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This week, many of us will gather around the Thanksgiving turkey to feast and give thanks. But sometimes dinner with the extended family can feel a bit chaotic: kids running around, in-laws complaining that they don’t see enough of the grandkids, grandparents giving a litany of their aches and pains, and you’re probably thinking about work.

We’re all coming from different interests, different needs, and different concerns. As a result, communication becomes complicated because everyone seems to speak a different language. But at least there’s one thing everyone generally agrees on: to find something to be thankful for – whatever it may be. Similarly, in most organizations the functional units rarely gather around “one table,” but when they do, they all seem to speak different languages based on misaligned objectives and competing goals, creating confusion and eroding profit. In fact, the impact of product proliferation on profit margin is about 18-25 percent.

Rather than looking to cut inventory, parts or employees – manufacturing companies need to find a common language – one based on actual customer buying patterns.

Download this FREE eBook on Big Data for Manufacturers here.

Let’s look at a company that manufactures ATMs. As the product line expands and more features and options are added, the costs begin to outweigh the profit. How are the functional units contributing to this (as well as suffering from it)? Consider the following examples:

  • The Supply Chain is struggling to figure out what parts are needed to meet customer demand.
  • Product Management continues to add more features without visibility into what market is buying what, where, and how.
  • The Sales team sells products that require excessive customization, but then miss lead times.

The one affliction eating at companies today is the complexity around product proliferation — It’s the single biggest determinant of increased cost and loss of efficiency through the entire supply chain.

We’ve found a way to cure this through pattern-based granular analysis of customer buying habits. Who’s buying what, with which, where? Without this insight on a granular level, you don’t know.

The ATM manufacturer discovered it was suffering long lead times and losing profit when selling into China. Through pattern-based granular analysis of customer buying patterns, it uncovered the cause. Of the 19,000 units sold, there were 162 unique configurations with only 122 units per configuration. That’s product proliferation as a direct result of limited insight into what the customers are actually buying!

How can manufacturers leverage Big Data? Download this FREE eBook on Big Data for Manufacturers here.

Another root cause of product proliferation is a reactive organization – The reactive organization is symptomatic of silo-ed functional units who have no shared understanding of what the company is (or should) actually be selling. So their metrics/goals continue to be misaligned.

Think back to the Thanksgiving dinner and consider scenario as it relates to manufacturing: product managers are talking about configurations, the sales team is talking about customers, and so on. Big Data analytics can act as a translator and reveal insights such as “a shortage of this part is going to impact this customer” or “this new feature – or lack of this feature – is going to affect our revenue this way.” This type of insight delivers the common language/shared understanding that enables the organization to satisfy demand, without increasing costs.

To read about more use cases for manufacturing, download this free eBook.

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