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Retail service qualitymanagement
In this file, you can ref useful information about retail service quality management such...
manage supplier and factory information, supplier quality audits and assessments, risk
assessments and management, interna...
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The da...
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limit...
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto...
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guarantee...
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution o...
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Retail service quality management

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Retail service quality management

  1. 1. Retail service qualitymanagement In this file, you can ref useful information about retail service quality management such as retail service quality managementforms, tools for retail service quality management, retail service quality managementstrategies … If you need more assistant for retail service quality management, please leave your comment at the end of file. Other useful material for retail service quality management: • qualitymanagement123.com/23-free-ebooks-for-quality-management • qualitymanagement123.com/185-free-quality-management-forms • qualitymanagement123.com/free-98-ISO-9001-templates-and-forms • qualitymanagement123.com/top-84-quality-management-KPIs • qualitymanagement123.com/top-18-quality-management-job-descriptions • qualitymanagement123.com/86-quality-management-interview-questions-and-answers I. Contents of retail service quality management ================== The growing globalization, increasing customer expectations, regulatory compliance requirements, complexity of the supply chain, as well as the pressure on retailers to quickly bring new products to the market, have created a number of product quality and safety issues. These issues have manifested in numerous product recalls which have shaken consumers' trust in the ability of companies to make quality and safe products. The year 2007 was a tipping point. Widely dubbed "The Year of the Recall," 2007 saw millions of consumer products, including toys, apparel, cribs, heaters, ovens, and jewelry being recalled for defective or harmful components. Many of these products had been manufactured by trusted brands. Even if a supplier or sub-supplier is to blame for a defective product, ultimately the retailer or the brand faces the brunt of customer dissatisfaction. In response, retailers are striving to step up integrated enterprise quality management programs, to ensure process and product quality as well as to improve visibility and traceability across the supply chain. Also, an enterprise-wide quality management system will enable retailers to unify their quality processes across their global operations. Retailers can thus effectively fulfill their supplier, customer and internal quality obligations, adhere to industry standard quality management methodologies such as cGMPs and Six Sigma, as well as other quality standards, help companies make profitable decisions across the value chain, keep up with the new and complex market trends, and enhance their overall product and process quality. MetricStream Quality Management Software Solutions MetricStream offers comprehensive enterprise quality management software solutions with pre- defined templates, checklists and workflows for streamlining quality processes, and proactively detecting, monitoring, and remediating quality issues. The solution provides capabilities to
  2. 2. manage supplier and factory information, supplier quality audits and assessments, risk assessments and management, internal quality inspections, production management, testing, incident / issue management, corrective actions, employee training, product recalls, customer complaints and compliance with standards and regulations. MetricStream solution enables retailers to adopt an automated and integrated risk-based approach to quality management, and thus efficiently prioritize key areas of concern. Ultimately, retailers can achieve quality and compliance at a lower cost, considerably reduce the risk of poor quality, achieve control over the end-to-end processes, enhance visibility, improve correlation between programs, and establish coherent metrics to monitor and manage program performance. Replete with charts and metrics, the solutions allow retailers to monitor supplier performance, and product quality performance in real-time. The solutions uniquely combine the reporting of product and process problems with flexible workflows, ensuring collaboration with suppliers, internal quality teams, third-party auditors and testing labs. Retailers can thus achieve resolution of issues in quick time. Retailers can further securely store, manage, and retrieve documents pertaining to products, processes and training. Key benefits of the solutions include:  The scalable and flexible enterprise system facilitates standardization of multiple quality management methods and processes across a company and its supply chain  Helps automate internal and supplier quality processes, and streamline the management of quality issues reported by customers  Helps eliminate resource-intensive manual efforts and documentation, duplication of actions, and redundancies  Provides a unified view of the quality and compliance programs across the enterprise and the supply chain, right from the procurement stage and supplier due diligence process to distribution and customer service  Facilitates timely risk management by helping retailers manage internal processes, which capture the latest alerts and events including supplier related data and industry regulations  Enables retailers to define measurable key performance indicators for assessing performance and corrective actions  Helps remove silos, enabling multiple operational and business units spread across locations to collaborate and investigate quality issues, and also implement consistent remediation plans  Offers enhanced measuring and monitoring process as well as the right set of values and reports in real time, to accurately evaluate the performance of various business units ================== III. Quality management tools 1. Check sheet
  3. 3. The check sheet is a form (document) used to collect data in real time at the location where the data is generated. The data it captures can be quantitative or qualitative. When the information is quantitative, the check sheet is sometimes called a tally sheet. The defining characteristic of a check sheet is that data are recorded by making marks ("checks") on it. A typical check sheet is divided into regions, and marks made in different regions have different significance. Data are read by observing the location and number of marks on the sheet. Check sheets typically employ a heading that answers the Five Ws:  Who filled out the check sheet  What was collected (what each check represents, an identifying batch or lot number)  Where the collection took place (facility, room, apparatus)  When the collection took place (hour, shift, day of the week)  Why the data were collected 2. Control chart Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts, in statistical process control are tools used to determine if a manufacturing or business process is in a state of statistical control. If analysis of the control chart indicates that the process is currently under control (i.e., is stable, with variation only coming from sources common to the process), then no corrections or changes to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this will
  4. 4. result in degraded process performance.[1] A process that is stable but operating outside of desired (specification) limits (e.g., scrap rates may be in statistical control but above desired limits) needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. The control chart is one of the seven basic tools of quality control.[3] Typically control charts are used for time-series data, though they can be used for data that have logical comparability (i.e. you want to compare samples that were taken all at the same time, or the performance of different individuals), however the type of chart used to do this requires consideration. 3. Pareto chart A Pareto chart, named after Vilfredo Pareto, is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The left vertical axis is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. The right vertical axis is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. Because the reasons are in decreasing order, the cumulative function is a concave function. To take the example above, in order to lower the amount of late arrivals by 78%, it is sufficient to solve the first three issues. The purpose of the Pareto chart is to highlight the most important among a (typically large) set of factors. In quality control, it often represents the most common sources of defects, the highest occurring type of defect, or the most frequent reasons for customer complaints, and so on. Wilkinson (2006) devised an
  5. 5. algorithm for producing statistically based acceptance limits (similar to confidence intervals) for each bar in the Pareto chart. 4. Scatter plot Method A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.[2] This kind of plot is also called a scatter chart, scattergram, scatter diagram,[3] or scatter graph. A scatter plot is used when a variable exists that is under the control of the experimenter. If a parameter exists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horizontal axis. The measured or dependent variable is customarily plotted along the vertical axis. If no dependent variable exists, either type of variable can be plotted on either axis and a scatter plot will illustrate only the degree of correlation (not causation) between two variables. A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, weight would be on x axis and height would be on the y axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it suggests a positive correlation between the variables being studied. If the pattern of dots slopes from upper left to lower right, it suggests a negative correlation. A line of best fit (alternatively called 'trendline') can be drawn in order to study the correlation between the variables. An equation for the correlation between the variables can be determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear
  6. 6. regression and is guaranteed to generate a correct solution in a finite time. No universal best-fit procedure is guaranteed to generate a correct solution for arbitrary relationships. A scatter plot is also very useful when we wish to see how two comparable data sets agree with each other. In this case, an identity line, i.e., a y=x line, or an 1:1 line, is often drawn as a reference. The more the two data sets agree, the more the scatters tend to concentrate in the vicinity of the identity line; if the two data sets are numerically identical, the scatters fall on the identity line exactly. 5.Ishikawa diagram Ishikawa diagrams (also called fishbone diagrams, herringbone diagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa (1968) that show the causes of a specific event.[1][2] Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation. The categories typically include  People: Anyone involved with the process  Methods: How the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and laws  Machines: Any equipment, computers, tools, etc. required to accomplish the job  Materials: Raw materials, parts, pens, paper, etc. used to produce the final product  Measurements: Data generated from the process that are used to evaluate its quality  Environment: The conditions, such as location, time, temperature, and culture in which the process operates 6. Histogram method
  7. 7. A histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to "bin" the range of values -- that is, divide the entire range of values into a series of small intervals -- and then count how many values fall into each interval. A rectangle is drawn with height proportional to the count and width equal to the bin size, so that rectangles abut each other. A histogram may also be normalized displaying relative frequencies. It then shows the proportion of cases that fall into each of several categories, with the sum of the heights equaling 1. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and usually equal size.[2] The rectangles of a histogram are drawn so that they touch each other to indicate that the original variable is continuous.[3] III. Other topics related to Retail service quality management (pdf download) quality management systems quality management courses quality management tools iso 9001 quality management system quality management process quality management system example quality system management quality management techniques quality management standards quality management policy quality management strategy quality management books

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