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Managing Product Recalls – Harness the power of Data

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Product Recall – a word that could send chills down the spine for some manufacturers, spark media attention, hamper hard-earned reputation, and, no doubt, pose an unwanted financial burden. Despite stringent rules and regulations around product safety, recalls regularly make headlines.

 

While no executive wants to face a recall, how it is handled determines the actual impact on the business. If an organization appropriately reacts and adjusts its operations, it can minimize damages and enhance its brand’s reputation for transparency, honesty, and genuine customer focus. Even after the need for a recall has been identified, the costs can quickly increase according to several associated factors list below:

Identification

The seriousness of the recall needs to be established; for example, is it contained to a consignment or batch, or a larger issue? If the product batch is at a warehouse waiting to be shipped, the recall cost will be far less than if the product is already in the consumer’s hand. Speed is of the essence at this point because if the product continues along the supply chain, the issue and cost can accelerate.

Perception

The emotional component of a recall can also wreak havoc on costs. For example, China’s parents are still haunted by an incident in 2008, the melamine baby milk scandal, and killed six infants. Many parents lost their trust in domestic brands, paving the way for foreign companies.

Regulatory reporting

Certain jurisdictions mandate reporting a product-related issue if any defects become known to a manufacturer or distributor, which may necessitate a recall. Companies need to decide which media channels should be used to release the recall notice. Social media platforms have reduced the potential costs, but advertising space in newspapers, television, and radio may still be required, and costs can increase considerably. The message about the recall will need to be carefully crafted to ensure it is clear and concise while also informing consumers that the company manages the issue effectively.

Logistics

The product may need to be physically removed from outlets, supermarkets, and/or showrooms. Retailers/dealers may also need to be reimbursed for their costs and loss of trade and remove the affected product from their shops/showrooms.

The ripple effect

If a faulty product is used as a component in other products, then the recall cost will also be considerably higher. While this is most common in the automotive sector, where a faulty part can easily damage other products in proximity, which must also be paid for, other sectors see similar situations.

Business interruption

Depending on the severity of the recall, lawyers, consultants, and extra staff may be required to help manage the recall. Also, the potentially lost person-hours associated with a recall’s distraction could be significant.

Rehabilitation

While the actual product recall is likely to be incredibly costly for a client, the expense does not stop once everything is safely off the shelves. The next significant cost can be returning the company and brand to its position before the recall, which may include extra promotional expense and sales promotion offers.

Prevention is Key – what more can we do? AI and Machine Learning to the rescue!

Big Data and emerging technologies underpin a positive response to an adverse event. Data analytics and emerging technologies in the supply chain, combined with customer intelligence and product knowledge, can help companies of all sizes more nimbly mitigate the detrimental effect of recall situations while simultaneously addressing customer concerns and preventing future recalls.

We are in the era of industry 4.0, where smart and connected devices, powered by machine learning and AI, can predict faults and anomalies in the manufacturing process. Another way AI and machine learning can help the manufacturers is by analyzing the flood of manufacturing data received by machines. By analyzing this data thoroughly and looking for anomalies via machine learning, you can predict catastrophic failures earlier, avoiding total breakdown and saving businesses large amounts of revenue and brand equity. This, in turn, minimizes businesses’ need to issue recalls routinely or for consumers to suffer the potentially dangerous fallout from faulty equipment.

Imagine being able to predict something before it does, pre-empt failures, and proactively take corrective actions. This is where artificial intelligence and machine learning come into play.

The ability to create a full digital copy of an engine is achieved by creating ‘Digital Twins’, granular virtual copies of parts in the manufacturing process, which are enabled by deep learning and artificial intelligence. By creating ‘Digital Twins’, insights can be garnered to address the tiniest of issues that would otherwise be missed during a manual inspection process.

When it comes to safety issues, the sooner they are discovered, the better. The advanced data analysis can help identify the early warning signs. Using multiple databases, complaints & reviews can be tracked and researched to pinpoint patterns with specific parts and performance. By investigating potential safety concerns and developing campaigns earlier, manufacturers can perform outreach to equipment owners more effectively to protect both the public and their brands.

Text analytics platforms can empower big manufacturing companies to quickly assess their customers’ expectations, possible miscommunication issues, and the impact of the company’s actions on customer sentiment. This approach to crisis management enables businesses to seamlessly align internally and place their customers at the center of their product recall strategy.

By combining machine learning and natural language processing, an organization can begin laying the foundation for cognitive analytics or artificial intelligence—sophisticated ways to make faster and more accurate decisions down the road.

In the study, published in the Journal of the American Medical Informatics Association (JAMIA) Open, the researchers taught an existing “deep-learning” AI called Bidirectional Encoder Representation from Transformations (BERT) to predict food product recalls from Amazon reviews with about 74% accuracy. The AI also identified 20,000 reviews that suggested potentially unsafe food products that had not been investigated.

Conclusion

Putting strong measures to handle and counter the reasons that caused product recalls can help keep potential adversaries at bay. It fosters the goodwill needed to maintain strong brand affinity. When customer notification is done right, customer loyalty is not just protected; it is enhanced.

Tavant offers a map for manufacturing companies to achieve cost leadership by improving their aftermarket operational efficiency and to increase aftermarket revenue by leveraging the latest innovations in data analytics. Reach out to us at [email protected]  for a quick demo and see how Tavant’s suite of analytical tools can help your business.

References:

  1. https://www.frayneaccidentinjurylaw.com/blog/10-famous-product-recalls/
  2. https://automotiveindianews.com/machine-learning-artificial-intelligence-reduce-vehicle-recalls/
  3. https://hbr.org/1996/09/a-strategic-approach-to-managing-product-recalls
  4. https://hbswk.hbs.edu/item/the-hidden-cost-of-a-product-recall
  5. https://www.keatext.ai/en/blog/customer-experience/using-text-analytics-to-guide-product-recalls

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