When your subject matter experts retire or switch employers, you can use big data to protect your brand. Find out how.
Recently, I crossed paths at an airport with a Midwestern brewmaster who shared that he was ready to retire, but simply couldn't. There was no one to take his place who could brew the company's trademark recipes for beer. This is not an uncommon business problem.
Semiconductor companies report that their master materials engineers, who could work around a material shortage and still come up with an effective product, are retiring. It's creating a know-how gap that might leave the next materials shortage unsolved, since newer employees lack the know-how and experience.
If this seems like a perfect problem for analytics to solve, analytics are certainly making inroads.
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Here are some "wine and cheese" examples.
Data from IoT sensors is collected at each step in the winemaking process, from soil moisture to vineyard sampling to weight tags to case goods to sales to consumer data. It enables vineyards to monitor and deliver a quality product. It might contribute to vineyards continuing to produce great wines—even after their wine masters have retired.
In Italy, the 13th century Parmigiano Reggiano hard cheese is being preserved by deploying data analytics and sensors across 350 dairy farms to better track the cheese production cycle, and to monitor the diets of the cows that produce the milk. It might enable cheese crafters to preserve the centuries-old greatness of this cheese.
Both use cases are examples of how companies have overcome know-how loss threats. How can you guarantee that your company's know-how won't just walk out the door and jeopardize your brand and reputation?
The short answer is: You can't.
But there are ways that utilizing a combination of analytics, IoT, and machine learning techniques, along with corporate training and knowledge replacement strategies, can help.
1. Don't forget the experts
Analytics, IoT, and machine learning will never replace the know-how that decades-long experts have. Before your product experts leave the company, it is imperative for the company to extensively interview these experts about product recipes and "secret sauces" so the knowledge can be documented and moved into an analytics database. At the same time, employee "apprentices" can be trained by these experts on product formulation.
2. Use analytics
The information gained from product experts can be translated into a knowledge database that new employees use in the future as they recreate company product recipes and/or use these age-old recipes as foundation pieces for new products.
3. Use IoT
Consistency is a key ingredient for products if you want to ensure that customers get the same level of quality that they have in the past. To achieve consistent products and fulfill brand promises, the company needs a consistent and repeatable production process. IoT can help in the effort, as it measures environmental factors, ingredients and components, and production processes every step of the way during product manufacturing.
4. Use machine learning
During product formulation and manufacture, there are bound to be new elements and circumstances that arise and that can be captured through automated techniques like machine learning, which observes product formulation and execution, spots new or emerging patterns, and presents new intelligence to your product database that employees can use. An example of this is a sudden change of climate that might dictate a different approach to farming a product, or a consistent shortage of an important metal in semiconductor manufacture that might need to be modified.
5. Use predictive analytics
Your analytics should also be used to study forward-looking topics, such as the overreliance on one product formulation expert, the long-term impact of global warming, and political unrest in certain areas of the world. By understanding the risks of the future, the company is better equipped to take proactive steps necessary to avoid these risks altogether.
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