Knowledge Base
• Facts about specific subject area
• Heuristics that express the reasoning procedures
  of an expert (rules of thumb)
• Software Resources
• Inference engine processes the knowledge and
  makes inferences to make recommend course of
  action
• User interface programs to communicate with end
  user
• Explanation programs to explain the reasoning
  process to end user

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Expert System Components
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Methods of Knowledge Representation

• Case-Based knowledge organized in form of cases
• Cases examples of past performance, occurrences
  and experiences
• Frame-Based knowledge organized in a hierarchy
  or network of frames
• Frames entities consisting of a complex package
  of data values

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Methods of Knowledge Representation

• Object-Based knowledge organized in network of
  objects
• Objects data elements and the methods or
  processes that act on those data
• Rule-Based knowledge represented in rules and
  statements of fact
• Rules statements that typically take the form
  of a premise and a conclusion
• Such as, If (condition) then (conclusion)

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Expert System Benefits

• Faster and more consistent than an expert
• Can have the knowledge of several experts
• Does not get tired or distracted by overwork or
  stress
• Helps preserve and reproduce the knowledge of
  experts

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Expert System Limitations

• Limited focus
• Inability to learn
• Maintenance problems
• Developmental costs
• Can only solve specific types of problems in a
  limited domain of knowledge

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Suitability Criteria for Expert Systems

• Domain subject area relatively small and
  limited to well-defined area
• Expertise solutions require the efforts of an
  expert
• Complexity solution of the problem is a complex
  task that requires logical inference processing
  (not possible in conventional information
  processing)
• Structure solution process must be able to cope
  with ill-structured, uncertain, missing and
  conflicting data
• Availability an expert exists who is articulate
  and cooperative

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Development Tool

• Expert System Shell
• Software package consisting of an expert system
  without its knowledge base
• Has inference engine and user interface programs

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Knowledge Engineer

• A professional who works with experts to capture
  the knowledge they possess
• Builds the knowledge base using an iterative,
  prototyping process

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Neural Networks

• Computing systems modeled after the brains
  mesh-like network of interconnected processing
  elements, called neurons
• Interconnected processors operate in parallel and
  interact with each other
• Allows network to learn from data it processes

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Fuzzy Logic

• Method of reasoning that resembles human
  reasoning
• Allows for approximate values and inferences and
  incomplete or ambiguous data instead of relying
  only on crisp data
• Uses terms such as very high rather than
  precise measures

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Genetic Algorithms

• Software that uses
• Darwinian (survival of the fittest), randomizing,
  and other mathematical functions
• To simulate an evolutionary process that can
  yield increasingly better solutions to a problem

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Virtual Reality (VR)

• Computer-simulated reality
• Relies on multisensory input/output devices such
  as
• a tracking headset with video goggles and stereo
  earphones,
• a data glove or jumpsuit with fiber-optic sensors
  that track your body movements, and
• a walker that monitors the movement of your feet

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Intelligent Agents

• A software surrogate for an end user or a process
  that fulfills a stated need or activity
• Uses its built-in and learned knowledge base
• To make decisions and accomplish tasks in a way
  that fulfills the intentions of a user
• Also called software robots or bots

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User Interface Agents

• Interface Tutors observe user computer
  operations, correct user mistakes, and provide
  hints and advice on efficient software use
• Presentation show information in a variety of
  forms and media based on user preferences
• Network Navigation discover paths to
  information and provide ways to view information
  based on user preferences
• Role-Playing play what-if games and other roles
  to help users understand information and make
  better decisions

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Information Management Agents

• Search Agents help users find files and
  databases, search for desired information, and
  suggest and find new types of information
  products, media, and resources
• Information Brokers provide commercial services
  to discover and develop information resources
  that fit the business or personal needs of a user
• Information Filters receive, find, filter,
  discard, save, forward, and notify users about
  products received or desired

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Data Mining as an Application Platform
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What is Data Mining Anyway?

• Machine learning of patterns in data
• Application of patterns to new data

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What is Data Mining Anyway?

• Machine learning of patterns in data
• Application of patterns to new data

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Comparative BenefitsPredictive Projects versus
Nonpredictive Projects
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What Does Data Mining Do?
Explores Your Data
Finds Patterns
Performs Predictions
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What does Data Mining do?Illustrated
DB data Client data Application data
DB data Client data Application data Just one
row
DM Engine
DM Engine
Predicted Data
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Data Mining Extensions to SQL (DMX)
CREATE MINING MODEL CreditRisk (CustID LONG
KEY, Gender TEXT DISCRETE, Income
LONG CONTINUOUS, Profession TEXT
DISCRETE, Risk TEXT DISCRETE PREDICT) USING
Microsoft_Decision_Trees
INSERT INTO CreditRisk (CustId, Gender, Income,
Profession, Risk) Select CustomerID, Gender,
Income, Profession,Risk From Customers
Select NewCustomers.CustomerID, CreditRisk.Risk,
PredictProbability(CreditRisk.Risk) FROM
CreditRisk PREDICTION JOIN NewCustomers ON
CreditRisk.GenderNewCustomer.Gender AND
CreditRisk.IncomeNewCustomer.Income AND
CreditRisk.ProfessionNewCustomer.Profession
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Server Mining Architecture
Analysis Services Server
Mining Model
Data Mining Algorithm
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Data Mining ProcessCRISP-DM
Doing Data Mining
Data
Putting Data Mining to Work
www.crisp-dm.org
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Data Mining Process in SQLCRISP-DM
SSAS (OLAP) DSV
SSIS SSAS (OLAP)
Data
Data
SSIS SSAS(OLAP) SSRS Flexible APIs
SSAS (Data Mining)
www.crisp-dm.org
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What Do Data Mining Applications Do?
Finds Patterns
Performs Predictions
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Data Mining Interfaces
C App
VB App
.Net App
Any App
OLEDB for OLAP/DM
ADO/DSO
Any Platform, Any Device
AMO
ADOMD.NET
WAN
XMLA Over TCP/IP
XMLA Over HTTP
Analysis Server (msmdsrv.exe)
OLAP
Data Mining
DM Interfaces
Server ADOMD.NET
.Net Stored Procedures
Microsoft Algorithms
Third Party Algorithms
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Algorithm Training
Algorithm Module
Case Processor (generates and prepares all
training cases)
StartCases
Process One Case
No
Yes
Converged/complete?
Done!
Persist patterns
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DM data flow
New Dataset
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