Farewell to the ‘5 Whys’

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On Valentine’s Day 1867 a poor carpenter and his wife welcomed Sakichi to the Toyada family, little knowing he was to become the father of Japan’s industrial revolution. Amongst his many inventions, Toyoda started the Toyota Industries company and created a concept he called the ‘5 Whys’.

The 5 Whys identifies a problem’s root cause by repeating the question ‘why?’. By the fifth time, the issue is usually found and a solution suggests itself. It was a foundation of the Toyota Motor Corporation, renowned for its efficient production lines and is incorporated into ‘Six Sigma’, a set of techniques and tools for process improvement.

The demise of the ‘5 Whys’

Today sees the demise of the ‘5 Whys’. Why? Because IBM and Cisco have joined forces to eliminate issues before they even happen – everywhere. They’re doing this by bring cognitive computing to the ‘edge of the network’. In practical terms, that means that companies can now analyse readings of all of their sensors, wherever they are in the world, immediately and with no need to move the information around.

Insight in the most remote locations

If you’re a a communications company like Bell Canada you can enable monitoring in the most far flung locations. You can keep track of fleet services spread far and wide. No need to ask ‘the 5 whys’ when a link in your distribution chain breaks, your sensors will already have alerted you to a potential weakness which you’ll have fixed.

Combining data gets ahead of degradation

If you’re Port of Cartegena on Colombia’s Caribbean coast you can spot patterns in the data from your cranes, trucks and trains, like vibrations, temperature and speeds and you’ll be able to get ahead of equipment degradation and schedule vital maintenance.

The ‘5 Whys’ has been a pillar of issue resolution, will you mourn the demise of the 5 Whys or embrace the IoT’s predictive capabilities? Find out more about how IBM and Cisco are making the best analytics anywhere, available everywhere, or join in the discussion below.

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