The Bank of England has a chart that shows whether a robot will take your job

robot jobs

The threat is real, as this chart showing the rise and fall of various jobs historically shows. Agricultural workers were replaced largely by machinery decades ago. Telephonists have only recently been replaced by software programmes. This looks like good news for accountants and hairdressers. Their unique skills are either enhanced by software (accountants) or not affected by it at all (hairdressers).

The BBC website contains a handy algorithm for calculating the probability of your job being robotised. For an accountant, the probability of vocational extinction is a whopping 95%. For a hairdresser, it is 33%. On these numbers, the accountant’s sun has truly set, but the relentless upwards ascent of the hairdresser is set to continue. For economists, like me, the magic number is 15%.

Another data analysis about jobs which will be phased out as time goes. It is an interesting analysis of historical job data. However, after I glanced through the bank report referenced in the article, I am not sure robots are the reason of the job replacement. For example, it could be replaced by cheap labor in foreign countries. The bank report shows only the jobs subject to be phased out due to technology advancement. People could just become productive. So, do not take robots too seriously!

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What’s Behind the Inventory Crisis of 2016?

The last time the inventory-to-sales ratio was this high was 2009, when we were in the throes of the Great Recession – people lost jobs, businesses closed, nobody was spending, nobody was growing.

What does it mean that inventory levels are this high in 2016? Are consumers not spending? Are we headed for another recession? Or are other forces at work?

Well, in April the Bureau of Economic Analysis reported that consumer spending experienced its biggest gain in six years. And while JPMorgan recently reported an increased probability of a recession in the next 12 months, no one’s sounding the alarm bells quite yet. Besides, inventory levels have been high since last fall.

So what else could be at work?

The Marketplace

Traditionally, a drop in consumer demand would cause a short-term build-up of inventory. But businesses would eventually compensate by cutting orders and manufacturers would produce less. But as we’ve seen, demand isn’t going down. And yet, inventory isn’t moving. Why?

One major culprit is the way consumers shop. Their expectations have changed. This is the age of Amazon Prime, Instacart, Uber and Lyft. Free shipping. In-store pick-up. 1-hour delivery. Easy exchanges and returns. Above all – convenience. If it isn’t convenient for a customer to buy something they want, they won’t buy it – or they’ll buy it somewhere else. Fulfillment has usurped the throne of customer satisfaction.

Traditional retailers have struggled because of this. As young, tech-driven start-ups bite into market with the luxury of fresh starts, traditional retailers have tried to stay competitive. One common tactic has been to keep buffer inventory on hand. Out-of-stock inventory kills customer loyalty. Not being able to fulfill quickly kills customer loyalty. But having lots of inventory doesn’t equate to efficient fulfillment. That requires having a modern, flexible supply chain. Without agility, retailers often lack the competence to satisfy customer demand, let alone fulfilling profitably.

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

“We view the Data Science Machine as a natural complement to human intelligence,” says James Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine.

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

A new and unique computer system from MIT has outperformed human intuition using its algorithms, and it’s amazing, and perhaps a little frightening: the Data Science Machine beat out over 600 human teams in finding predictive analysis.

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

Read more at Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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10 Tips For Getting Started With Global Supply Chain Risk Management Programs

In exploring AGCO’s success with implementing a global supply chain risk management (SCRM) program, we can summarize our key recommendations to other manufacturers and services oriented companies in 10 tips:

  1. Start to engage with solution providers – Try them out, start to inflict the pain of visibility on your internal stakeholders, teach your organization to act with many blinders removed and adopt a more strategic level of thinking.
  2. Solutions are in a state of flux – Early adopters will likely have to go through radical changes in their programs as this industry matures, but this is preferable to remaining on the sidelines, getting stuck deeper in the old ways.
  3. Heuristics will make a big difference over time – Both in helping to eliminate false positives and also in identifying real issues with greater precision. Aggregated metadata from your third parties, combined with other big data sets, all processed in real time, will drive a change toward solutions that not only show what your supply base looks like but also helps manage risk scenarios and develop mitigation plans of action.
  4. A picture is worth a 1,000 conference calls – Think of a map, showing all your major internal and external business relationships (manufacturing facilities, warehouses and distribution facilities, logistical paths, suppliers and their suppliers, etc.). This simple illustration can quickly rally stakeholders around a common cause.
  5. Good SCRM analysis requires good data – Don’t skimp on the prep work. You know that sooner or later you do need to get to a clean master data management understanding, as well as item level PO analysis. You also need to fully assess your key suppliers and their immediate supply base and product lifecycles. This is a good time to start on that journey.

Read more at 10 Tips For Getting Started With Global Supply Chain Risk Management Programs 

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