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Recently the editors of Computerworld laid out a nice overview of all the new features in Microsoft’s Excel 2016 – a cheat sheet of sorts in giving readers a quick summary of What’s New for 2016.

These new features are available to users of both the standalone Excel version and the one incorporated into their newer Office 365 suite subscription.  We’ll provide the link to the full article at the end of this post, so you can review it yourself.  Meanwhile, a quick summary:

  • The Tell Me feature makes Excel simpler to use by letting you tell Excel what you want to do – say, create a Pivot Table – and get instant direction with shortcuts on doing what you requested, so you can start building that table right away.
  • Smart Lookup lets you do research online in the background while you’re working on your sheet. It uses Bing to do a web search on words at places like Wikipedia, and lets you “define” words or further “explore” as well.
  • New chart types:
    • Histograms – for statistical analysis
    • Waterfalls – for showing running financial totals, say from gross revs to net income
    • Hierarchical Treemaps – to help find patterns in data
    • Sunbursts – for showing graphical relationships between categories and their sub (and sub-sub-) categories
    • Pareto (80/20) – a “sorted histogram” that shows both bars and lines to better display cumulative totals of percentages (you could see, say, primary machine “downtime reasons” at a glance)
    • Box & Whisper – for deeper drilldowns than a histogram to see frequencies within data sets.
  • Improved Collaboration for users of the online version (only). A better way to share spreadsheets and see who has made what changes and when.  You can do some “simpler sharing” with the desktop version, with some limitations, to allow changes by more than one user at a time.
  • Quick Analysis is a new feature that lets you quickly highlight select cells and then click a little lightning bolt icon to perform instant analysis (for Greater Than purposes, numerical averages, or a chart on the fly).
  • Forecast Sheet lets you build forecasts based on historical data if you are working with time-based historical data in your worksheets.
  • Get & Define (formerly Power Query) is a BI tool that lets you import, combine and shape data from a variety of local and cloud sources.
  • 3D Maps allow you to plot geographic and other information on a map. You’ll need of course suitable data and to prepare it for 3D Maps.

As a bonus, Computerworld provides a nifty 2016 Ribbon Reference Guide and 2016 listing of Keyboard Shortcuts at their site as well.  You’ll find it all here: http://www.computerworld.com/article/3193992/desktop-apps/excel-2016-cheat-sheet.html?cid=cw_nlt_computerworld_microsoft_2017-05-16

 

Randall Schaefer is an APICS CPIM (retired) consultant who recently published an article entitled “Cycle Counting Is Not a Guessing Game” for APICS Magazine.  In it, he describes a few scenarios that lead to common errors in cycle counts, such as only counting negative on-hand balances, counting after a production run when quantities are likely to be low, or counting only when the MRP system says to order more inventory.

Schaefer then reminds readers of “the right way to count” which we’ve reprised from his article below.

Since cycle counting is critical to effective inventory management, and hence to reliable MRP (material requirements planning), we thought it would be useful to remind readers of those key principles.  As he notes, commitment to the principles ensures accurate counts of inventory.

  • First, items must be counted at a predetermined frequency
  • Second, cycle counts should be performed more frequently for high-value or fast-moving items than low-value or slow-moving items.
  • Third, the primary purpose of cycle counting is to identify items in error in order to trigger research, identification and elimination of the causes.
  • Fourth, there are two types of cycle counts:

The first is a parts-based system, which counts every location on record to get a total. This is then compared to the stated inventory.  If a part ends up in a location not on record, it is effectively lost forever.

The second type of cycle count is location-based.  Here, every storage location is visited when its turn comes up.  The parts found are counted and compared with the inventory records.  This method eventually finds parts lost to the parts-based system.  As Schaefer points out, “the experienced supply chain management professional understands that world-class cycle counting requires both.

About twenty years ago American manufacturers began changing the face of the supply chain when they began “offshoring” — moving production to cheaper sources in foreign nations, expanding a national supply chain into a global one.  Fast forward 15 years or so, and a fair portion of that production has been seen returning home once again due largely to quality issues, but also to the increasing cost of much of that foreign manufacturing, once things like travel, shipping, duty fees, vendor due diligence, theft and piracy and a host of other issues are factored in.

Today, the opportunities and challenges afforded by 3D printing are beginning to create still another new form of “reshoring” that is poised to challenge everything we know about manufacturing.  Since its first patent was issued over 30 years ago, 3D printing is quickly becoming ubiquitous.  Its capabilities are now way beyond the prototype state, and the variety of materials that can be used has grown exponentially, notes John Collins, an APICS CFPIM, and Erick Jack dean of the Collat School of Business in a recent article for APICS Magazine.

Then there’s the recent article in The Economist (“A Third Industrial Revolution”), noting that the digitization of manufacturing is as significant as the mechanization of the textile industry and the introduction of mass production in the automotive sector.  “The ability to produce smaller batches of items tailored to specific customer needs at significantly lower costs could make the factory of the future look more like the weaver’s cottage than Ford’s assembly line.”  And that’s not to mention what this development implies for the changing skill sets of today factory workers as design and programming grow in emphasis.

Some are predicting that global logistics efforts will be reduced as manufacturers shift more of the capabilities and production back to their home shores to take advantage of customer and market proximity.  According to the PLS Logistics Blog, “part of the supply chain will become superfluous.”

When you think about, it makes sense if manufacturers can deliver small batches of customized products and prototypes.  It makes for leaner inventories, for one.  It increases the ability to respond more quickly to customer requests.  Manufacturers may be able to respond to orders directly from factories, thus eliminating some distribution elements.  Locations of stock might be consolidated, and transportation routes are likely to contract as smaller manufacturing locations provide more local 3D printing.

Collins and Jack ask whether it “might even be possible that 3D printing will supersede the concepts of nearshoring and reshoring.  After all,” they state, “where a manufacturing facility is located won’t matter much if customers can ‘deliver’ the products they purchase at home via a personal 3D printer.”

The supply chain of manufacturing has long been all about “speed, cost, quality and flexibility.”  3D printing provides both challenges and opportunities in all these.  Creating ways to incorporate new technologies like 3D printing into our processes provides plenty of both – including the opportunity to be an innovator while remaining competitive in a changing supply chain landscape.

In our prior post, we pointed out that China is on the verge of becoming the world leader in the production, sale and implementation of robots, with a stated goal of producing at least half the nation’s own robots for manufacturing by 2025.  The takeaway from that view, outlined recently in Bloomberg BusinessWeek might be that the world has much to fear from the ascendance of this wave of Chinese bots.

But a recent counterpoint to such a robot apocalypse offered by Greg Ip of the Wall Street Journal suggests that in fact, robots aren’t destroying enough jobs, fast enough.

In short, Ip points out that by enabling society to produce more with the same workers, automation like robots becomes a major driver of rising standards of living – in effect, a productivity boost.  While some say that “this time is different” because the technological change is so profound they fear that millions of workers will be out of work or at best consigned to more menial tasks… Ip says the evidence shows we’re moving in exactly the opposite direction.

He notes that while the U.S. “has many problems, job creation isn’t one of them.”  Job creation has averaged 185,000 per month this year and unemployment is down to a ten year low.  Wage gains are even up, slightly.  Ip says that “if automation were rapidly displacing workers the productivity of the remaining workers ought to be growing rapidly.”  Instead, worker output per hour has been dismal in most sectors, including manufacturing.

When slow-growing occupations are compared to fast-growing ones in data going back to 1850 (a proxy for job creation and destruction driven by ‘technology’), they find that churn relative to total employment today is the lowest on record.

Ip’s point is that the past was, in fact, much more ‘convulsive’ than today’s job churn.  American consumption he notes is gravitating toward goods and services whose production is not easily automated.  Societies increasingly are devoting “a growing share of their income to consumption in sectors where productivity [is] stagnant.”  The idea is that robots can replace fewer things that go into GDP than we think.

As examples he cites medical breakthroughs in new, more expensive treatments rather than cheaper existing treatments, and that child-care work has soared because parents won’t leave kids in the care of a robot.  Over the past decade, “low productivity sectors” including education, health care, social assistance, leisure and hospitality have added nearly 7 million jobs, whereas information and finance, where value added per worker is 5 to 10 times higher, have cut or barely added jobs.

His conclusion: We need a change in priorities.  Instead of worrying about robots destroying jobs, we need to use them more, especially in low-productivity sectors.  While robots may one day replace truck drivers, “it’s more urgent to make existing drivers, now in short supply, more efficient,” and to be more concerned about reducing the labor, and thus the cost of energy, rather than worry about jobs added in areas like solar power.  The alternative, notes Ip, “is a tightening labor market that forces companies to pay ever higher wages that must be passed on as inflation.  And that, he notes, “is a more imminent threat than an army of androids.”

 

Industrial automation continues to progress, and nowhere is that happening at a faster clip now than in China.

Robots are making rapid headway in many plants around the world.  Currently South Korea leads in adoption with some 531 robots per 10,000 workers in 2015, followed by Germany at 301, and the U.S. at 176.  At 49 per 10,000, China lags, but is determined to catch up and surpass other nations.  For the first time, in 2013 more robots were sold in China than anywhere else in the world.  Last year, 90,000 robots were installed in China, fully one-third of the world’s total – all in an accelerating effort to counter higher labor costs.

The same fervor that made China a leader in solar panels and high speed rail is now embraced by its planners in the adoption of robots in factories.  And everyone should be concerned.

China has an aging workforce and increasing labor costs, and industrial automation there is crucial.  But as John Roemisch, a VP at robot manufacturer Fanuc America Corp. says, “There’s nothing keeping them from coming after our market.”  As the CEO of IRobot adds, “China has a great history of being an effective fast follower… The question will be, can they innovate?”  To that end, China has three enormous advantages: scale, growth momentum and money, according to a May 7th article in Bloomberg BusinessWeek.

Through a sweeping proposal released in China dubbed “Made in China 2025,” Beijing will focus on automating key sectors of the economy that include car manufacturing, electronics, appliances, logistics and food production.  At the same time, they plan to increase their own production of robots to over 50% of total Chinese sales by 2020.

A Chinese start-up named E-Deodar has developed proprietary technology that allows it to create $15,000 factory robots, a cost about one-third cheaper than foreign ones.  It has mastered technology for servomotors, drivers and control panels to gain a proprietary competitive advantage, and is said to act much like a Silicon Valley startup.  Says it’s General Manager Max Chu, “People ask me, how long can you make robots?” he says. “I say it’s simple, we will make robots until there’s no more people in factories.”

The U.S. is not sitting idle.  We’ve all seen the Roomba vacuums that promise to make domestic life a bit easier.  China and others nations are now collaborating on building them for the global market.   But while building vacuum cleaners is one thing — the industrial goal is here is much bigger.  Amazon, for instance, hopes to build logistics systems that create near-humanless warehouses with packages delivered by drones or driverless vehicles.  JD.com is rushing to automate its business in the quest to replace tens of thousands of warehouse workers and deliverymen.  It’s currently testing drones to deliver packages in rural regions and experimenting with robots to deliver on college campuses, according to Bloomberg.

As its Chief Technology Officer said recently, it’s all about “who can learn… who can get better faster.  We are all just starting out.”

But while this might be the most disconcerting part of all for today’s low-skill worker, an interesting — and completely opposite — counterpoint has been voiced by Wall Street Journal editor Greg Ip.  We’ll provide Ip’s counterpoint in our next and concluding post on the ascendance of robots. Stay tuned…

 

As most of us have noticed by now, the pace of technology – long proceeding at a snail’s pace as generation after generation lived more or less as their parents had – has been accelerating at what to many feels like a breakneck rate.  We’ve gone from linear progress to exponential, moving from the industrial revolution to the current digital one at an ever-quickening pace.

Moore’s Law, now over 50 years old, postulated that the number of transistors per square inch on a circuit board would double every year or two since – and today, that continued pace means that exotic technologies that include AI (artificial intelligence), robots, cloud computing and 3-D printing systems are proliferating, evolving in many cases faster than we humans can keep up.  It seems like things keep getting faster, smaller and smarter.

And therein lies the downside of all this technical innovation, says Gary Smith, a logistics expert with the New York City Transit in a recent issue of APICS Magazine.  Smith believes that “the rate of technological change exceeds the rate at which we can absorb, understand and accept it.”  This is acutely true in the world of supply chain, with its deep reach into manufacturing, distribution and just business in general.

Most importantly he notes that “disruptive technologies require a workforce that adapts to new processes, new ways of learning and training systems.”  In that spirit, he suggests key considerations and qualities that are going to be important within supply chains of the future, ones that the next generation workforce can expect to have to incorporate into their work patterns.  Among them:

  • Data analysis and database development skills. The ability to analyze and produce actionable results from data using logic and fact with insightful opinions and interpretation of available data will be critical.
  • Critical thinking. It’s vital to data analysis and fact-based decision making.  The ability to quickly acquire knowledge and break it down into its logical components, and then analyze and drill for accurate and actionable conclusions matters.  You have to be able to take complex situations and break them down into their component tasks.  Or as Franklin Covey would say, “start with the end in mind.”  Critical thinking means “abstraction, systems thinking, experimentation and collaboration,” notes Gary Smith.  To wit:
  • Abstraction. The ability to discover patterns in data.  Often, lessons from one industry can be applied to another, for example.
  • Systems thinking. That is, viewing issues as a part of the whole – how issues relate to the rest of a system.  Often, the “good of the many outweighs the good of the few.”
  • Experimentation. Complex problems require trial and error, testing and experimentation.  It’s okay to fail, as that’s part of learning.  Fail fast, think differently and learn to adapt as new conditions present themselves.
  • Collaboration. It’s working with others toward the common goal.  Easier said than done.  It requires team-building and facilitation skills, along with everyone keeping their eyes on the prize.  Collaboration is particularly important in supply chain and ERP work, where silos need to be broken down and people need to cooperate and effectively communicate.

These are the critical skills companies will be looking for.  We see the need for it every day in ours, and we’re only one of many.  So in a very real sense, the future really is now.

In our prior post (“The Evolving Promise of Unbreakable Computer Security”) we suggested that the evolution of quantum computing would make it possible to create virtually unbreakable computer security due to its ability to create almost absurdly difficult encryption through the use of complex prime number “decompositions” that even today’s super-computers could not solve.

As a counterpoint today we offer the opinions of editors at The Economist, offered in the April 8, 2017 issue, in which they suggest that “computers will never be secure,” and that “to manage the risks, [we need to] look to economics rather than technology.”

We’ll let you be the judge as to which opinion might ultimately prevail.

The Economist editors suggest that “computer security” itself is a contradiction in terms.  Hardly a day passes that we don’t read about the latest cyber-attack (we’ve helped several of our ERP clients after they were harassed or held hostage through ransomware attacks).  Recently the central bank of Bangladesh lost $81 million… Yahoo almost torpedoed its sale to Verizon due to massive data breaches… and allegations persist about Russian hacking of the U.S. elections.

Today, there is a huge black market for data theft and extortion tools, including hackers for hire.  And soon enough, the Internet of Things (of which we’ve written frequently) will present even more devices that never expected to be hacked, but are ripe for attack.  The bottom line is “there is no way to make computers completely safe.  Software is hugely complex.”

And perhaps common sense would dictate that when you have millions of lines of code, like Google or Microsoft, errors are inevitable.  The Economist states that “the average program has 14 separate vulnerabilities, each a potential point of illicit entry.”  And after all, we are reminded, there’s the internet, where security was pretty much an afterthought.

So, what to do?  According to the Economist’s editors, it’s all about managing the risk.  Their suggestions include:

  • Start with regulation. If you can’t weaken encryption for just the bad guys, then make sure encryption is strong for everyone.  “The same protection that guards messages in WhatsApp also guards bank transactions and online identities.”
  • Set basic product regulations. They suggest promoting “public health” for computing, with solutions ranging from “internet-connected gizmos” that are updated when flaws are found, to forcing users to change passwords and user names often.  Enforce reporting laws already in place that make companies disclose when they are hacked.
  • Overall, says The Economist, incentives to take security seriously are weak, and the long-established disclaiming of liability by providers may soon bump up against traditional protection and liability laws, especially where computer products become embedded in devices traditionally protected by established liability law. In other words, the courts may one day force the liability issue.  And there’s nothing like the government to come down hard with new rules.
  • Cyber liability insurance. It’s a small but growing market for protecting consumers.  Product companies may soon find buying it preferable to the destructive consequences they might otherwise assume in liability cases.

Finally, they note that when the internet was new, no one took security seriously, and no one objected.  But today’s internet is ubiquitous, and not taking security seriously, given the known risks and consequences, is no longer forgivable.  As the editors conclude, “changing attitudes and behavior will require economic tools, not just technical ones.