Monthly Archives: April 2017

One Hundred and Twenty Years Ago Today

One Hundred and Twenty Years Ago Today J.J. Thomson announced his discovery of the electron at the Royal Institution in London.

Without a good understanding of electrons, which play an essential role in electricity, magnetism, and conductivity (in addition to more fundamental gravitational, electromagnetic, and weak force interactions), we would never have made the advances we made in modern technology. For example, even though, based on the work of Goldstein and Hittorf, Sir William Crookes developed the first cathode ray tube back in the 1870s without knowing what they were, it was electrons that made them work — as determined by J.J. Thomson and colleagues through experiments they conducted in 1896 (based on the work of Lorentz and Schuster).

The visual computing revolution started with the cathode ray tube, and, moreover, as there is no computing without electricity, and no electricity without electrons, without the discovery of electrons, and a good understanding of what they enabled, we wouldn’t be where we are today.

Opposites Attract

A Sourcing and Procurement Love Story.

[S] I’m M.C. Doc on the rap so mic it
[S] Here’s a little story and you’re sure to like it
[S] Swift and sly and I’m playing it cool
[S] With Procurement, ’cause buyers rule!

[S] Oh it seems we never ever agree
[S] You like catalogs
[S] And I like RFPs
[S] I take things serious
[S] And you take ’em light
[S] I analyze daily
[P] And I buy when I like

[P] Back office sayin’
[P] We ain’t gonna last
[P] Cuz I move slowly
[S] Catalogs move fast

[S] I like it quiet
[P] And I love it loud
[B] But when we get together
[B] We both can stand proud

[S] I take 2 steps forward
[P] I take 2 steps back
[B] We come together
[B] Cuz opposites attract
[S] And you know — it ain’t fiction
[P] Just a natural fact
[B] We come together
[B] Cuz opposites attract

[P] Who’d a thought we could be partners
[P] I place the orders
[S] And I cut the contracts

[S] I like it neat
[P] And I make a mess
[P] I take it easy
[S] But I get obsessed
[S] I’ve got the budget
[P] And I’m always broke
[P] I don’t like strategy
[S] My eyes on the prize

[B] Things in common
[B] Just ain’t a one
[B] But when we’re working in-sync
[B] It’s nothin’ but black ink

[S] I take 2 steps forward
[P] I take 2 steps back
[B] We come together
[B] Cuz opposites attract
[S] And you know–it ain’t fiction
[P] Just a natural fact
[B] We come together
[B] Cuz opposites attract

[Repeat Chorus]

[B] Oh ain’t it somethin’
[B] How we lasted this long
[B] Both of us
[B] Provin’ everyone wrong
[B] Don’t think we’ll ever
[B] Get our differences patched
[B] Don’t really matter
[B] Cuz we’re perfectly matched

[Repeat Chorus Twice]

[B] Nothing in common, only trust
[P] I’m like a minus …
[S] I’m like a plus
[S] One saving up,
[P] One spending down
[B] But together we’re on common ground

[P] When things go wrong we make corrections
[S] To keep things moving in the right direction
[B] Try to fight it but we’re telling you Jack
[B] Its true — this — Opposites Attract!

… but the unanswered question is, should they?

When Selecting Your Prescriptive, and Future Permissive, Analytics System …

Please remember what Aaron Levenstein, Business Professor at Baruch College, said about statistics:

Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital.

Why? Because a large number of predictive / forecasting / trending algorithms are statistics-based. While good statistics, with good sufficiently-sizeable data sets, can reach a very high, calculable, probability of accuracy a statistically high percentage of the time, if a result is only 95% likely 95% of the time, then the right answer is only obtained 95% of the time (or 19 / twenty times), and the answer is only “right” to within 95%. This means that one time out of twenty, the answer is completely wrong, and may not even be within 1%. It’s not the case that one time out of twenty the prediction is off more than 5%, it’s the case that the prediction is completely wrong.

And if these algorithms are being used to automatically conduct sourcing events and make large scale purchases on behalf of the organization, do you really want something going wrong one in twenty times, especially if an error that one time could end up costing the organization more than it saved the other nineteen times because it was primarily sourcing categories that were increasing with inflation or decreasing according to standard burn rates as demand dropped on outdated product offerings, but one such category was misidentified. If instead of identifying the category as about to be in high-demand, and about to sky-rocket in cost due to the reliance on scarce rare earth metals (that are about to get scarcer as the result of a mine closure), it identified it as low-demand, cost-continually-dropping, over the next year and chose a monthly-spot-buy auction, then costs could increase 10% month over month and a 12M category could, over the cost of a year, could actually cost 21.4M (1M + 1.1M + 1.21M …), almost double! If the savings on the other 19, similarly valued, categories was only 3%, the 5.7M the permissive analytics system saved would be dwarfed by the 9.4M loss! Dwarfed!

That’s why it’s very important to select a system that not only keeps a record of every recommendation and action, but a record of its reasoning that can be reviewed, evaluated, and overruled by a wise and experienced Sourcing professional. And, hopefully, capable of allowing the wise and experienced Sourcing professional to indicate why it was overruled and expand the knowledge model so that one in twenty eventually becomes one in fifty on the road to one in one hundred so that, over time, more and more non-critical buying and automation tasks can be put on the system, leaving the buyer to focus on high-value categories, which will always require true brain power, and not whatever vendors try to pass off as non-existent “artificial intelligence” (as there is no such thing, just very advanced machine-learning based automated reasoning).

Are We About to Enter the Age of Permissive Analytics?

Right now most of the leading analytics vendors are rolling out or considering the roll out of prescriptive analytics, which goes one step beyond predictive analytics and assigns meaning to those analytics in the form of actionable insights the organization could take in order to take advantage of the likely situation suggested by the predictive analytics.

But this won’t be the end. Once a few vendors have decent predictive analytics solutions, one vendor is going to try and get an edge and start rolling out the next generation analytics, and, in particular, permissive analytics. What are permissive analytics, you ask? Before we define them, let’s take a step back.

In the beginning, there were descriptive analytics. Solutions analyzed your spend and / or metrics and gave you clear insight into your performance.

Then there are predictive analytics. Solutions analyzed your spend and / or metrics and used time-period, statistical, or other algorithms to predict likely future spend and / or metrics based on current and historical spend / metrics and present the likely outcomes to you in order to help you make better decisions.

Predictive analytics was great as long as you knew how to interpret the data, what the available actions were, and which actions were most likely to achieve the best business outcomes given the likely future trend on the spend and / or metrics. But if you didn’t know how to interpret the data, what your options were, or how to choose the best one that was most in line with the business objectives.

The answer was, of course, prescriptive analytics, which combined the predictive analytics with expert knowledge that not only prescribed a course of action but indicated why the course of action was prescribed. For example, if the system detected rising demand within the organization and predicted rising cost due to increasing market demand, the recommendation would be to negotiate for, and lock-in supply as soon as possible using either an (optimization-backed) RFX, auction, or negotiation with incumbents, depending upon which option was best suited to the current situation.

But what if the system detected that organizational demand was falling, but market demand was falling faster, there would be a surplus of supply, and the best course of action was an immediate auction with pre-approved suppliers (which were more than sufficient to create competition and satisfy demand)? And what if the auction could be automatically configured, suppliers automatically invited, ceilings automatically set, and the auction automatically launched? What if nothing needed to be done except approve, sit back, watch, and auto-award to the lowest bidder? Why would the buyer need to do anything at all? Why shouldn’t the system just go?

If the system was set up with rules that defined behaviours that the buyer allowed the system to take automatically, then the system could auto-source on behalf of the buyer and the buying organization. The permissive analytics would not only allow the system to automate non strategic sourcing and procurement activities, but do so using leading prescriptive analytics combined with rules defined by the buying organization and the buyer. And if prescriptive analytics included a machine learning engine at the core, the system could learn buyer preferences for automated vs. manual vs. semi-automated and even suggest permissive rules (that could, for example, allow the category to be resourced annually as long as the right conditions held).

In other words, the next generation of analytics vendors are going to add machine learning, flexible and dynamic rule definition, and automation to their prescriptive analytics and the integrated sourcing platforms and take automated buying and supply chain management to the next level.

But will it be the right level? Hard to say. The odds are they’ll make significantly fewer bad choices than the average sourcing professional (as the odds will increase to 98% over time), but, unlike experienced and wise sourcing professionals, won’t detect when an event happens in left-field that totally changes the dynamics and makes a former best-practice sourcing strategy mute. They’ll detect and navigate individual black swan attacks but will have no hope of detecting a coordinated black swan volley. However, if the organization also employs risk management solutions with real time event monitoring and alerts, ties the risk management system to the automation, and forces user review of higher spend / higher risk categories put through automation, it might just work.

Time will tell.

All Aboard the M&A Train!

It seems that the M&A train, once sporadic, is now running on a regular schedule (thanks largely to Coupa and it’s 1B valuation that allowed it to raise enough cash to scoop up providers left, right and center). Is this good or bad? The answer is it all depends who you are.

Generally, when a company buys another, it does so with an objective in mind that, should the acquisition help it to complete the objective, helps the buyer and usually the set of customers that the buying company wants to satisfy. This might also include a sub-set of the acquired’s customers, which would then be helped in the process, but may also exclude a set of the acquired’s customers, which would not be help. Then there’s the acquired. Depending on the strength of the company, the goals of the management / owners on acquisition, and the alignment with the buying organization, it might be a good thing, or it might be a bad (or very bad) thing.

What do we mean? Let’s take each affected group at a high level and indicate what could be good or bad.

Buying Company

Potential Positive: New Technology

New technology offers the buying company a host of potential benefits including, but not limited to, new technology to sell its current customer base, new technology to bolt onto in a potentially new customer base, and process insights it did not have before.

Potential Negative: Dis-satisfied Customer Base

Expanding the customer base is not always a positive if the customers being acquired are not happy customers from the get-go. Even if the customers are happy, they might be unsettled by an acquisition …

Buying Company’s Customers

Potential Positive: New Technology

Not only does the buying company have new technology to sell, the existing customer base has new technology, that they might desperately need, to buy, and, moreover, they might also be able to buy at a discount because they are already spending with the vendor.

Potential Negative: Less Support

If the company acquired an unhappy customer base, all of the resources might be tasked with making the acquired customers happy because the company was acquired for those customers. This means that support for current customers would drop. And that’s not good.

Bought Company’s Customers

Potential Positive: Vendor has a bigger piggy bank

If the acquiring company has more resources, those could be spent improving the situation for the bought company’s customers. Better support, tech upgrades, more integrations, etc.

Potential Negative: Acquiring company is Mega-co

… and acquired company is mini-co, acquired only because it’s technology posed a future threat and mega-co decided the best risk mitigation was to buy mini-co when it was small and cheap with just a few customers as the acquisition cost dwarfed the potential losses to market share if mini-co succeeded in their efforts. In this case, Mega-co wouldn’t care at all about the customer base and could just ignore them completely.

Bought Company

Potential Positive: Bigger Piggy Bank

… which could be used to further the mission … but

Potential Negative: Lack of Support

… if the mission of the bought company does not match the mission of the buying company.

So what does this mean for Coupa, Trade Extensions, and their customers? the doctor knows you want to know, but the doctor will not provide his thoughts until the acquisition is complete.