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IDS 302 Exam #2


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operates on "pull" or demand basis - lean production systems, zero inventory system, continuous flow, material as needed, stockless production - ultimate goal: a balanced system one that achieves smoth, rapid flow of baterials, no bottle necks - (1) Eliminate disruptions (2) Make a system flexible - able to handle a mix of products
What happens to control limits when p increases?
Wider - based on formula
Acceptance Sampling
Either accept goods or reject them when shipment arrives based on sample: 1) Inspection before the actual process (raw materials) (inputs), 2) Inspection after the production (finished goods)(outputs)[upon delivery of goods], 3) Inspection of lots or batches to measure conformance to prearranged standards; is an old school philosophy but still used heavily --> NOT CONSISTENT WITH TQM
Quality Costs
1) Internal failure, 2) External failure, 3) Appraisal costs, 4) Prevention costs
Cause and Effect Diagram
FISH BONE diagram - invented by Ishikawa - Lists results of process and causes of problems, uses knowledge of labor, promotes discussion, helps develop corrective action, like Pareto
Inventory Functions
1) Meet anticipated demand (anticipation stocks), 2) Smooth Production Req. (seasonal inventories), 3) Decouple operations (buffers to permit continuous operations), 4) Protect against stockouts (safety stocks)
4 Items Negotiated Between Buyer and Seller
1) AQL (Acceptable Quality Level), 2) LTPD (Lot Tolerance Percent Defective), 3) Type I Eror, 4) Type II Error
Acceptable Quality Level (AQL)
The general/average long-term small percentage level of defects at which the consumer is still willing to accept lot as "good"
Product Dimensions of Quality
Performance, Aesthetics, Special features, Conformance, Reliability, Durability, Perceived Quality, Serviceability, Safety, Other
Random Variation
Caused by infinite factors, non-controllable, It is difficult to control and minimize this type of variation, must improve system, cannot eliminate this type of variation - is always the same amount - is the natural spread of the data - examples: caused bydust, air quality, attitudes, molecules, static electricity
Quality Control
1) SPC 2) Acceptance Sampling
Baldridge Award
yearly quality award given by U.S. govt. to recognize achievements of U.S. companies - est. 1987
Walter Shewart
perfected statistical control charts in 1931
woker's have authority to stop production
green, yellow, red lights indicate possible quality problems
Assignable Causes of Variation
Unusual, the reason for "out-of-control" process, source of variation can be identified; permanent or intermittent, examples: machine malfunction, worker mistakes, we want to eliminate this type of variation, NOT RANDOM
Shows distribution of measurements to compare against specifications - look for symmetric distribution else may have abnormalities - static view of process - want symmetrical, breathing room on ends
Type I Error
False alarm - producer's risk - they think process is out of control when it actually is in control - Probabibility of happening: alpha
ISO (International Organization for Standardization)
promotes worldwide genetic standards for improvement of quality, productivity, operating efficiency - sets minimum standards/guidelines and the monitors firms for adherence to these quality standards
Joe Juran
Strategic quality planning, pareto, quality = fitness for use, juran's trilogy: quality plan, control, improvement - Quality Plan: prepare to meet goals, Control: meeting goals in operations, Improvement: increasing quality, performing
Problems with Acceptance Sampling
Doesnt add value, Not Consistent with TQM, allows for and expects some defects to occur (contradicts TQM), only finds bad quality doesn't measure good quality, unsophisticated
Prevention Costs
most important - costs of ensuring process conformance to specifications - preven defects through systems, training (extensive & ongoing), vendors, improve workmanship, quality machines, good hiring, good suppliers
"card" - in dicates quantity, two-bin system; maintains discipline of production
Types of Variation
1) Random Variation - "Common Causes" or "Natural Variation" 2) Assignable Causes of Variation
binomial distribution - percentage or proportion - two categories
Lot Tolerance Percent Defective (LTPD)
Maximum, upper limit percentage of defects in a single shipment that the buyer (consumer) will tolerate without rejecting. If percent of defects is greater than LTPD, customer will flat out reject the shipment (bad lot)
Determinants of quality
Design, Conformance, Ease of Use, Service after delivery, Monitoring
As a process gets more exacting, what should happen to control limits?
Limits get closer together
What effect on width of control chart does an increase in sample size have?
Limits get closer together - decreases upper limit, increases lower limit
Appraisal Costs
inspections, testing; try to find defects
Edward Deming
"Major source of poor quality is too high of variation," "It is the top management's job to improve quality," "Train employees in problem solving and quality management"
Steps in developing control charts
1) Prepare (Plan), chose measure, how to collect data, frequency; 2) Collect data: record date, calculate stats, plot; 3) Determine trial limits UCL, LCL, center line; 4) Analyze results: ID outliers, detect non-random (assignable) variation, visualize, determine if process stable, recomputed units 5) Problem solve: continue process, take corrective action
the ability of a product or service to consistently meet or exceed customer expectations
continuous improvement, do everything well
Quality of Conformance
Producer's view - is product manufactured according to blue print and design
Process Control Methods for Attriubte Data
P-chart (binominal distribution) or C-chart (poisson distribution)
Quality characteristics
Must become the highest priority of the company
Attribute Data
Discrete, categorical data, whole numbers, number of defective products - It is easier to obtain attribute data and it uses larger sample sizes (50, etc.)
Elements of JIT
1) flexible resources, 2) cellular layouts, 3) pull production, 4) kanban control, 5) small lot production
Statistical Process Control
Monitoring the production process (transformation) during the actual transformation to make sure variation is minimized by taking samples of the product and seeing if it fits into an acceptable range (limits) (target value); checking for assignable variation (eliminate) --> CONSISTENT WITH TQM
Six Sigma
A philosophy, goal, and or methodology utilized to remove waste and to improve the quality, cost, and time performance of a business operation. The process is so exact and precise taht very few defects occur even when the process mean shifts (up to 1.5 SD's left or right) - Japanese development results in as few as 2 defects per billion - over 99.999999% accuracy
Range Chart
Done before x bar chart - searches for a shift in variability/stability/dispersion of process - average fill may still be 16oz but variation/standard deviation suddenly increased - looks for variation (spread) problems and process uniformity - want narrow variation
Periodic System
Variable quantity, fixed time - physical count of inventory at intervals (weekly, monthly) - used by small retailers, advantage = economies in processing & shipping, diadvantage = infrequency forces carrying extra stock
What does a service level of 95% suggest?
Have enough inventory to meet 95% of the time
Koaru Ishikawa
Cause & Effect (Fishbone)
Process Capability Ratio
should be at least 1 - preferred more than 1.33 - Tolerances are simply a request, not control limits, machine's natural behavior within the toleracnes
Perpetual Inventory System
Continual, Fixed Order Quantity - system that keeps track of removals from inventory continuously, monitoring current levels of each item - reorder at fixed level or quantity - advantages = determine optimal order quantity, disadvanatge = record keeping. Can be (1) batch: inventory done periodically, could result in dropping below reorder point of demand spikes; or (2) on-line: recorded immediately (always up to date)
poisson distribution - Count the number of defects, can't calculate percentage since do not know gran total, examples: complaints, cracks, breaks, scratches, calls
Benefits of JIT
1) Reduced inventory, 2) Improved quality, 3) Lower costs, 4) reduced space requirements
Deming Prize
National Total Quality from Japan - main focus on statistical control
Mean Chart
X bar chart - searches for a shift in mean/average of process - variation is still fine, but average fill of bottle increased
When looking at a control chart, what does one look for to determine if a process is in control?
Most data points near centerline, no patterns, random distribution, no points outside limits, approx. the same number of points above and below the center line
Quality of Design
Consumer's view - combination of price and the fitness of use (does it work/meet your standards)
Type II Error
Think process is in control when it isn't - Consumer's Risk -
Total Quality Management (TQM)
philosohy involving whole organization to continually improve quality and achieve customer satisfaction. 3 key philosphies: (1) continuous improvement (2)involvement of everyone, (3) customer satisfaction
2 Measures of Quality
1) Control Charts 2) Run Charts
Pareto Diagram
Ranks defects from most frequent to least frequent using bar charts - highlights where major defects and problems coming from, sets priorities - 80% of your problems come from just 20% of the issues --> need to focus on the 20% (fix)
External failure costs
Customer complaints, returns, warranty claims, product liability costs, lost sales
Genichi Taguchi
need product uniformity, product + process quality of design - minimze variation: every product is the same
Philosophy: constancy of purpose, continual improvement, profound knowledge
mistake proofing, device to prevent a defect - quality at the source, looks to prevent defect
Who defines quality?
The customer
workers, not inspectors are responsible for quality
SPC Data Types
1) Variable Data (Range Chart and Mean Chart) 2) Attribute Data
Process capability
The range where product can reasonably be produced within the design specifications with only natural variation or nonsignificant assignable variation - also need trained operator and correct materials - Range of natural variation of a process as determined by common causes - measured by the proportion of output that can be produced within the design specifications
Internal failure costs
Scrap: discard, Rework: fixing, Proces failure (why), Downtime (replace, repair), Price Reductions from poor quality
Process Control Errors
Type I, Type II
Run Chart
Shows results in order of manufacture - not randomly selected - detects abnormal patterns, process stability, dynamic view of process - less scientific and less useful than control chart
Control Chart
Help improve system, monitor (ensure) quality, achieve uniformity, avoid over or under adjustments - Need random pattern within limits (process stable), need no abnormal patterns (no runs, no trends, no periodicity, no hugging) - outliers and some patterns indicate a special cause (local) problem, its managers top priority to improve system
Variable Data
Measured, variable, continuous data, like the weight of a cereal box - other examples: lengths of wood, fill a bottle, MEASUREMENTS - It is more difficult to obtain variable data, Uses smaller (as little as 4 or 5 times) sample sizes

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