 Part 1. Introduction to MM4XL
 Part 2. MM4XL Tools
 1. Strategic Tools
 BCG Matrix
 Brand Mapping
 Brand Switch
 Decision Tree
 Forecast Manager
 McKinsey Matrix
 Profile Manager
 Quality Manager
 Risk Analyst
 Risk Analyst Expert in a Few Minutes
 Introduction to Decision Analysis
 Introducing Risk Analyst with an example
 1. How to run Risk Analyst
 2. Simulation Never heard of it
 3. Examples
 4. Functions
 1. Property Functions
 2. Utility Functions
 3. Distribution Functions
 mmBETA (Scale, Shape)
 mmBETAGEN (Scale, Shape, [Optional: Lower], [Optional: Upper])
 mmBINOMIAL (Trials, Successes)
 mmCHI2 (Degrees)
 mmDISCRETE (InputRange, Probabilities)
 mmERF (Mean)
 mmERLANG (Scale, Shape)
 mmEXPON (Mean)
 mmEXTVAL (ModalValue, StDeviation)
 mmGAMMA (Scale, Shape)
 mmGAUSSINV (Mean, Scale)
 mmGEO (Trials)
 mmHYPERGEO (Sample, Defects, BatchSize)
 mmINTUNI (Lower, Upper)
 mmLOGISTIC (Mean, StDeviation)
 mmLOGNORMAL (Mean, StDeviation)
 mmNEGBIN (Failures, Successes)
 mmNORMAL (Mean, StDeviation)
 mmPARETO (Location, ModalValue)
 mmPARETO2 (Location, ModalValue)
 mmPERT (Lower, ModalValue, Upper)
 mmPOISSON (Mean)
 mmRANDBETWEEN (Lower, Upper)
 mmRAYLEIGH (ModalValue)
 mmSTUDENT (Degrees)
 mmTRI (Lower, ModalValue, Upper)
 mmUNIFORM (Lower, Upper)
 mmWEIBULL (Life, Shape)
 Probability functions
 Technicalities
 Sources
 2. Analytical Tools
 Business Formulas
 mmBASS, Bass Diffusion Model
 mmBEI, Brand Equity Index
 mmBEP, BreakEven Point
 mmBEPR, BreakEven Point with Fixed Rate of Return
 mmBUYRATE, Purchase Rate Model
 mmCAGR, Compound Annual Growth
 mmCHIp, Chi Squared Test
 mmCODING, Coding of variables
 1. Customer Satisfaction
 2. Database Functions
 mmDHMS, Number to Time
 mmEI, Evolution Index
 mmEXPECT, Expected values
 3. Forecast Errors
 mmGROWTH
 mmGROWTHBACK
 mmGRP, Gross Rating Points
 mmHERF, Herfindahl Index
 mmINTERPOLE, Linear Interpolation
 mmLEARN, Learning Curve
 mmMSAR, Market Share to Advertising Ratio
 4. Opportunity Index
 5. Performance Ranking
 6. Project Management
 mmPREMIUM, Price Premium
 mmPRESS, Product Performance Index
 7. Price Indexes
 8. Queuing Theory
 mmRANGE
 mmREBUY, Repeat Purchase Rate
 mmREBUYS, Estimated Number of RePurchases
 mmRELATIVE
 mmSAMPLE, Sample Size
 mmSAMPLEMIN, Minimum Sample for Significant Values
 mmSEASON, Seasonality Indexes
 mmSHARE
 mmSIGNIF, Significance Test
 mmVARc, Coefficient of Variation
 Cluster Analysis
 CrossTab
 Descriptive Analyst
 Gravitation Analysis
 Proportion Analyst
 Sample Manager
 Segmentation Tree
 Variation Analyst
 3. Charts and Maps
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 Part 1. Introduction to MM4XL
 Part 2. MM4XL Tools
 1. Strategic Tools
 BCG Matrix
 Brand Mapping
 Brand Switch
 Decision Tree
 Forecast Manager
 McKinsey Matrix
 Profile Manager
 Quality Manager
 Risk Analyst
 1. How to run Risk Analyst
 2. Simulation Never heard of it
 3. Examples
 4. Functions
 1. Property Functions
 2. Utility Functions
 3. Distribution Functions
 mmBETA (Scale, Shape)
 mmBETAGEN (Scale, Shape, [Optional: Lower], [Optional: Upper])
 mmBINOMIAL (Trials, Successes)
 mmCHI2 (Degrees)
 mmDISCRETE (InputRange, Probabilities)
 mmERF (Mean)
 mmERLANG (Scale, Shape)
 mmEXPON (Mean)
 mmEXTVAL (ModalValue, StDeviation)
 mmGAMMA (Scale, Shape)
 mmGAUSSINV (Mean, Scale)
 mmGEO (Trials)
 mmHYPERGEO (Sample, Defects, BatchSize)
 mmINTUNI (Lower, Upper)
 mmLOGISTIC (Mean, StDeviation)
 mmLOGNORMAL (Mean, StDeviation)
 mmNEGBIN (Failures, Successes)
 mmNORMAL (Mean, StDeviation)
 mmPARETO (Location, ModalValue)
 mmPARETO2 (Location, ModalValue)
 mmPERT (Lower, ModalValue, Upper)
 mmPOISSON (Mean)
 mmRANDBETWEEN (Lower, Upper)
 mmRAYLEIGH (ModalValue)
 mmSTUDENT (Degrees)
 mmTRI (Lower, ModalValue, Upper)
 mmUNIFORM (Lower, Upper)
 mmWEIBULL (Life, Shape)
 Probability functions
 Risk Analyst Expert in a Few Minutes
 Introduction to Decision Analysis
 Introducing Risk Analyst with an example
 Technicalities
 Sources
 2. Analytical Tools
 Business Formulas
 1. Customer Satisfaction
 2. Database Functions
 3. Forecast Errors
 4. Opportunity Index
 5. Performance Ranking
 6. Project Management
 7. Price Indexes
 8. Queuing Theory
 mmBASS, Bass Diffusion Model
 mmBEI, Brand Equity Index
 mmBEP, BreakEven Point
 mmBEPR, BreakEven Point with Fixed Rate of Return
 mmBUYRATE, Purchase Rate Model
 mmCAGR, Compound Annual Growth
 mmCHIp, Chi Squared Test
 mmCODING, Coding of variables
 mmDHMS, Number to Time
 mmEI, Evolution Index
 mmEXPECT, Expected values
 mmGROWTH
 mmGROWTHBACK
 mmGRP, Gross Rating Points
 mmHERF, Herfindahl Index
 mmINTERPOLE, Linear Interpolation
 mmLEARN, Learning Curve
 mmMSAR, Market Share to Advertising Ratio
 mmPREMIUM, Price Premium
 mmPRESS, Product Performance Index
 mmRANGE
 mmREBUY, Repeat Purchase Rate
 mmREBUYS, Estimated Number of RePurchases
 mmRELATIVE
 mmSAMPLE, Sample Size
 mmSAMPLEMIN, Minimum Sample for Significant Values
 mmSEASON, Seasonality Indexes
 mmSHARE
 mmSIGNIF, Significance Test
 mmVARc, Coefficient of Variation
 Cluster Analysis
 CrossTab
 Descriptive Analyst
 Gravitation Analysis
 Proportion Analyst
 Sample Manager
 Segmentation Tree
 Variation Analyst
 3. Charts and Maps
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mytest > help > Part 2. MM4XL Tools > 1. Strategic Tools > Quality Manager > Introduction to Quality Control
Quality Manager Introduction to Quality Control In a world crowded with products of every kind and with customers' demands, we need to pay attention to improving what is already available as opposed to developing new things. Improvement in line with customer needs and wishes requires a profound understanding of the competing environment and of the competitive ability of companies. The ability to compete can be thought as a chain of processes that take an input, add value to it, and produce an output. Often, due to a stereotyped way of thinking, when we talk of processes we tend to think of a production line. But what about sales and management processes? For instance, the time an order takes to be processed, the number of orders flowing in every day, the length of time to answer customer inquiries, the trend in gross profit over time, the trend of return on sales, the response to direct marketing campaigns, the calls to 0800 phone numbers, and the interviews of a panel or tracking survey. These are just a few of the situations that business decisionmakers can understand as a sequence of events that altogether form a process. Large processes can be broken down into components, which enable identification of the details that are causing the process to fail or succeed. The concept of improvement, or change for the better, is key to quality, and it has been effectively summarized in the now popular Japanese term Kaizen, which means continuous improvement involving everyone in the organization. Within this context, statistical quality control can help companies to increase their ability to compete effectively by improving the quality of the output they offer in the market. What is statistical quality control (SQC)? Statistical quality control (SQC) applies statistical analysis to ensure that the output, products, and services of a company satisfy the needs of the customers. The characteristics of a sample of products or one or more processes are measured in order to make decisions regarding their quality. There are two groups of analytical methods in SQC:  Statistical process control (SPC)
 Acceptance sampling (AS)
Statistical process control (SPC) SPC is a decisionmaking tool useful to ensure that processes perform within limits. When a process goes beyond limits, SPC helps to identify when the change happens and the manager can assess whether the change is good or bad. If the change is bad, action should be taken to remove the cause. If the change is good, the occurrence of the cause should be made common practice. Effective SPC requires selecting characteristics useful to measure the process, and gathering accurate measurements. There are two kinds of measures:
 Attribute characteristics are measured with counts, for instance, the number of visiting customers or incoming calls.
 Variable, or continuous, characteristics can take any number and are typically measured with devices, for instance, the weight of packaged goods or the time it takes to process an order.
Attribute characteristics can be monitored with:
 P charts and NP charts. Useful when dealing with lots, for instance, boxes containing 24 packs each or 250 bottles of shampoo made during each production cycle.
 C charts and U charts. Useful when dealing with single units, such as the number of errors on a single newspaper page or the number of customers receiving the wrong items in their orders.
Variable characteristics are monitored with:
 Xbar and Sigma (XS). Two charts used to detect changes in the average or in the amount of variation in the process.
 Xbar and Range (XR). Used in place of the XS charts when the sample size is smaller than 6.
When the characteristics of the sample do not meet the specifications it means the process is not in control. A technique called process capability analysis helps relate control limits to specification limits and find out whether the process is performing as planned or not.
Acceptance sampling (AS) AS helps ensure that the material a company receives and delivers is acceptable. The acceptance is stated according to the inspection of one or more samples taken from one or more lots. Unacceptable samples require action. In case of incoming goods, the merchandise can be sent back to the supplier. Outgoing services and products, on the other hand, call for the removal of the cause of rejection. Care must be taken that each lot contains the output of one single process only for a specific period of time. Mixing up lots and periods of measurement could prevent the analyst from identifying the source of the problem and correcting the malfunction. Samples must be drawn randomly. Three main tools are used in AS:
 Operating characteristics curve (OCC). Given a certain acceptance level, the OCC plots the probability of accepting a lot versus different levels of quality.
 Hypergeometric operating characteristics curve (HOCC), like OCC but for small lot sizes.
 Average outgoing quality (AOQ), is a chart showing the product of incoming quality times the probability of acceptance.
An alternative to AS is the inspection of 100% of all items in all lots, but this may be economically unfeasible.
Variation, source of improvement Every process is a function of 5 elements: material, methods, machines, environment, and people. Variation exists in each element and, when all are combined, generates variation in a process. Variation is divided into two groups of causes: common and special. Common variation is due to chronic causes built into the process, and it is always there. Special variation arises due to acute influences that are not commonly part of the process. The Kaizen concept of improvement aims to eliminate both common and special causes of variation. Toyota and Motorola, among many other companies, apply the Kaizen concept. In a process, however, it is not always immediately apparent what the variation arises from. A description of the variables measuring the process may shed some light, and basic statistics such as mean, median, mode, range and standard deviation are commonly used to describe the distribution of measurement. As shown in the chart above, these descriptive statistics help to indicate whether a process is producing a stable, normally distributed output, or whether it has changed to an unstable condition. When the process fails to work correctly, however, this information alone does not help to find out when the change occurred. Fortunately, the quality control charts (QCC) used in SPC can fill this gap. For more information about descriptive statistics, read also the material concerning the tool Descriptive Analyst available with MM4XL software. Besides a number of useful indices, the tool draws boxplot charts and makes the Pareto analysis (ABC curve) often used to describe variables. This makes it useful for investigating processes further. 