Category Archives: Market Intelligence

Even Forbes is Falling for the the Gen-AI Garbage!

This recent article in Forbes on the Supply Chain Shift to Intelligent Technology is what inspired last week’s and this week’s rant because, while supply chains should be shifting to intelligent technology, the situations in which that is Gen-AI are still extremely rare (to the point that a blue moon is much more common). But what really got the doctor‘s goat is the ridiculous claims as to what Gen-AI can do. Claims with are simultaneously maddening and saddening because, if they just left out Gen-AI, then everything they claimed is not only doable, but doable with fantastic results.

Of the first three claims, Gen-AI can only be used to solve one — and only partially.

Procurement and Regulatory Compliance
This is one example where a Closed Private Gen-AI LLM is half the battle — it can process, summarize, and highlight key areas of hundred page texts faster and better than prior NLP tech. But it can’t tell you if your current contracts, processes, efforts, or plans will meet the requirements. Not even close. In fact, no AI can — the best AI can just indicate the presence or absence of data, processes, or tech that are most likely to be relevant and then an intelligent human needs to make the decision, possibly only after obtaining appropriate expert Legal advice.
Manufacturing Efficiency
streamline production workflows? optimize processes? reduce errors? No, Hell No, and even the Joker wouldn’t make that joke! You want streamlining? You first have to do a deep process cycle time analysis, compare it to whatever benchmarks you can get, identify the inefficiencies, identify potential processes and tech for improvement, and implement them. Optimize processes? Detailed step by step analysis, identification of opportunities, expert process redesign, training, implementation, and monitoring. Reduce errors? No! People and tech do the processes, not Gen-AI — implement better monitoring, rules, and safeguards.
Virtual Supply Collaboration
A super-charged chatbot on steroids is NOT a virtual assistant. Now, properly sandwiched between classical AI and rules-based intelligence it can deal with 80% of routine inquiries, but not on its own, and it’s arguable if it’s even worth it when a well designed app can get the user to the info they need 10 times faster with just a couple of clicks. Supply chain communicating? People HATE getting a “robot” on a support line as much as you do, to the point some of us start screaming profanities at it if we don’t get a real operator within 10 seconds. Based on this, do you really think your supplier wants to talk to a dumb bot that has NO authority to make a decision (or, at least, should NEVER have the authority — though the doctor is sure someone’s going to be dumb enough to give the bot the authority … let’s just hope they can live with the inevitable consequences)?

And maybe if the article had stopped there the doctor would let it pass, but
first of all, it went on to state the following for “AI”, without clarifying that Gen-AI doesn’t fit in the process, leading us to conclude that, since the first part of the article is about Gen-AI, this part is too, and thus is totally wrong when it claims that:

“AI” understands dirty data
with about 70% accuracy where it counts IF you’re lucky; that’s about how accurate it is at identifying a supplier from your ERP/AP transaction records; an admin assistant will get about 98% accuracy by comparison
it can “confirm” inventories
all it can do is regurgitate what’s in the inventory system — that’s not confirmation!
it can identify duplicate materials
first it has to identify two records that are actually duplicates;
and how likely do you think this is with a supplier mapping accuracy of 70%?
it can identify materials to be shared among facilities
well, okay, it can identify materials that are used across facilities and could be located in a central location — but how useful is that? it’s not because, first of all, YOU ALREADY KNOW THIS, and, second, IT CAN’T DO SUPPLY CHAIN OPTIMIZATION — THAT’S WHAT A SUPPLY CHAIN OPTIMIZATION SOLUTION IS FOR! OPTIMIZATION!!! We’ll break it down syllabically for you so you know what to ask for. OP – TUH – MY – ZAY – SHUN!
it can recommend ideal storage locations
again, NO! This requires solving a very sophisticated optimization model it doesn’t have the data for, doesn’t know how to build, and definitely doesn’t know how to solve.
it can revamp outdated stocking policies
well, only the solution of a proper Inventory OPTIMIZATION Model that identifies the appropriate locations and safety stock levels can identify how these should be revamped
it can recommend order patterns by consumption and lead time
that’s classical curve fitting and tend projection

And, secondly, as the doctor just explained, most of what they were saying AI could do CAN’T be done with AI, and instead can only be done with analytics, optimization, and advanced mathematical models! (You know, the advanced tech (that works) that you’ve been ignoring for over two decades!)

The Gen-AI garbage is getting out of control. It’s time to stop putting up with it and start pushing back against any provider who’s trying to sell you this miracle cure silicon snake oil and show them the door. There are real solutions that work, and have worked, for two decades that will revolutionize your supply chain. You don’t need false promises and tech that isn’t ready for prime time.

Somedays the doctor just wishes he was the Scarecrow. Only someone without a brain can deal with this constant level of Gen-AI bullsh!t and not be stressed about the deluge of misinformation being spread on a daily basis! But then again, without a brain, he might be fooled by the slick salespeople that Gen-AI could give him one, instead of remembering the wise words of the True Scarecrow.

Interrupt that Risk Event with Interos and Sustain Stable Supply Chains

Supply Chain risks are on the rise, as are disruptive events, and an event anywhere in your supply chain, even four levels down, can bring your operations to a halt if you can’t detect it, respond quickly, and take active mitigations. To this end, as chronicled in Part X of our Source-to-Pay+ Series that discussed Supply Chain Risk, a number of vendors have cropped up in the last few years around Supply Chain risks, but not all players are equal.

One of the first of the new breed of integrated supplier and supply chain risk players, and one of the most differentiated, is Interos. Interos was founded in 2005 by Jennifer Bisceglie as a consultancy focussed on helping organizations map out, understand, and get a handle on supply chain risk. Jennifer realized near the end of last decade that, with supply chains becoming so long, so complex, and so interconnected across the digital, financial, and physical realms, that technology would be needed to support organizations in this effort.

The core team knew that in order to do this, they’d need a completely new type of technology, so they sought out a new team to build one of the first outside-in business relationship graphs using trade data, third-party data sources and artifacts (such as ownership data, executive data, etc.), and even press releases. Then, on top of this relationship data, they’d need to layer risk data to help an organization identify risks in the supply chain. This would involve capturing risk events as well in order to help them understand which clients may need to be notified and/or use the Interos platform to gauge the extent that a risk event may impact them. So that’s what they built — at a global scale.

Interos has built a business relationship (knowledge) graph that connects 11 Billion relationships across 410 Million companies. These companies are then risk scored against 230+ attributes across six (6) different categories of risk: Finance, Geo-political, Restrictions/Sanctions, ESG, Cyber, and Catastrophic, depending on the extent of information available. At a minimum, they track country/industry level risks and will use that when there is insufficient data to assess the specific company risk against a specific attribute. Based on the assessment of each risk, Interos will compute an overall i-ScoreTM from 1 to 999, with lower scores being higher risk. It will then scan your entire network, from sink to source, and identify all high risk suppliers for you.

The Interos Resilience platform, which processes tens of thousands of sources and over 3 Terrabytes of raw data daily, constantly monitors for new relationships, information, and (related) events that could pose a change in an entity’s risk status, as well as indicate the presence of a (potentially) catastrophic event, including a natural disaster or a cyber-attack. For each of the six risk domains, the platform scans for a number of factors, sub-factors, and individual attributes. We’ll cover the primary factors in this post, and if you have a particular area of interest, you can always drill in during a demo or discussion with Interos.

With respect to Finance, the platform looks for the following:

  • Liquidity: Cash, Working Capital
  • Solvency: Assets, Capital Efficiency, Credit Rating, Debt Coverage, & Leverage
  • Profitability, Debt Coverage, & Valuation

With respect to Geo-Politcal risk, the platform looks at the following:

  • Political Instability
  • State Capacity
  • Political Process
  • Economic Rights
  • Socio-Economic Development

With respect to Restrictions/Sanctions, the platform looks at the following:

  • Sanctions (USA, UK, EU, etc.)
  • Associated Sanctioned Individuals
  • Import/Export Embargos
  • Associated Regulations

With respect to ESG, the platform looks at the following:

  • Environmental Performance
  • Social Commitment
  • Governance Strategy

With respect to Cyber, the platform looks at the following:

  • System Attacks (compromised accounts, cyber-attacks, data spills, etc.)
  • System Vulnerabilities
  • Supply Chain Cyber Events
  • Cyber Compliance
  • Cyber Threat Activity

With respect to Catastrophic risk, the platform looks at the following:

  • Localized Natural Hazard and Disaster Risk
  • Communication Capacity
  • Healthcare Capacity
  • Infrastructure Capacity
  • Burden of Disease Risk

Based on all of this, the platform is very useful for companies that need to perform

  1. Supplier due diligence
  2. Continuous related party monitoring
  3. Real-time catastrophic event detection

Interos is one of the most complete supply chain risk intelligence platforms for supplier due diligence. The ability to quickly screen a supplier on six highly relevant domains can give an organization confidence that the organization understands the risk profile of a supplier before onboarding it, which is not something you can get from a traditional credit score or an empty search on sanction lists.

Interos is one of the few platforms that can be counted on for continuous related party monitoring as it processes over 3 TB (Terrabytes) of data a day, constantly updates risk scores and related events for affected entities in the system, and can propagate updates through the business relationship graph in real time.

Interos is also one of the few platforms that can be used to do real-time catastrophic event detection where the event is not limited to a single event type, as the platform monitors for natural disasters, man-made disasters, bankruptcies, and cyber incidents — some of which Interos can detect before anything is reported due to a change in organizational behaviour — and it can immediately propagate news of events or risks to one of the 410M+ business entities it tracks to all impacted clients who can use their relationship explorer to identify all the links it has to the company.

For example, if there’s a fire in a raw material or component factory (which seems to happen in one of the few major RAM suppliers every decade — just do a few historical Google Searches if you don’t believe me) two (or three) tiers down the chain under your tier 1 supplier, you can immediately map out all of your tier 1 suppliers that trace down to that factory and make sure they have enough stock on hand to continue producing your products until you expect that factory to come back online (by either instructing them to immediately secure additional stock on your behalf or doing so for them) well before your competition realizes there’s going to be a disruption a week down the road when the plant is announced shut down and it finally trickles down to local news half a world away.

The platform monitors and tracks natural disasters globally down to a gird of 10 km squares, as well as potential paths of storms, waves, and fires, and can thus immediately identify each business entity that is likely to have been impacted as well as each business entity that is likely to be impacted if a natural disaster (such as a storm) continues its course. Thus, if a tsunami hits the coast of Japan, it can allow an organization’s incident response teams to immediately identify just those organizations in Japan in the area the wave hit and allow it to focus its efforts on just those suppliers, vs. having to reach out to and assess every supplier in Japan, of which it may have hundreds if it is in electronics when only ten were in the immediate area. The time savings alone is incalculable. (And, of course, if an earthquake hit a province in China, it would take an army of consultants months to figure out precisely what suppliers were close enough to the fault line to likely have suffered [significant] damage vs those far enough away to only feel minor shaking whereas the Interos platform will calculate all of this in just a few minutes.)

However, one of the most unique risk monitoring capabilities lies in its proprietary digital behavioural modelling that can often detect when an organization has experienced a potential cyber-attack, breach, or data theft and alert customers to that potential cyber-incursion days, or weeks, before the organization announces a breach and/or it makes the news. Using the business relationship graph, this immediately allows an organization to determine every first-tier supplier that relies on that organization. The organization then has to determine if any of those suppliers has access to the organization’s financial account information, personnel data, or confidential intellectual property. Those tier 1 suppliers that do need to be immediately approached and asked if any of that data was shared with, or accessible by, the sub-tier supplier that was breached, or affected by. If so, the organization can immediately start taking mitigation actions before they themselves are the target of a cyber attack.

The platform is very easy to use. When a user logs in, they see a summary of their full supply base and multiple sub-tier relationships (which for a multi-national with tens of thousands [10k+] of tier 1 suppliers can be hundreds of thousands of tier-3 suppliers). The user can see the number of suppliers by tier who are high risk, medium risk, low risk, and, possibly, unknown (as it’s a brand new supplier where there is little to no information on that supplier). Note that the number of “unknown” suppliers will typically be really small, and for most truly global companies with 500K global suppliers in their extended supply chain, the unknown will be significantly less than 5K (usually 0.5% or less).

(Note: If more than 1% of your extended supply chain falls into high risk, you have some serious problems. In a good supply chain, the vast majority of suppliers should be low risk (> 95%) with a small percentage medium risk, preferably no high risk, and preferably no unknown.)

You also see a breakdown of risk by

  • each of the six (6) risk domains, which lets you see if there is a particular risk concentration,
  • average risk by groups of interest (which could be country, product line based, strategic suppliers, etc.),
  • a summary of natural hazards and disasters currently being tracked, both visually and textually (which shows the number of potential tier 1, 2, 3+ suppliers that are potentially impacted)
  • a visual summary of the most relevant current events being reported on (with links to full articles in third party sources), and
  • a quick link to the relationship explorer tool that will let you find all of your connections to an entity of interest

When you select a category of high-risk suppliers (overall or by category), it will bring up a list of companies with their individual i-Scores that you can select to to bring up their complete risk scorecard (if you have unlocked their scorecard; depending on your subscription level, you have so many credits that allows you to unlock that many scorecards; you can buy more if you need, but most since most companies don’t need to evaluate more than a small percentage of tier 2+ suppliers, their packages are usually sufficient). The scorecard summary will summarize the score in each of the six areas, and will allow you to drill down into the factors, sub-factors, and individual attributes that are known and scored (and contribute to the overall score), which include those discussed above.

The scorecard will also summarize company corporate data (industry registrations and codes, locations, etc.), its tier 2 and tier 3 relationships and risks, which can be filtered to all known relationships (in your extended supply chain), as well as all events (and related sources) that have been detected that are relevant to that supplier entity. If a risk score is low (or suddenly drops), you will have access to all of the data that contributed to that score to make your own judgement (and jump-start your investigation).

The platform also has a geographic view of natural disasters that is interactive and allows a user to drill into a region, filter on natural disaster type (earthquake, tropical storm, volcanic eruption, etc.), and even project a few days in the future (if the disaster is a tropical storm, cyclone, tsunami, etc. and there is forecast data available from Interos‘ 3rd party, or public, sources). In addition, it can be used to look at historical natural disaster and weather event data, which goes back between 50 and 200 years, depending on how much historical data is available for the region, as well as the risk of each natural disaster type (wildfire, drought, earthquake, flood, etc.) in the region base on all of this historical data.

And the relationship explorer is likely the most useful part of the platform because, if a risk event is detected, such as a natural disaster or a cyber breach, you can instantly trace all of your active relationships to that company, and immediately start the process to determine if these tier 1 (and tier 2) suppliers will be impacted, and, if so, the degree to which you’ll be impacted. Not only will you know about an event days, or weeks, sooner than you would know without this platform (and by then it may have been too late to find an alternate source of supply or protect your data), but you can limit your discovery and mitigation efforts only to suppliers that might be affected, versus doing massive surveys and reach-outs (that can take days or weeks) to find out who might be impacted in the first place.

Interos is a one of the most powerful, and complete, risk intelligence platforms out there and one that should definitely be on your shortlist if you’re looking to get 360-degree visibility into your supplier, and supply chain, risk.

The Supply Chain is Full of Hidden Risks

A recent article in the Supply Chain Management Review by Avetta provided Insights for Procurement Leaders on tackling hidden risks in the supply chain. As per the article, supply chains are full of:

  • Geographic Vulnerabilities
  • Cybersecurity Threats
  • Ethical and Compliance Issues
  • Financial Instability
  • Environmental Recklessness

… and all of this poses a major risk to your supply chain. Avetta‘s baker’s dozen of recommendations are to:

  • conduct due diligence on all level of suppliers
  • identify alternate sources
  • monitor geographical developments
  • prioritize cybersecurity measures
  • conduct regular risk assessments
  • foster a culture of cyber awareness
  • establish clear codes of conduct
  • regularly audit supply chain partners
  • prioritize transparency and accountability
  • rigourous financial due diligence
  • monitor key financial indicators
  • prioritize sustainability initiatives
  • establish robust contingency plans

And these are all good, but most of the risk results from one thing:

  • lack of timely, accurate data on
    • the physical supply chain (people, plants, product, vehicles, etc.)
    • the financial supply chain (the financial state of suppliers, contractors, employees, etc.)
    • the information supply chain (completeness, accuracy, security, etc.)

This says that if you really want to tackle the hidden risks, you need to start with the following as you can’t tackle anything you can’t identify:

  • supply chain visibility — map every entity in your supply chain
  • external risk monitoring — whenever a geographical, political, environmental, or cyber disruption happens anywhere, and is reported, you need to detect that, identify all entities that may be affected, confirm which entities in your supply chain are affected, and take an appropriate mitigating action
  • cyber network monitoring — you need to monitor your entire network, every server, every client (desktop, laptop, tablet, AND cell phone), every router, every API end point, and every wire … your weakest link is your effective security
  • cross-system and account financial monitoring — money disappears when there are holes for it to fall into; holes exist when you have disconnected P-Card, e-Procurement, and AP systems, especially across divisions and you aren’t correlating balances between transfers, bank accounts, and investments on at least a daily basis
  • activity monitoring — all waste, loss, and fraud is the result of a bad actor, whether or not the bad acting was intentional (hint: if the loss is significant, it usually is intentional; incompetence often only results in minor loss); but you can’t monitor everyone, even if you wholly operate in a jurisdiction where doing so is legal; but, when everything is digitized, you can monitor every action, whether or not is in accordance with policy, flag everything that isn’t, and escalate any actions that are against policy that should be investigated

As you detect issues and disruptions, you can start with standard mitigation actions, and as you identify patterns of commonality, you can identify additional contingency plans, which you should already have for every product or service that is critical to your operation.

Note that Sourcing Innovation has published a list of 55+ Supply Chain Risk Vendors that already have solutions that do a lot of this monitoring. There’s no excuse for your organization not to have at least an 80% solution in place today.

You Don’t Need Gen-AI to Revolutionize Procurement and Supply Chain Management — Classic Analytics, Optimization, and Machine Learning that You Have Been Ignoring for Two Decades Will Do Just Fine!

Open Gen-AI technology may be about as reliable as a career politician managing your Nigerian bank account, but somehow it’s won the PR war (since there is longer any requirement to speak the truth or state actual facts in sales and marketing in most “first” world countries [where they believe Alternative Math is a real thing … and that’s why they can’t balance their budgets, FYI]) as every Big X is pushing Open Gen-AI as the greatest revolution in technology since the abacus. the doctor shouldn’t be surprised, given that most of the turkeys on their rafters can’t even do basic math* (but yet profess to deeply understand this technology) and thus believe the hype (and downplay the serious risks, which we summarized in this article, where we didn’t even mention the quality of the results when you unexpectedly get a result that doesn’t exhibit any of the six major issues).

The Power of Real Spend Analysis

If you have a real Spend Analysis tool, like Spendata (The Spend Analysis Power Tool), simple data exploration will find you a 10% or more savings opportunity in just a few days (well, maybe a few weeks, but that’s still just a matter of days). It’s one of only two technologies that has been demonstrated, when properly deployed and used, to identify returns of 10% or more, year after year after year, since the mid 2000s (when the technology wasn’t nearly as good as it is today), and it can be used by any Procurement or Finance Analyst that has a basic understanding of their data.

When you have a tool that will let you analyze data around any dimension of interest — supplier, category, product — restrict it to any subset of interest — timeframe, geographic location, off-contract spend — and roll-up, compare against, and drill down by variance — the opportunities you will find will be considerable. Even in the best sourced top spend categories, you’ll usually find 2% to 3%, in the mid-spend likely 5% or more, in the tail, likely 15% or more … and that’s before you identify unexpected opportunities by division (who aren’t adhering to the new contracts), geography (where a new local supplier can slash transportation costs), product line (where subtle shifts in pricing — and yes, real spend analysis can also handle sales and pricing data — lead to unexpected sales increases and greater savings when you bump your orders to the next discount level), and even in warranty costs (when you identify that a certain supplier location is continually delivering low quality goods compared to its peers).

And that’s just the Procurement spend … it can also handle the supply chain spend, logistics spend, warranty spend, utility and HR spend — and while you can’t control the HR spend, you can get a handle on your average cost by position by location and possibly restructure your hubs during expansion time to where resources are lower cost! Savings, savings, savings … you’ll find them ’round the clock … savings, savings, savings … analytics rocks!

The Power of Strategic Sourcing Decision Optimization

Decision optimization has been around in the Procurement space for almost 25 years, but it still has less than 10% penetration! This is utterly abysmal. It’s not only the only other technology that has been generating returns of 10% or more, in good times and bad, for any leading organization that consistently uses it, but the only technology that the doctor has seen that has consistently generated 20% to 30% savings opportunities on large multi-national complex categories that just can’t be solved with RFQ and a spreadsheet, no matter how hard you try. (But if you want to pay them, a Big X will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at 5K a day, there goes three times any savings they identify.)

Examples where the doctor has repeatedly seen stellar results include:

  • national service provider contract optimization across national, regional, and local providers where rates, expected utilization, and all-in costs for remote resources are considered; With just an RFX solution, the usual solution is to go to all the relevant Big X Bodyshops and get their rate cards by role by location by base rate (with expenses picked up by the org) and all-in rate; calc. the expected local overhead rate by location; then, for each Big X – role – location, determine if the Big X all-in rate or the Big X base rate plus their overhead is cheaper and select that as the final bid for analysis; then mark the lowest bid for each role-location and determine the three top providers; then distribute the award between the three “top” providers in the lowest cost fashion; and, in big companies using a lot of contract labour, leave millions on the table because 1) sometimes the cheapest 3 will actually be the providers with the middle of the road bids across the board and 2) for some areas/roles, regional, and definitely local, providers will often be cheaper — but since the complexity is beyond manageable, this isn’t done, even though the doctor has seen multiple real-world events generate 30% to 40% savings since optimization can handle hundreds of suppliers and tens of thousands of bids and find the perfect mix (even while limiting the number of global providers and the number of providers who can service a location)
  • global mailer / catalog production —
    paper won’t go away, and when you have to balance inks, papers, printing, distribution, and mailing — it’s not always local or one country in a region that minimizes costs, it’s a very complex sourcing AND logistics distribution that optimizes costs … and the real-world model gets dizzying fast unless you use optimization, which will find 10% or more savings beyond your current best efforts
  • build-to-order assembly — don’t just leave that to the contract manufacturer, when you can simultaneously analyze the entire BoM and supply chain, which can easily dwarf the above two models if you have 50 or more items, as savings will just appear when you do so

… but yet, because it’s “math”, it doesn’t get used, even though you don’t have to do the math — the platform does!

Curve Fitting Trend Analysis

Dozens (and dozens) of “AI” models have been developed over the past few years to provide you with “predictive” forecasts, insights, and analytics, but guess what? Not a SINGLE model has outdone classical curve-fitting trend analysis — and NOT a single model ever will. (This is because all these fancy-smancy black box solutions do is attempt to identify the record/transaction “fingerprint” that contains the most relevant data and then attempt to identify the “curve” or “line” to fit it too all at once, which means the upper bound is a classical model that uses the right data and fits to the right curve from the beginning, without wasting an entire plant’s worth of energy powering entire data centers as the algorithm repeatedly guesses random fingerprints and models until one seems to work well.)

And the reality is that these standard techniques (which have been refined since the 60s and 70s), which now run blindingly fast on large data sets thanks to today’s computing, can achieve 95% to 98% accuracy in some domains, with no misfires. A 95% accurate forecast on inventory, sales, etc. is pretty damn good and minimizes the buffer stock, and lead time, you need. Detailed, fine tuned, correlation analysis can accurately predict the impact of sales and industry events. And so on.

Going one step further, there exists a host of clustering techniques that can identify emergent trends in outlier behaviour as well as pockets of customers or demand. And so on. But chances are you aren’t using any of these techniques.

So given that most of you haven’t adopted any of this technology that has proven to be reliable, effective, and extremely valuable, why on earth would you want to adopt an unproven technology that hallucinates daily, might tell of your sensitive employees with hate speech, and even leak your data? It makes ZERO sense!

While we admit that someday semi-private LLMs will be an appropriate solution for certain areas of your business where large amount of textual analysis is required on a regular basis, even these are still iffy today and can’t always be trusted. And the doctor doesn’t care how slick that chatbot is because if you have to spend days learning how to expertly craft a prompt just to get a single result, you might as well just learn to code and use a classic open source Neural Net library — you’ll get better, more reliable, results faster.

Keep an eye on the tech if you like, but nothing stops you from using the tech that works. Let your peers be the test pilots. You really don’t want to be in the cockpit when it crashes.

* And if you don’t understand why a deep understand of university level mathematics, preferably at the graduate level, is important, then you shouldn’t be touching the turkey who touches the Gen-AI solution with a 10-foot pole!

Last Friday Was International Women’s Day. You Made a Big Fuss. Well, What Did You Do This Week?

This is taken from a LinkedIn post the doctor posted on Monday, March 11. It’s being reposted here for those who don’t follow LinkedIn and because, as expected, he hasn’t heard a single peep from any organization that was spewing platitudes last Friday as if praise one day a year was doing enough.

If you truly celebrate women, then please tell me:

What are you doing TODAY to

  1. increase the number of women in Management, STEM, Executive Suites, and Investment Firms,
  2. close the pay gap that is still 15% to 30% across these areas,
  3. encourage women to join your company to pursue their career, and
  4. enable the work life balance they need to be AS or MORE successful than their male counterparts?

As most of you are probably well aware from the deluge of “we support and honour our female leaders who … ” posts on LinkedIn last Friday, International Women’s Day was last Friday (2024-Mar-08). I stayed silent, as usual, because I found the majority of them very upsetting.

While some of the posts were very sincere, and some came from individuals I know had the best of intentions:

  1. Lip service does nothing to address the four major issues above.
  2. The lip service I saw in some of these posts was about as meaningful as a token thank you card at the annual Christmas party.
  3. Few addressed the real issues women still face in “traditional” workspaces run, and dominated, by men.
  4. Those few that honoured teams with equal representation or greater, or at least statistically average representation (in companies in fields where women are currently only 25% of the workforce, like STEM) have done nothing to educate their peers on how important this is and how successful they are because of it.

If you are a leader in a company (with actual employees) that truly cares, then I challenge you to celebrate their achievements and capability every day, and once a month make a post on efforts your company is taking to increase the number of women, close the pay and rank gaps, and support their work life balance, either through hiring, training, support for community programs that do such or at least make a post on the stellar accomplishments they have accomplished that would put an average salesman to shame.

And to keep doing this until they have the equality, and the respect they deserve.

The simple facts are

  1. women are half the population,
  2. are just as capable of men (as there is NO difference between average IQ scores), and
  3. should be half the workforce.

If women are not half the workforce at your company (or at least not represented statistically in line with the average representation in the field your company is in), it’s not their lack of achievement, dear men, it’s yours!