Growing Business through Warranty Management of Manufactured Products

For most professionals in “For profit” companies, the goal is to work on initiatives that either grow Sales or reduce cost or both. Growing a business requires stakeholders of the business to understand the drivers of the business. For many manufacturers, one of the significant drivers of business is “Product Warranty”. Besides impacting costs negatively, warranty if considered high, can and will negatively impact customer satisfaction. Which if left unaddressed will cause loss of customer(s). On the contrary, when the warranty is understood and managed with an eye towards results, a business can grow with a constant focus on improving warranty and therefore “customer satisfaction”.

This article will deal with identifying the process by which warranty can be connected to cost and customer satisfaction in an actionable manner. This will then identify “where to work”. When the right actions are taken at the right time with the right resources, the initiatives will reduce warranty impact to the organization. It is not unusual for an application of this process to obtain a “double digit” percentage improvement in warranty.

Understanding the impact of warranty, will require going in deeper into the data already available in the organization. This data needs to be understood and analyzed thoroughly. In many cases all the data/information is available within the ERP system. Sometimes the “Service data” is held in a separate “Service management” software system. This data in aggregate is the base “data set” required to conduct warranty analysis with.

Examples of needed Product “Manufacturing data” include : Date of manufacture, serial number, product type, product model number, component serial numbers, production line #, shift etc. Examples of “Service data” include : Serial number, date of service, servicer name, problem found, parts replaced, location of problem, servicer name, servicer location, time to get to service, time to resolve problem, etc. In most instances, the serial number connects the product between manufacture and service. For this reason, when you call in for “service warranty” on your product, the customer service representative is most interested in the serial number of your product. On most occasions the customer service rep will ask for the model number and the serial number of the product. This step begins the diagnosis process. ?

Tools to analyze data: This data set in aggregate can be analyzed on Excel if simple enough. A bit more complex and/or voluminous data might require the use of a more powerful tool such as Microsoft Power BI. I have found this tool to work well in several instances. More complex data may require more powerful “Data Science” tools.

Here are the process steps as I see them:

1.?????? Develop aggregated data set and apply appropriate data analysis tool. My preference continues to be Microsoft Power BI.

2.?????? Analyzing the data: The data needs to be analyzed for trends in terms direction, velocity and acceleration. Understanding the control limits of these values will help identify which areas need attention and when. The analysis will consist of finding the larger or more impactful “cost & warranty” drivers. ?

3.?????? Data Validation: As the information developed is assessed, one needs to ask the question: “Does the data make sense”? Additionally, one must test the data in various ways/circumstances to make sure that one can rely on the analysis being conducted. Often, when the errors are identified, adjustments must be made to be able to get reliable analysis from these tools.

4.?????? Draw takeaways from analyses: These analyses once validated, need to be assessed with a critical and trained eye. This assessment can be done by a “Subject Matter Expert” (SME) or a team of relevant experts in the organization. The assessment will begin to identify the areas of “where to work” aka “Defines the Problem”.

5.?????? Prioritizing “Where to Work”: The functional leaders are best at taking the information available and prioritizing which of the initiatives need to be resourced. Here is one way, I have looked at the information to develop priority.

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Frequency of analysis: It is recommended that this “aggregated data” be analyzed on a regular basis. Depending on the product and business complexity, my recommendation is to conduct this analysis on a quarterly if not a monthly basis.

The organization can then deploy appropriate resources at the right time to resolve these issues in a prioritized manner. Addressing the issues proactively has the potential to prevent disruptions to business for both the customer as well as the organization. Thus, improving customer satisfaction in the organization’s products.

A further embellishment of this process can include applying Artificial Intelligence (AI) to the assessment process in the back end, to further speed up the assessment. Additionally, it can eliminate the need for an expert to be available to understand and interpret the data. This is an area I am researching on how best to apply AI to this data set. Paraphrasing the words of an expert in the AI field and my friend “Laks Srinivasan”, Co-Founder and Managing Director at Return on AI Institute - The good news is that today in the world of AI, if you can imagine it, it is likely doable with the right resources with the right data set. ?At the same time, it is important to note that applying AI, without data validation can provide misleading answers.

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Chinmoy Banerjee

September 22, 2023

www.chibenterprise.com

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