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Problems with Solutions - Business Process Improvement | BIT 3454, Exams of Introduction to Business Management

Virginia Polytechnic Institute and State University (Virginia Tech)Introduction to Business Management
Prof. Deborah F. Cook

Material Type: Exam; Professor: Cook; Class: Business Process Improvement; Subject: Business Information Technology; University: Virginia Polytechnic Institute And State University; Term: Fall 2013;

Typology: Exams

2012/2013

Uploaded on 12/05/2013

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bg1
Take it with a grain of salt, but it’s pretty solid context wise. no chart math
Text mining
1. Q? how does data mining relate to text mining being accomplished. (similar techniques?
Clustering?)
a. Association analysis, visualization, predictive analytics?
2. Q 2?, crawling, webscrapping
3. Data mining review
a. Lots of rows and columns
b. Low signal to noise ratio
c. Not always for specific analysis
d. General models
i. Prescriptive
ii. Descriptive
4. Text mining
a. Turns text data into numeric data for analysis or predictive modeling
i. Specifically patterns, connections, profiles, and trends
b. Text mining applications
i. Analysis of survey data
ii. Analysis of comment data
iii. Spam identification
iv. News articles
v. Warranty claims
vi. Social media
c. Letting Text mining do the legwork
i. Finds material
ii. Reads
iii. Understands
iv. Consolidates
v. Only thing left is for the human to absorb and act on given data
5. Terminology
a. Stopwords
i. The, to, and
ii. Words filtered out prior to processing the text
b. Stemwords
i. Knee, knees
ii. Singular/plural
iii. Syn
1. Fracture, broken
c. Collocation
i. Words appearing near each other
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Partial preview of the text

Download Problems with Solutions - Business Process Improvement | BIT 3454 and more Exams Introduction to Business Management in PDF only on Docsity!

Take it with a grain of salt, but it’s pretty solid context wise. no chart math

Text mining

  1. Q? how does data mining relate to text mining being accomplished. (similar techniques? Clustering?) a. Association analysis, visualization, predictive analytics?
  2. Q 2?, crawling, webscrapping
  3. Data mining review a. Lots of rows and columns b. Low signal to noise ratio c. Not always for specific analysis d. General models i. Prescriptive ii. Descriptive
  4. Text mining a. Turns text data into numeric data for analysis or predictive modeling i. Specifically patterns, connections, profiles, and trends b. Text mining applications i. Analysis of survey data ii. Analysis of comment data iii. Spam identification iv. News articles v. Warranty claims vi. Social media c. Letting Text mining do the legwork i. Finds material ii. Reads iii. Understands iv. Consolidates v. Only thing left is for the human to absorb and act on given data
  5. Terminology a. Stopwords i. The, to, and ii. Words filtered out prior to processing the text b. Stemwords i. Knee, knees ii. Singular/plural iii. Syn
  6. Fracture, broken c. Collocation i. Words appearing near each other

d. Concordance i. Context of word use e. Entity recognition i. Tasks most likely to accomplish f. Clustering i. Process of organizing objects into groups whose members are similar in some way ii. Low signal to noise ratio

  1. Text Mining example a. Dow Chemical company i. Began text mining 1996 ii. Accessed hundreds of thousands documents
  2. Characteristics of text a. Word frequency i. List of words and their frequency b. Collocation i. Words commonly appearing near each other c. Concordance i. The contexts of a given word or set of words d. Entity recognition i. Identifying names, places, time periods, etc
  3. Opinion mining a. Is the text subjective or objective i. Polarity b. Is the text positive or negative i. Polarity c. Determine strength of PN polarity

Improve

  1. An improved business process has a. Enhanced functionality i. Outputs delivered and business goals achieved b. Increased quality i. Conformance, operability, reliability c. Increased flexibility i. Adaptability to variations and compliance with future needs d. Reduced operation time i. Cycle time and queue, service, wait e. Reduced cost i. Operation, failure, preventive, appraisal
  2. Improve

b. Red i. Feelings. Allows free speech “This is how I feel about the matter” c. Yellow i. Brightness. Moves ahead of situation with positive hope d. Black i. Caution. Negative assessment e. Green i. Creative,. lateral thinker f. Blue i. Manage. “in-control” hat

  1. TRIZ a. Russian, “a problem-solving, analysis and forecasting tool derived from the study of patterns of invention in the global patent literature.” b. Also known as “theory of inventive problem solving” c. Generalized solutions i. Problems and solutions are repeated across industries and sciences ii. Patterns of technical evolution tend to be repeated across industries and sciences iii. Creative innovations often use scientific effects outside the field where they were developed d. Eliminate contradictions i. Example 1. Dairy farm couldn’t dry cow manure due to increased energy cost 2. TRIZ led to drying method used for concentration of fruit juice, which required no heat
  2. 7 sim keys to BI a. lots of BI projects fail b. un-related focus on the customer c. senior sponsors that are interested and supportive d. crystal clear vision e. actually manage change f. plain spoken and extensive g. people transition through change differently
  3. 7 sim steps a. doubtful on exam
  4. Improvement Measures a. Time b. Quality c. Cost i. Cost of process enactment ii. COQ d. Flexibility

e. Performer capability maturity model i. Level of process knowledge, has lean focus, optimizing processes, includes breakdown of categories

Control Part 1

  1. Control a. Understanding variation b. Variables control charts c. Control chart theory d. Attribute control charts
  2. Definition of Control a. Successful control is based on an understanding of variation i. Red bead experiment 1. Illustrates typical bad management 2. Promotes performing the process right the first time 3. All workers perform in a system out of their control 4. Experiment a. There are 4000 beads, 800 red, 3200 white b. Worker is supposed to choose 50 white beads per day with 50 tries ii. Funnel experiment
  3. Used to describe the adverse effects of tampering with a process by making changes to it without first making a careful study of the possible causes of the variation in that process
  4. Experiment a. A marble is dropped through a funnel onto sheet of paper b. Paper has a target where we want marble to come as close as possible c. There are four rules used to try and optimize hitting the target
  5. Variation and tampering a. Variation i. A stable system exhibits chance causes of variation ii. Variations outside this stable pattern are called assignable causes of variation
  6. Control Charts a. A process is in control if the observed variation is due to inherent or natural variation i. This variability is the cumulative effect of many small, essentially uncontrollable, causes b. A process in and out of control if a relatively large variation is introduced that can be traced to an assignable cause
  7. Questions

a. This is due to the assessment for attribute is done more quickly by simple inspection or count, where variable data requires a measuring instrument

  1. Control Charts for Variable Data i. Reference red notebook for math problems ii. Control limits relate to averages of samples, where spec limits relate to individual measurements iii. Control limits are not the same as spec limits iv. Control limits are not the same as natural variation limits
  2. Process Monitoring and control a. Once a process is back in control, it should be monitored daily b. Workers should use control charts and be trained as necessary c. Control charts indicate when to take action, and more importantly when to leave a process alone d. When a process is in statistical control, the points on a control chart fluctuate randomly between the control limits with no recognizable pattern
  3. Variation a. Understanding of variation enables us to allow for variation when making decisions
  4. Tampering a. Blaming individuals for intrinsic system problems b. Rewarding individuals for success also is intrinsic to system c. Often causes more variation, a funnel if you will
  5. Common vs. Special Causes a. Common i. Everyday variation (normal) ii. Typically comes from many sources iii. Often mistaken for special causes b. Special Causes i. Out of the ordinary, specific source of variation ii. Sometimes written off as common c. Confusion between the two leads to frustration, greater variability, and higher costs d. Deming – “variation is inherent to system”
  6. Misinterpreted Variation a. Confusion = bad b. Leads to i. Diverted attention from important matters ii. Increased variation iii. Loss of productivity iv. Loss of confidence v. Jeopardizing careers
  7. Typical Out-of-Control Patterns (rules of usage phase)

a. Rule 1 – if a point falls outside control limit, that point is OOC i. Operational definition => runs rules => identifies patterns

  1. Runs rules – specific rules regarding process b. Rule 2. A process exhibits a lack of control if any two out of three consecutive subgroup statistics fall in one of the A zones or beyond on the same side of the centerline. i. Two or three points fall outside 2-sigma c. Rule 3. A process exhibits a lack of control if four out of five consecutive subgroup statistics fall in one of the B zones or beyond on the same side of the centerline.

i.

  1. Using x-bar and R-chart together a. A down trend in R-chart causes hugging of center line in x-bar chart

Control Part 3

  1. Control Chart theory a. Hypothesis testing i. Used to determine whether claims on product or process parameters are valid ii. IN SPC we are really testing a hypothesis of the process being in-control iii. Based on sample data iv. Compare null hypothesis and alternative hypothesis v. Type 1 and type 2 errors
  2. Type 1 – infer the process is OOC when it actually in control
  3. Type 2 – infer the process is in control when its OCC b. Determination of control limits c. Rational subgrouping d. How to judge performance of control chart i. Average Run Length (ARL) – the number of samples on average required to detect OOC signal 1. ARLs – shift per change in process e. Operating Characteristic Curve i. Measure of goodness of control chart’s ability to detect changes in process parameters
  4. Uses of control charts a. Common cause (top); special cause (bottom)

b. Track Improvements i. Make sure process is centered and stable, and additional improvements made to process c. Eliminate process control mistakes i. Over-adjustment ii. Under-adjustment d. Levels of Quality Consciousness i. No quality consciousness ii. Defect detection iii. Defect prevention iv. Never-ending improvement v. Innovations

  1. Limits a. Control limits b. Specification limits i. Should never be shown on x-bar charts c. Natural process variation limits (range)

Attributes

  1. Types of Attributes a. Defective i. Item is nonconforming unit b. Defect i. An imperfection of some type that is undesirable, although it does not necessarily render the entire good or service unusable
  2. Basic types of attribute control charts a. Classification charts b. Count charts

o Order/ship it  Manufacturing  Internal and external customers o Internal  Assembly line  designers o external  law enforcement  citizens  customer expectations o cheap o accurate o easy to carry o easy to use   Titleist  Marketing  Market-driven company Chocolate manufacturing