Monthly Archives: December 2023

14% of Procurement Leaders Have Adequate Talent to Meet Future Needs? Bull Crap!

the doctor has to stop looking at headlines, especially those on surveys of Procurement Leaders. Because there is no way that 14% of companies have adequate talent to meet future Procurement Needs. Why? Because there’s no way that 14% of companies have adequate talent to meet current Procurement Needs. Adding this survey result from Gartner with the recent survey result from Forbes who said that 9% of companies claim to be ready to manage risks posed by AI, and the doctor is starting to wonder who they h3ll they are asking to fill out these surveys … because it’s clear that these people either have no connection to reality, are drowning so deep in despair in their job that the only way they can keep their sanity is to pretend that the future is going to be way better than it is today (even though there’s no way that can be true if the company doesn’t fix the problems it has now because problems in companies multiply faster than European Rabbits in Australia), or are higher than a kite on drugs (as that’s how they deal).

The reality is that just about every company has problems in Procurement around:

  • Tech: they don’t have enough modern Sourcing and Procurement systems, sometimes it’s because they are cheap are not-forward thinking (another problem), other times it’s because they aren’t technologically proficient enough (to even know what they need)
  • Risk: otherwise, there’d be a lot less disruptions (even when pandemics hit as they would be doing more near-sourcing, have backup plans ready to go, etc.)
  • Contracts: ask them where there contracts are, and what they are usually protected from and what they are usually not
  • Logistics: beyond risk, chances are they don’t have the right network for the logistics they need or the right carriers for the network they are forced into
  • Spend Under Management: they aren’t able to do nearly enough projects in a year to address enough significant/strategic/critical spend (either due to lack of talent, tech, turbidness [of spend], etc.)
  • Negotiation: some companies are paying more on contract than the spot market, sometimes this is bad negotiation, sometimes this is lack of insight, but regardless, it’s problematic
  • Forecasting: both actual demand (because you can’t trust Sales & Marketing) and future supply/demand imbalances and prices
  • inventory management: (because JIT sometimes stands for just-in-trouble)
  • Spend Visibility: for every dollar: who, what, when, where, why, and how … they just don’t know
  • etc. etc. etc.

If companies had enough talent today, they wouldn’t have the majority of these problems.

But here’s the thing, even if the mythical company existed that had none of these problems, Procurement is still constantly evolving. The suppliers they need to buy from are constantly evolving. The supply networks from the supplier to the company to their consumers are constantly evolving. Technology is constantly evolving. You don’t even know what’s coming, so you can’t know what skills you need, or if your talent will be ready. (Hint: They won’t. Because, even though we keep telling you, you won’t Train Them! [Even though educated, efficient talent are way more productive, you still cut the training budget first for reasons the doctor can’t fathom!])

In other words, it’s ludicrous for any company in the real world that buys and sells products and services in the real world who is, more than likely, barely treading water today to think they are prepared for tomorrow! (Now, they might be in good shape if they have top talent today, but they still need to keep that talent trained and at the top of their game to have any chance of being ready for tomorrow.)

But one thing this survey exposed is the fundamental problem with surveys — people can overestimate their knowledge or readiness (or score themselves higher than they should because they won’t publicly admit they aren’t doing as well as they could be) and then the analyst firm is stuck publishing the results it collects.  Even if they don’t seem plausible when you dig deep.

Global Sourcing Agencies — Are They The Hidden Evil of the Outsourcing World?

Note the Sourcing Innovation Editorial Disclaimers and note this is a very opinionated rant!  Your mileage will vary!  (And not about any firm in particular.)

We all like to blame the Big X (and the larger Mid-Sized consultancies) for the outsourcing revolution that put the whole world in sh!t when the pandemic started (because they spent three decades convincing every CEO and their favourite corporate lap-dog they would get immediate savings [which was true] by outsourcing everything possible to China, a country that then proceeded to do mandatory city-wide lockdowns for three years every time a single COVID case was confirmed). Not only did sudden unavailability in a single geographic source break many supply chains, but the three decades of unnecessary outsourcing also significantly contributed to GHGs and hastened our trajectory to a global 2C temperature increase as transportation GHG emissions have approximately doubled over the last 30 years (and are now responsible for about 30% of global emissions, especially since just 15 older ships contribute more GHG emissions annually than 50 Million cars).

But it’s not just the Big X and Mid-Sized pushing us towards “low cost countries” on the other side of the world (where they have to help with the introductions, organizational transition management, on-site audits, etc. etc. etc. to pocket 33% of those ephemeral savings as consulting fees), it’s Global Sourcing Agencies that are adopting their fee models, tactics, and strategies to help you find the right “partners” with their “in-country” consultants who can help you on the ground, except at slightly lower costs and with slightly more focussed industry expertise.

And the truth of the situation is that if you can’t produce the products (assemblies, components, parts) you need at home, you need to outsource. But the reality is that, today, you should be outsourcing as close to “home” (where “home” is the market you’re sourcing for, so if you’re a true global multi-national, sourcing near the US for the American market, in/near Europe for the European Market, in/near Australia and New Zealand for the Australasia market, and so on). You’re not sourcing from Russia for Argentina or China for the US. It makes no sense (and, at the end of the day, when you compound the disruption costs on top of the outsourced management and super high logistics costs, costs too many extra cents).

And chances are, now that you are trying to move to a closer to “home” market, you have no clue what suppliers are there, what their real production capabilities are, how well they have served other customers in your industry, how easy they are to work with, what your chances of (eventually) becoming a customer of choice really are, and how much help you can get on the ground if you need it. So you need a Global Sourcing Agency to help you, just like you will often need a Big Consulting Agency to help you with Procurement Transformation. But in this situation, it is many times more critical you choose the right one. If you choose a Global Sourcing Agency that specializes in China manufacturers when you are trying to pull out of China sourcing for your North American Market (and thus need deep insight into the Mexican and Brazilian manufacturing market), you’re not going to get many (if any) good options and end up being convinced that, for worse or for even worse, you need to stay in China.

So where’s all this coming from? What appears to be sponsored business spam. For example, the Business NewsWire and the Big News Network are pushing an unattributed* article titled The Role of Global Sourcing Agencies in Business across any business press release site that will accept it.  In our opinion, it’s a thinly veiled attempt to ensure that, with the current (long overdue) focus on “near-sourcing” (which you should have been doing since the initial rise of Mexican outsourcing half a century ago as a response to the introduction of Maquiladoras in the 1960s), that you stay in China (which is, of course, likely the LAST thing you should do unless you are also selling that product to China or nearby [Austral]Asia).

It’s yet another article making generalized good points about how Global Sourcing Agencies can help in theory, but whether they achieve that in practice depends on whether they have the right people, the right relationships, and the right technology — in the region you need them to be in. (Which, and we can not say this enough, is often NOT China!)

Now, if you are a global firm that sells to EurAsia or Austalasia, please use these firms that specialize in china.  You don’t want to be sourcing from South America or Africa for something you can build in Asia!   And if you want to re-shore from China to South America for your American market, find a firm that specializes in South America.

Just like every Big X has their areas of specialty (see when should you use a Big X), every Global Sourcing Agency has theirs.  Use them wisely.   While the right partner can help you reap long term rewards, the wrong partner will lead you deep into the dark woods of fabled nightmares from which you will never emerge again. (And, just like when you select the wrong Big X, it will be your fault.  If you select a Global Sourcing Agency that specializes in China, they will reasonably expect you want China.  Again, if that’s the case, great.  If not … )

 

* We’re glad the article it’s unattributed. We don’t want to single out any company in particular here. It’s the entire outsourcing business model we’re questioning!  We hope it evolves into a model that helps you outsource to near-source countries!  After all, just like America should not be buying something in China it could make in America to sell in America, America should not be buying something in America to sell in China it can make in China!  Sourcing needs to be re-shored to the nearest available source to minimize transport needs, costs, and delivery times.  Not one focus on whatever country looks to be the cheapest or best in the short term!

‘Tis the season … to bring an end to seasonality! (And JIT!)

Consumer shopping may be seasonal, but supply chains no longer support seasonality. The pandemic finally broke over-stretched supply chains, they haven’t fully recovered, and, as per this recent article over on Capgemini, we are still in a situation where 42% of CPR [Consumer Products and Retail] (also known as CPG, Consumer Purchased Goods) organizations expect stockouts or product shortages, 38% expect late deliveries, and 35% foresee labour shortages.

Marketers might like seasonality, as it makes them absolutely necessary, and sales people might like seasonality, because it gives them a reason to push sales (and possibly close a sale in a given time period), but human seasonality is limited to SAD (seasonal affective disorder). Just because consumers want to buy 5 times as many units of a product in December as they do the rest of the year doesn’t mean that humans in September can make 5 times as many units. If a plant normally runs 8 hours a day, the most a plant can theoretically run is 24 hours a day and the most it can do is triple its output. But that assumes it has enough, trained, seasonal, workforce. That’s not likely. Maybe it can split the skilled workforce in half, force half to take the second shift, and have each regular worker supervise one seasonal worker in an effort to double output. But a seasonal worker is not going to be as efficient as a regular worker, and, in the end, maybe output will increase by two thirds. Not much better than if they could just convince their entire workforce to work 12 hour shifts for the month and increase output by about 40% (you’re not getting the theoretical 50% as the workforce will be tired somewhere beyond the 8 to 10 hour mark).

Furthermore, you not only need to have five times the amount of product produced, you also need it transported to you — from half a world away. Seasonal capacity, especially in the late summer/early fall (to get goods to North America in time for the holiday season), has always been limited and with the scuttling of many cargo ships during the pandemic (including some ships that never made a single voyage) due to lack of cargo (because China shut a [port] city down), seasonal capacity is even less than it was. So how do you get the goods during the season, which is what you have been doing/attempting to do since the 80s thanks to the Big X and Mid-Sized consultancies advising you to switch to just in time (and push the inventory cost onto the manufacturer/supplier)? The short answer is, you roll the bones and hope for the best (because JIT now stands for just in trouble). And that’s not a good answer.

If you have “seasonal” demand because either

  • your business model is selling seasonal items or
  • you allowed marketing and sales to take what should be a product always in demand and make it seasonal

Then you have to start managing your own inventory close to the point of sale/last-mile distribution (if you do a lot of on-line business) and start building it up months in advance, based upon normal (non-OT production) and optimal distribution volumes. Yes, inventory is expensive, but what you don’t get is that

  • you’re paying for it anyway (because the supplier is charging you their overhead)
  • you’re losing a lot of sales, and profit, when you stock out
  • a few months of inventory is not that expensive and it’s only expensive if you overstock and then have to discount/fire sale

In other words, do proper data driven forecasting, ensure marketing and sales manage demand by driving people to the products that you have enough of that optimize your profit, right size your “local” warehouses, pick the cheapest locales for a region (your main warehouse doesn’t have to be in the city or even the primary business park, can be in a tier 3 business park a half hour out – that’s not going to add much to delivery cost), and start integrating core product management functions back into your business. Even if you sell seasonal, eliminating seasonality from your management model will decrease overall cost (no more shipping at peak rates in peak seasons or paying overtime overhead), decrease stock outs, and increase profit. Just do it.

An Absolutely Fabulous Article by Cory Doctorow on the (Gen) AI Bubble …

and how it’s going to pop like every other tech bubble since the first dot com bust!

What Kind of Bubble is AI?
  by Cory Doctorow

Cory doesn’t say it, but he makes it pretty clear that when the bubble pops, like every tech bubble that has come before, there may not be much less to salvage when it does (especially since no one is thinking about what happens when it does pop).

So I’ll clarify:

A lot of people are going to lose a lot of money

(and while stupid investors hyping this bandwagon heading for a cliff probably deserve to lose every penny, all of the pensioners in the pension funds they scammed don’t; so if you run a pension fund, please pull out of ridiculously overvalued Gen AI NOW!)

A lot of people are going to lose their jobs

(and it’s going to be more devastating to the tech sector than the Silicon Valley Bank failure this year combined with the recession forecast that resulted in over 250K IT jobs being slashed in the USA alone)

A lot of hardware is going to suddenly go idle

and smaller cloud providers are going to go under when the big name cloud providers all of a sudden drop their prices to the floor just to keep the revenue coming in (resulting in the monopolies of Amazon, Google, and Microsoft controlling most of the servers outside of China and Russia)

The problem is, as Cory clearly lays out, when you take one step back and look at the ridiculous hype from a business/revenue lens, all of the big, exciting use cases for AI are either

a) low dollar [and low-stakes and fault-tolerant] (helping us cheat on our [home]work or generating stock-art for bottom feeders [who won’t pay an artist and don’t mind ripping off the IP from thousands of artists]) or

b) high-dollar but high-stakes and fault-intolerant (self driving cars, radiological cancer detection, worker screening and hiring, etc.)

and when you consider the data center costs of these super-sized models (as these data centers consume MORE energy than a small town), low-dollar AI applications won’t pay the bills and high-dollar AI applications cost MORE to deploy than to just do it the traditional way with an educated and capable human!

E.g. self-driving cars don’t work (and “Cruise” needs to employ 1.5 times as many supervisors as a taxi service would employ drivers to keep their cars, which still hit and critically injure people, relatively safe)

E.g. radiological cancer detection requires a human expert to spend the usual amount of time in diagnosis before consulting the AI, and then, if the AI doesn’t agree, spend that much time again

Not that we’re not stopping you from jumping on the (Gen-)AI bandwagon or selling that silicon snake oil that Open AI and Microsoft AI are selling. We’re just not joining you on the (Gen-)AI bandwagon as the steering algorithm is defective and it’s heading straight for a very high cliff at a very high speed …

Merry Christmas!

Good Questions to Ask If Procuring Tools With AI, Especially If You’ve Answered the First Question Wrong!

Continuing on with our statement that sometimes you have to listen to a lawyer, a recent article over on Bloomberg Law noted that Companies Should Ask These Risk Questions When Procuring AI Tools and gave us four questions in particular that were good:

Do I Understand the Data

The article gets it right when it says that AI tools are only as robust as the data they’re trained on, as well as the need to know what data is collected, how, and if all rights are respected when doing so. But what they didn’t get is that the data determines what models and techniques can be used, and what models won’t be that effective or reliable. A vendor sales rep will tell you that whatever technique it’s using is just right for your problem, but the reality is that the sales rep likely doesn’t have anywhere close to the mathematical knowledge to know if its appropriate or not, especially since that sales person may have barely passed remedial junior math (as not all US states require remedial senior math to graduate High School). Furthermore, there’s no guarantee that even the tech teams know if the model is appropriate or not. If the company just hired a bunch of developers with maybe a year of university math, gave them access to a bunch of libraries, and all they did was test out various machine learning models until one appeared to work to a sufficient degree of accuracy on the test suites they compiled, it doesn’t mean they understand the model, why it worked, or even the appropriate characteristics of the data set that allowed the model to work — it just means that they can say for data sets that look like this, it should work. (But what is look like?) You need to understand the data, and find someone who understands the models that it is appropriate for.

Have I considered Regulatory Scrutiny?

Not only do you have to take note that The Department of Justice, Federal Trade Commission, and other regulators are focused on whether technology companies and their tools create anti-competitive environments or put consumers at a disadvantage, but many jurisdictions are considering or implementing laws against the use of black-box technology where the output — which determines whether or not a person can get a loan, be insured, or even apply for a job or government program — and the logic behind the decisions, and the rules that were applied, cannot be explained. You could also be in trouble if the process is fully automated and there isn’t a human in the loop to validate the decision, if the systems uses (third party) data that it has no right to use, or if the output data is not sufficiently protected if it was generated from input data that must be protected and the output can be reverse engineered.

Have I Mitigated Security Risks?

It’s not just traditional cyber attacks on the system, it’s well calculated queries that can slightly perturb the system over time until the outputs after the 10th, 100th, or 1000th slight, imperceptable, perturbation result in an output the system never should have given in the first place, such as approving a ten million dollar loan to a high-risk foreigner who will take the money and run or denying insurance to all people with a genetic defect likely to result in a specific condition down the road that can only be treated by a single drug owned by a single pharmaceutical who will drive people into bankruptcy for a pill that costs $5 to make.

Did I include Best Practices in the Contract?

More specifically, did you include the best practices you want followed in the contract? Don’t leave best practices up to the vendor to define however they want to define them. Make sure you cover all necessary security measures, compliance with all government and regulatory guidelines on AI in the regions you intend to use it (and open standards if there are none, guidelines from the UN, the Responsible AI Institute, or something similar), and so on.

And these are great questions, but the first question you should always ask is:

Do I Really Need AI?

And only when you choose the wrong answer, and say yes, do you need to ask the questions above. The reality is that you don’t ever need AI. AI means that you, or the vendor, were just unwilling to take the time to understand the problem and design an appropriate solution. Remember that when you try to jump on the AI bandwagon heading off the cliff (for the sixth decade in a row).