Category Archives: rants

Don’t blame the lawyers. Blame the bankers!

Recently, listosaur posted the list of the 10 Most Despised Professions in America. According to the list, the most hated profession in America are Members of Congress. SI has to agree with this one as anyone who would shut down a whole country for almost a month over petty party differences deserves a bad reputation, but doesn’t agree with Lawyers being in third place while Wall Street Traders are in tenth. While even the doctor can sympathize with William Shakespeare when he said the first thing we do, let’s kill all the lawyers, the lawyers are not responsible for the current state of the global economy and are definitely not responsible for three of the top four risks as identified in the 2014 Annual Global Risks Report put out by the World Economic Forum.

The people responsible for three of the top four risks, directly and indirectly, are the bankers. According to the 2014 World Economic Forum Global Risks Report, the top four risks are:

  1. Fiscal Crises in Key Economies
  2. Structurally High Unemployment/Underemployment
  3. Water Crises
  4. Severe Income Disparity

1. Fiscal Crises in Key Economies

Many of the fiscal crises playing out around the world are the result of a stock market collapse of one kind or another. Hedge funds, (sub-prime) housing markets, commodity markets, etc. All of which are controlled, and manipulated, by bankers.

2. Structurally High Unemployment/Underemployment

What are the causes of unemployment? While we like to blame job outsourcing, technological advancement, or other trends that tend to displace jobs, the reality is that as old industries decline new industries emerge. In reality, GDP drives employment and unemployment more than other factors that usually get the blame. But the reality is, in the private sector, bankers influence employment and unemployment more than anything else. In a recession, or a slow economy, every hire cuts into profit margins and quarterly numbers, and a company is penalized by the bankers on Wall Street for every penny it is off in its quarterly earnings call. And even as the economy recovers, fearful of hiring too fast and getting penalized by Wall Street, a company will hold off on hiring as long as possible. So even though it is up to the company whether or not it will hire when it has the cash to do so, the fear the bankers install in the company is typically enough to make it hold off as long as possible. Yet again, the bankers’ manipulations of a market are putting us all at risk.

4. Severe Income Disparity

The gaps between the rich and poor are widening every day, and this is threatening social and political stability as well as economic development the world over. And who are the richest people? Typically, wealthy industrialists and bankers. Where do the industrialists keep most of their money? In banks, where it can be manipulated by the bankers to earn those wealthy industrialists even more money. The bankers are making the rich richer and the poor poorer every day.

the doctor will be the first to admit that this is a very simplified view of the matter at hand, but the reality is that, directly and indirectly, the banks have a lot more control over the global economic situation than we should like and their greed is doing more damage and good. We don’t need to be able to measure precisely to tell good from bad. So even if you hate them (and hate them with good reason), don’t blame the lawyers. Blame the bankers. When all is said and done, the lawyers are just a royal pain the in @ss. Bankers, on the other hand …

Machine-to-Machine Networking Can Take Predictive Analytics a Long Way

… but the day the machines can figure out that sunlight through a window is causing the machine to malfunction is the day the machines take over and kill us all!*

What brought on this rant? A recent piece over on ThomasNet on “Machine-to-Machine Networking” that said thanks to predictive analytics, BMW found that the bug wasn’t in the machinery; sunlight coming through a window in one facility was slowly heating up machinery to temperatures beyond optimum range and affecting the components being produced. While this is correct, it is misleading. The reality is that thanks to embedded sensors and M2M networking, the analytic software that powered the predictive analytic engine noticed that the core temperature in part of the machinery producing cylinders on one production line increased during the day while the core temperature of the same machinery on an identical production line (in another plant) stayed constant once the machine heated up. Since it’s generally a bad thing when a machine overheats, the software alerted the production manager that the machine was running too hot late in the day and probably needed to be serviced.

At this point, the production manager would assign an engineer to inspect the machine and run some basic tests, only to find that everything was working fine. However, armed with the data that the machine was overheating, upon finding no probable internal causes, the engineer would examine the surroundings, particularly at the time when the machine typically overheated, and notice that it was in the direct path of sunlight later in the day. Since engineers know that light is a heat source and that metal observes heat, especially when it is in the path of direct sun for hours, the engineer would conclude that at least part of the problem was the direct sunlight, shield the machine, and monitor the performance, and core temperature, for the next few days. At this point, the engineer would notice that the core temperature and production quality stayed constant and would then be able to conclude that the sunlight was the cause of the problem.

All the M2M-enabled predictive analytics package for preventive maintenance is able to determine is that something is not operating at typical performance levels, be it heat, throughput, energy usage, etc. It points you to the source of the problem, not the root cause. You’ll still need a smart engineer to figure out why the machine is overheating, why the energy usage has shot up, why the defect rate is increasing, etc. In a few cases, the software will be able to determine that a sensor is broken, a connection is down, or a part is broken when data cannot be retrieved, checksums are incorrect, or scans come back with known error types. But this is not the typical behaviour. On average, the best you’ll be able to figure out is that a part needs to be replaced, but not what’s wrong with it. In many cases, it will be cheaper to just swap out the part then to try and diagnose and fix it, so you’ll do just that and not worry about what went wrong. It’s a valid approach, as it keeps the machine up, costs down, and saves you money in the long run — but not one that helps you figure out why the part wore out. Was it a defect in the part or a problem with the machinery it’s embedded in? If the former, you don’t care what the defect was — only that the supplier replace it under warranty. If it’s a problem in your machinery (such as voltage spikes causing the part to burn out early), then you’ll (eventually) suffer multiple part failures and want to know the root cause (but you won’t be able to even suspect the machinery until you have the 3rd such failure).

the doctor is all for predictive maintenance and using sensors and machine-to-machine networking to be efficient and cost-effective about it, but wants all providers and promoters of such technology to be very clear about what it can, and can’t do. It can detect variances (and abnormal operational conditions) and correlate them to patterns that suggest potential problems and a need for part replacement, but it can’t say for certain why those problems exist. In the hands of a smart engineer, it will help to diagnose the machine or part that is source of a rare or difficult problem much faster than the engineer could track the source machine or part on her own, but it won’t be able to identify the root cause. That will still require brainpower. The machines can’t replace us yet. Remember that before you get oversold.

* Unless the machines need us to power The Matrix. The scary thing is that the day of dread may not be too far off! The NSA is building Skynet, Amazon is building autonomous drones, GE and Boeing are trying to make everything smart, and 3-D printing is at the point where we can now make primitive replicators. And everyone seems to have forgotten about Asimov’s three laws of robotics, thinking AI is still decades off when you can buy a GPU on a high-end PC graphics card that can do over 4 Trillion instructions per second. (In comparison, peak performance of the Intel 8088 processor was a mere 1 Million Instructions Per Second.) The computational power exists — all that is missing is the algorithm.

Prediction Time Again? Ugh. Part II

In yesterday’s post we noted that back in the noughts, many Sourcing and Procurement technologies were naughts when it came to delivering on their promises, and left a bad taste in the mouth of many earlier adopters, including those that can benefit a company the most. To this end, we highlighted the four examples, in no particular order, of e-Auctions, e-Invoicing, Spend Analysis, and Punch-Outs. Then we asked what are the leading Supply Management companies promoting today?

Today’s leading Supply Management companies are promoting:

e-Auctions

Sophisticated e-Auction platforms that allow multiple auction formats, weighted-bids, preliminary RFXs, custom lots, sophisticated award rules (all, at least two suppliers, pre-defined split, etc.), weighted bids, and even built-in real-time decision optimization. The platforms today are infinitely better than the platforms ten years ago, but the song still sounds the same to the CXOs who signed off on the first generation e-Auction platforms only to get burned in the noughts.

e-Invoicing
Unlike the early point-technology providers, the leading platforms are not selling a single technology point solution and promising you that one-size will fit all, they are selling an integrated platform that can accept invoices from just about any channel you get them from, in almost any format — EDI, cXML, Supplier Network, PO-flip, e-mail/fax to OCR, print-to-Cloud and/or custom dual-entry. Whereas a good e-Invoice solution in the past was successful if you managed to get 90% of the 20% of suppliers who constitute 80% of your spend on-board after a three year slog, today’s solutions can easily accommodate 98%+ of your suppliers out-of-the-box and can be rolled out globally in as little as 12 months! However, the Finance executives who tried the early entrants in the e-Invoice market ten years ago and got burned are once bitten, twice shy.

Spend Analysis

In 2005, BusIQ released BIQ, a spend analysis tool that can be used by anyone to build a spend cube on any spend dataset export(s) anyway they wanted and do so in a matter of hours (for a five figure price tag)! Introducing the concept of layered-rules and mapping by exception, it completely transformed the approach to spend analysis which was, until then, based on sophisticated OLAP cube construction (which could take days, or weeks, depending on the number of rules and the size of the spend data set) and often required teams of Indians in the back-room of your provider’s off-shore support centre frantically mapping the unclassifiable exceptions in the hope enough of your data could be mapped in time to pass the sniff test. But with BIQ, a spend master could build a cube in a few hours, create dozens of ad-hoc reports, and follow her hunches until a unique, and valuable, savings opportunity was identified. Since it only took a few hours (or days) of her time, the cost to explore a hunch dropped from tens of thousands to rebuild a cube to a few hundred dollars, and savings opportunities could be identified quickly and easily. It raised the bar, and pushed a number of providers to substantially improve their offerings while simultaneously decreasing the price. Spend Analysis is something every Supply Management department should do and a tool every Sourcing and Procurement professional should have at their disposal, but since the early adopters typically had to fork over six figures for an ROI that proved to be much less than they expected, these initial early adopters are now hesitant to take a chance on a new solution as they believe that spend analysis is still a “fool me once, shame on you but fool me twice, shame on me” buzzword.

Punch-Outs

Today’s vendors are selling complete, cloud-based, integrated consumer-like shopping portals that integrate punch-out 2.0 instances with buyer-and-supplier catalog management and third-party portals, offering consistent federated search across the punch-outs, catalogs, and portals along with buying-organization price verification, maverick spend detection, and integrated split purchase-order generation. This means that the technology integrates seamlessly, the integration is managed by your SaaS provider, if the price presented by the supplier is not the contract price the buyer is alerted in real time, purchases for off-contract products or products at off-contract prices can be prevented, or at least held until the price is corrected, and each supplier can get their own, customized, purchase order that they can flip back into an invoice (at the push of a button) once the product(s) are shipped. But organizations that got an IT migraine that was beyond their wildest expectations when they tried punch-out 1.0 early in the noughts still fear that there aren’t strong enough painkillers on the market to deal with the cluster head-aches they expect will accompany any new punch-out implementations.

So now you know why SI feels that, for the most part, 2014 will be 2013 part II and 2009 part VI. Until the early adopters get the message loud and clear that today’s technology is truly a decade ahead of its predecessor technology and leaves the problems of its predecessors in the past and until the economy forgets the financial crisis and returns to a growth- and innovation-focussed economy, and not a cost-cutting and market-retention economy, progress will continue to be a slow, up-hill battle and will be limited by the choices of the market leaders.

Prediction Time Again? Ugh. Part I

Why can’t a new year come without all of my fellow bloggers making hopeful, yet unrealistic, predictions about the upcoming year? And why can’t they stop inquiring about mine? Because, the reality is that 2014 is going to be 2013 part II, which was 2012 part II, which was in turn 2011 part II, which was in turn 2010 part II, and which was in turn 2009 part II. Supply Management, like many sectors, has yet to finish recovering since the financial crisis of 2007-2008 and there has been relatively little in the way of game-changing innovation to pull people back to the table, primarily because a lot of the best (and most innovative) solutions on the market that companies should be buying sound like the solutions they bought ten years ago — solutions which never delivered on their promises.

Back in the noughts, many Sourcing and Procurement technologies were naughts when it came to delivering on their promises, and left a bad taste in the mouth of many earlier adopters. Consider the following examples, in no particular order:

e-Auctions

Typically, e-Auctions worked great the first time when the consulting or solutions company was allowed to pick the category the solutions’ company knew would work great (based on current market conditions), but then backfired the second time. When the auction was run the first time, the supply (greatly) exceeded demand, and the buyer was able to cut a lot of fat out of the margin. But then, as the global economy was growing, by the time the buyer got back to the category, supply was constrained, the supplier’s raw material costs were rising, and there was no fat left to trim. Lucky buyers saw a cost reduction of 2% or 3% (compared to the 12% or 23% they saw in the first auction) but unlucky buyers actually saw costs increase!

e-Invoicing

In the early days, Procurement and AP Automation technology suppliers were promising to solve this problem by way of cXML, OCR, or Supplier Networks, each of which have their failings. cXML required the supplier to have a solution that was capable of delivering invoices in cXML, which, in the early days, was limited to suppliers who also used Ariba (who developed the protocol), and as this was a small percentage of the supply base, it was a dismal failure. OCR, which was, and is, still maturing, also proved to be a train-wreck as it failed miserably on poorly formatted invoices, invoices with fonts that were too small, invoices that were hand-written or that had hand-written notes, and invoices that used unrecognized abbreviations — which, combined, were the majority of invoices.

Spend Analysis

In the early days, the tools were very difficult to use, classification and cleansing was even harder, and most companies had to outsource the analysis which often costed high six figures when all was said and done. In addition, since most vendors didn’t understand the operations of the company intimately, or the many ways the different business units categorized their data, and relied heavily on simple auto-classification to speed up the project (and attempt to make it more profitable for them), the classifications were often filled with classification and categorization errors that could only be corrected by changing the rule set and rerunning all the data, which typically took the provider at least a week. And if you wanted to see the data classified (or cubed) another way for comparative purposes, forget it.

Punch-Out

The purported answer to catalog proliferation, all punch-outs did was proliferate their own set of of problems. Just like many AP departments were drowning in paper (invoices), many Procurement departments were drowning in paper (catalogs). Punch-outs were supposed to solve that problem, as all you supposedly had to do was punch-out from your shopping-cart to a punch out to find what you wanted, no catalogs needed. Well, for this to work, the supplier had to support punch-out, the supplier had to have enough technical sophistication to support multiple pricing models (and always apply your contract pricing), and your IT team had to have the technical sophistication to properly integrate your supplier’s punch-out. And then you had to rely on the supplier to actually get the contract pricing right. Did everything go right all the time? Not even close.

And what are the leading Supply Management companies promoting today? Come back tomorrow to find out.

If The Degree Is Doomed, So Is America!

The HBR blog has a lot of thought-provoking content, which is good, but also has a lot of philosophical content which, if misunderstood, or interpreted as more than a thought-experiment, could lead to very, very bad consequences. Take the recent post on The Degree is Doomed by Michael Stanton which ends with the value of a college degree has been in question since the Great Recession, but there have yet to emerge clear alternatives for the public to rally the around. There are plenty of contenders, though, and it won’t be long before one of them crystalizes the idea for the masses that the traditional degree is increasingly irrelevant in a world with immediate access to evaluative information.

Anyone who takes this literally, including the idiot software CEO I spoke with recently [who] said he avoids job candidates with advanced software engineering degrees mentioned in the post, is setting themselves, and the rest of us, up for massive failure. While I will be the first to admit that there are lots of relatively useless degrees in Academia from an industry perspective, not all degrees are useless and the mandate of a University is not to train you for a job — it’s to prepare you to think critically, solve problems, and improve yourself. This, in turn, allows you to learn the specific skills required for a job related to your field of study quickly and easily.

But this isn’t the point I want to make in this post. And while I agree with the author that the value of paper degrees lies in a common agreement to accept them as a proxy for competence and status, and that agreement is less rock solid than the higher education establishment would like to believe as many want to promise you a job in return for the considerable amount of dollars you need to fork over to get your degree, this doesn’t mean that the following notions presented in the post are true:

  1. On sites such as GitHub, user profiles contain work samples and provide community generated indicators of status and skill,
  2. Employers have never before had such easy access to specific and current information pertaining to a candidates’ potential, and
  3. The traditional degree is increasingly irrelevant in a world with immediate access to evaluative information.

SI has to disagree with all of these statements. Let’s take them one by one.

  1. Code samples a candidate presents to you through an online portfolio are not necessarily indicative of status and skill. The code samples could be someone else’s, stolen or paid for. The quality of the code could be the result of pure luck. I’ve seen developers struggle with a problem, copy fragments of code from various projects, and, through a lack of understanding of the depth of the problem, end up with a quick and dirty solution that just happens to work well in the real world, like Quicksort, due to the peculiarities of the data set. (Quicksort’s average performance as a sorting algorithm is on the same order of magnitude as its best case performance but it’s worst case is exponentially slower than it’s best case, compared to a merge or heap sort with a worst case that is essentially no worse than its best case.) And even if the code is the candidate’s and the quality is not due to pure luck, it could be based on someone else’s understanding of the problem. (A big part of development is problem solving — it’s much easier to translate an algorithm into code than to come up with one.) Plus, a single person can only do so much so there is no way to tell if the person can function well in large software projects or has the background to architect for such projects. And, to use a real-world metaphor that everyone understands, just because you can put up a shed using a do-it-yourself kit, doesn’t mean you can build an apartment building. Just because you can code a little utility library doesn’t mean you have what it takes to write scalable, reliable, adaptable, and responsive Enterprise Software.
  2. A few specific pieces of work product do not paint a full picture of a candidate’s potential. For starters, in addition to being able to create a good work product, a candidate has to work well with others, adapt to changing job requirements, learn on the job, and create new and different work products from the one he has already produced. A few static pieces of content are nowhere near sufficient to judge whether or not the candidate has anywhere close to the IQ and EQ that you need.
  3. Given our comments to 1 and 2 above, at the present time, the traditional degree is becoming increasingly relevant in a world with too much access to incomplete, non-illuminative, and unverifiable information. Provided it’s an accredited degree from an accredited institution known for high standards, it’s often the only way to judge if a candidate possesses the basic knowledge, skills, and EQ foundation that she will need to excel on the job today and tomorrow. While it is generally the case that an academic program will not teach a candidate everything she needs to know to do your job, it is the only program that will give her the foundation she needs to learn what she needs to know to do your job. You might have to give her some training, but you can be confident with the right training, she will get the job done.

I know Aerosmith told us to Dream On, but I really don’t think what the author presented in the HBR blog post is what they meant. 😉