{"id":16120,"date":"2013-07-17T19:59:00","date_gmt":"2013-07-18T02:59:00","guid":{"rendered":"https:\/\/devwww.3cloudsolutions.com\/post\/improving-customer-satisfaction-through-product-reliability-2\/"},"modified":"2024-01-08T13:34:58","modified_gmt":"2024-01-08T21:34:58","slug":"improving-customer-satisfaction-through-product-reliability","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/improving-customer-satisfaction-through-product-reliability\/","title":{"rendered":"Improving Customer Satisfaction Through Product Reliability"},"content":{"rendered":"
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Technology has provided many methods to approach the question of customer satisfaction.\u00a0 For companies manufacturing products, there should be no surprise that reliability is an important factor for customers.\u00a0 However, before the customer\u2019s perception of quality can be addressed, it is beneficial to obtain accurate insight into the product reliability.<\/p>\n

<\/p>\n

\"Customer<\/p>\n

This post is not going to quantify the relationship between customer satisfaction and reliability but instead focus on the key steps to analyze a product\u2019s reliability.\u00a0 To get started, there are four steps that you can follow in determining the reliability for your product.<\/p>\n

    \n
  1. Collect data<\/li>\n
  2. Model the product life<\/li>\n
  3. Estimate the distribution parameters for the product life model<\/li>\n
  4. Estimate reliability<\/li>\n<\/ol>\n

    Collect Data<\/b><\/p>\n

    The data pertaining to failed and suspended (still working) units provides the basis for the analysis.\u00a0 The expected reliability can be gathered by running a sample of designed tests.\u00a0 In addition, collecting data on the product life of customer units will provide further insight and aid in detecting abnormal usage.\u00a0 With some planning and foresight a process can be established to capture the vital information.<\/p>\n

    Model the Product Life<\/b><\/p>\n

    This is a key step as the quality of decisions based on the estimated reliability pertain directly to the quality of the model.\u00a0 Of course the model is only as good as the data as Shane Risk explains in more detail<\/a>. \u00a0For those curious in the technical details, a common model used is the Weibull distribution as this model has the flexibility to address various characteristics of typical product failures.<\/p>\n

    Estimate the Distribution Parameters<\/b><\/p>\n

    With the model selected, the data collected should be fitted to determine the parameters for the model distribution.\u00a0 This can be done in various tools specific to statistical analysis, however the calculations can also be done in Excel or automated through a\u00a0SQL Server Integration Services\u00a0package.<\/p>\n

    Estimate Reliability<\/b><\/p>\n

    At this point the estimated reliability can be calculated and graphed.\u00a0 From here proactive decisions can be determined with greater insight and support.\u00a0 For example, the reliability graph below compares two versions of a product over the expected life in terms of cycles.<\/p>\n

    \"Reliability<\/p>\n

    Obviously, forecasting reliability does not make a more reliable product in itself.\u00a0 However, the results can guide you through questions like: Is the problem a supplier issue, customer usage issue, or an expected failure?\u00a0 How long should warranty periods last for a product?\u00a0 When should preventative maintenance occur?\u00a0 With answers to these kinds of questions the root cause behind customer satisfaction (or dissatisfaction) can be establish.<\/p>\n

    Action Items<\/b><\/p>\n

    Key action items are to identify the critical questions you need to address and the benefit from those questions being answered.\u00a0 The critical questions will guide what data is required to formulate statistically supported answers.\u00a0 Keep following this blog<\/a> for examples and further tips.\u00a0 Still have questions?\u00a0 Reach out<\/a> to us so your analytics can transform from an idea to a model.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Explore how advanced analytics assists with improving customer satisfaction through product reliability.<\/p>\n","protected":false},"author":21,"featured_media":15033,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[395,260],"tags":[319,317],"class_list":["post-16120","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-ai","category-data-ai","tag-machine-learning-ai","tag-manufacturing-industrial","topics-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/16120","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/comments?post=16120"}],"version-history":[{"count":0,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/16120\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media\/15033"}],"wp:attachment":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media?parent=16120"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/categories?post=16120"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/tags?post=16120"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}