Category Archives: Technology

Digital Disruptors or Digital Disruptions? Part I

Kinaxis recently published a post on 8 digital disruptors that are coming soon to your supply chain. But, at least as far as SI is concerned, hopefully not too soon. While they all pose promise in theory, the reality is that it’s going to be a while before they deliver in practice. And while the doctor doesn’t like having to play the role of the grumpy old man who keeps shouting get your tech off my lawn sometimes he has to as no one else will. The reality is that some developments should stay in the world of sci-fi, at least for now. Let’s take them one by one.

Connected Home

The promise: more insights into customer demands and usage patterns

The reality: the fridge auto re-orders everything the customer buys, even if the customer only bought it to try and hates it, and all the demand signals are double what they should be … there goes your forecasts!

IoT at Retail

The promise: eliminate shelf stock-outs

The reality: the system not only pushes stock to the shelves, but triggers the inventory system to re-order at push levels, which will include one-time peaks as a result of sales and clear-outs, which will result in excess inventory being ordered (and possibly cleared-out later on to a discount seller)

In-store Robotics

The promise: improve customer service with robots

The reality: the robots drive your customers even more insane than those automated telephone systems, because they can’t be hung up on, won’t leave the customer alone, and don’t stop repeating “I don’t understand your inquiry, please repeat” … end result, lost sales, lost robots (when they are punched to bits), and lawsuits (from the customers who break their hands beating up your robots)

Crowdsourced Delivery

The promise: the gig economy delivers faster and cheaper than you ever thought possible

The reality: sometimes it works, but other times packages sit at a pickup site for a week, get damaged, or just go missing – at rates much higher than with traditional delivery services as the crowd-sourced delivery truck skips a pick-up (because it over-committed), as the Big Box Mart delivery employee tosses it in his truck, and as the thief, who signed up to the network under a false id with the overall intent of stealing high value items for sale, makes off with your goods

And yes, the doctor realizes that:

  • the re-order bug in the connected home could be fixed, or the system programmed to require user approvals for first-time re-orders, but as the system “learns” and gets good, the user will just trust it
  • the IoT Retail system could be alerted of cancelled lines, sale periods, etc. — but without flawless integration, human error will lead to exacerbated error
  • the customer service robots could be programmed to understand get lost and get lost, but there will always be an unaccounted for situation (the customer doesn’t speak an expected language, doesn’t speak at all, has a system indecipherable accent, etc.)
  • the crowdsourced delivery system could be limited to vetted partners, but isn’t that what carriers are?

None of these technologies are anywhere close to prime time and given all of the current weaknesses in supply chain software and integration between various systems with limited integration options across platforms, this is not a situation that’s going to change overnight.

Is the End of the Wild Digital West in Sight? I Hope So!

The MIT Technology Review recently published a great article on The Dark Secret at the Heart of AI which notes that decisions that are made by an AI based on deep learning cannot be explained by that AI and, more importantly, even the engineers who build these apps CAN NOT fully explain their behaviour.

The reality is that AI that is based deep learning uses artificial neural networks with hidden layers and neural networks are a collection of nodes that identify patterns using probabilistic equations whose weights change over time as similar patterns are recognized over and over again. Moreover, these systems are usually trained on very large data sets (that are much larger than a human can comprehend) and then programmed with the ability to train themselves as data is fed into them over time, leading to systems that have evolved with little or no human intervention and that have, effectively, programmed themselves.

And what these systems are doing is scary. As per the article, last year, a new self-driving car was released onto New Jersey roads (presumably, because, the developers felt it couldn’t drive any worse than the locals) that didn’t follow a single instruction provided by an engineer or programmer. Specifically, the self-driving car ran entirely on an algorithm that had taught itself to drive by watching a human do it. Ack! The whole point of AI is to develop something flawless that will prevent accidents, not create a system that mimic us error prone humans! And, as the MIT article states, what if someday it [the algorithm] did something unexpected — crashed into a tree. There’s nothing to stop the algorithm from doing so and no warning will be coming our way. If it happens, it will just happen.

And the scarier thing is that these algorithms aren’t just being used to set insurance rates, but to determine who gets insurance, who gets a loan, and who gets, or doesn’t get, parole. Wait, what? Yes, they are even used to project recidivacy rates and influence parole decisions based on data that may or may not be complete or correct. And they are likely being used to determine if you even get an interview, yet alone a job, in this new economy.

And that’s scary, because a company might reject you for something you deserved only because the computer said so, and you deserve a better explanation than that. And, fortunately for us, the European Union thinks so too. So much so that companies therein may soon be required to provide an adequate, and accurate, explanation for decisions that automated systems reach. They are considering making it a legal right for individuals to know exactly why they were accepted for, or declined, anything based on the decision of an AI system.

This will, of course, pose a problem for those companies that want to continue using deep-learning based AI systems, but the doctor thinks that is a good thing. If the system is right, we really need to understand why it is right. We can continue to use these systems to detect patterns or possibilities that we would miss otherwise, many of which will likely be correct, but we can’t make decisions based on this until we identify the [likely] reasons therefore. We have to either develop tests, that will allow us to make a decision, or use other learning systems to find the correlations that will allow us to arrive at the same decision in a deterministic, and identifiable, fashion. And if we can’t, we can’t deny people their rights on an AI’s whim, as we all know that AI’s just give us probabilities, not actualities. We cannot forget the wisdom of the great Benjamin Franklin who said that it is better 100 guilty persons should escape than that one innocent person should suffer, and if we accept the un-interrogable word of an AI, that person will suffer. In fact, many such persons will suffer — and all for not of a reason why.

So, in terms of AI, the doctor truly hopes that the EU stands up and brings us out of the wild digital west and into the modern age. Deep Learning is great, but only as a way to help us find our way out of the dark paths it can take us into and into the lighted paths we need.

We Need BlockChain, But Not for the Reasons You Think.

The biggest use for blockchain right now is to support digital currency, namely bitcoin, and secure trade of that currency. And since it has the potential to revolutionize e-payments, everyone is talking about it. But let’s face it, your employees don’t take bitcoin, your suppliers probably don’t take bitcoin, and your customers aren’t paying in bitcoin. Most of your employees want direct debit, your contractors want checks, and your suppliers probably want ACH. Bitcoin and blockchain is the furthest thing from their minds and, thus, is the furthest thing from yours.

But there is one use for block chain, and that is, simply put, the secure transfer of IOUs. What do we mean by this? About a year ago we penned a post that asked With Currencies Crazy, Is It Time to Return to Barter. In this post we asked what if there was no exchange of currency. What if it was an exchange of a raw material or service for another raw material or service, where each raw material or service came from the organization or a partner in the same country. Since the value of a product or service, adjusted for inflation, is relatively constant over time and since the relative value of one versus another is also relatively constant over time, such a contract would not be subject to rapid changes in value differences regardless of what happened in the currency markets.

Now imagine if instead of trading raw materials, you could trade IOUs and send them up and down supply chain until all of the differences could be settled within a country. You wouldn’t need to exchange raw materials with a company you might not want to, and, more importantly you definitely wouldn’t need to deal in non-native currencies. You could just settle those IOUs with in-country in-currency bank transfers, clear out the IOUs, and all would be settled.

Up until now, there has been no way to securely trade those IOUs. You had to trade payments in banks. But now, with the advent of blockchain, you can trade those IOUs simply by creating an IOU cryptocurrency specifically for keeping track of all the barters. And, if you’re not sure how to optimize the trading of IOUs, we gave you a great idea on how to do that in our post on With Currencies Crazy, Is It Time to Return to Barter — you build a special, shared, supply chain optimization model that allows all participating entries to upload their data and opt-in to in-currency barter optimization and then trade the IOUs through the new cryptocurrency and only the final imbalances in each country need to be paid. It’s the future …

Forty Five Years Ago Today

The United States of America, under the leadership of Richard Nixon, launched Landsat-1, the first satellite of what began the US’ Landsat program – the longest running program for the acquisition of satellite imagery of Earth. (We are now up to Landsat 8, launched four years ago on February 11, 2013.)

As succinctly summarized by Wikipedia, the images collected and archived at receiving stations around the world are a unique resource for global change research and applications in agriculture, cartography, geology, forestry, regional planning, surveillance and education, and can be viewed through the U.S. Geological Survey (USGS) ‘EarthExplorer‘ website. For example, the latest, Landsat 7, records data across eight spectral bands with resolutions ranging from 15 to 60 meters and a temporal resolution of 16 days.

And while Landsat 1 only had two sensors, the return beam vidicon (RBV) and a first generation multispectral scanner (MSS) that recorded, respectively, visible and near infrared photographic images and radiometric images, this was still extremely valuable imaging data where none had existed before. And without it, we’d never have Google Earth.

When Selecting Your Next Supply Management Solution Remember …

All opinions are not equal. Some are a very great deal more robust, sophisticated and well supported in logic and argument than others.
Douglas Adams

This is something that should always be kept in mind when soliciting opinions on a perspective solution for Supply Management. Consider who you are going to ask:

  • Your co-workers.
  • Your peers on a user group.
  • Vendor references.
  • Vendor representatives.
  • Analysts.
  • Bloggers.

Consider their average perspectives.

  • Co-workers: probably didn’t look under the UI covers of potential solutions because, like you, they are too busy …
  • Peers: stuck in a single world view provided to them by their vendor … and they are gonna love it or hate it …
  • Vendor References: peers who absolutely love the solution (or they wouldn’t be given to you) …
  • Vendor Reps: there to sell their solutions, so they will give you the best of theirs and the worst of their peers …
  • Analysts … will give you a reasonably fair comparative analysis of the vendors they know … which are typically the ones that made their quadrant … which are typically the biggest companies and/or their biggest customers …
  • Bloggers … who will tell you everything they know … but unless you pick the blogger who specializes in that area … it won’t be everything you need … but, with the exception of analysts, far better than the rest because they do their research on each vendor they cover …

In other words, when trying to select a solution and soliciting opinions from your internal survey, not all responses should be weighted equal. Insight from those who have done their homework should be weighted more heavily than from those who quickly assessed a UI and decided they like the Amazon-one best (even though a B2C interface may be totally unsuited for the task at hand) or from those with restricted world views (which make them experts on one vendor in the final three but not the other two).

Keep this in mind if you want to truly select the best solution.