Category Archives: Market Intelligence

The Supply Management Paradox

The best supply chain is invisible, but an invisible supply chain gets no recognition in your average company.

This is the one lesson they don’t teach you in Operations Management or Supply Chain 101, probably because they don’t want to discourage you given the upward battle we still face in our chosen discipline of Supply Management.

The sad reality is that your average employee in your average company, and even your average C-Suite executive in too many companies, has no knowledge of this paradox. Just like your average person is unaware of Bernoulli’s Paradox or even the Birthday Paradox.

At an average company, the majority of the supply chain function is invisible from most employees. Good show up at the warehouse, then get shipped to the office / store locations. When an employee needs a new laptop, tablet, or phone, s/he logs into the company portal and selects one of the pre-approved options and the item is on her desk within 2 business days. The sales guy places the order, and the customer gets it when promised. No one knows how much research and time goes into identifying appropriate suppliers, negotiating contracts, signing contracts, placing purchase orders, negotiating change orders, receiving goods, performing quality spot-checks, receiving invoices, matching everything, making sure the right goods get to the right locations, coding restock alerts / automated orders, handling returns (and ensuring credits are received and replacements arrive on time), handling switch overs when a new source of supply needs to be brought on, ensuring industry regulations are not violated, ensuring sustainability goals are met, ensuring there is no third party child labour in the supply chain (or anything else that could tarnish brand image), and so on. Hundreds, if not thousands, of hours have to go into making that “one click laptop replacement” work as desired.

Plus, in a well researched, planned, and smoothly executed supply chain, raw materials and components show up almost just-in-time (JIT) at the plant that is producing your goods. Then the boxes are waiting at the other end to package them, and as soon as the boxes are filled, the palletizer is there to pallet them. As soon as the pallets are full, the pallet jacks are waiting to load them unto the truck that just pulled up to take them to your distribution centers. Etc. Etc. Engineers don’t have to worry about raw materials or components being late or in insufficient supply. Loading dock personnel don’t have to worry about needing extra temporary storage as the trucks are there when the order is complete. Etc. Etc. Not only do they not have to worry about supply chain functions beyond their jobs, but your job looks like it’s the easiest job in the world because, like magic, everything (and everyone) is there when they need it. As a result, the better your supply chain runs, the less respect you get in an average company for doing a “hard” job because you make it look so easy.

That’s the supply management paradox, and one of the reasons many of us still don’t get No Respect.

Enterprise Software Companies Do Need Media Relations (Re-Post)

This post initially ran five years ago, but since the PR frenzy is back (as a result of the M&A frenzy), this needs a re-post!

In yesterday’s post, we insisted that Enterprise Software Companies DO NOT need Public Relations, because they do not. Why? Simple. They DO NOT sell to the public. They sell to big corporations. Big corporations are not the public.

Also, the messaging that you need to sell to a CFO is nothing like the message that you need to sell to an impulsive consumer. Good business is all about productivity, progress, and Return On Investment. Good public relations is all about feeling, connection, sexy, environmental responsibility, or anything else that happens to be the buzz of the day. Good enterprise relations is all about results. Public relations, like consumer advertising, is in constant flux. But the basics of good business never change.

However, the advertising channels through which business advertising have exploded, not only as a result of the rapid expansion of the ubiquity of the world wide web, but of social media as well. As a result, the complexity of media management has increased dramatically. The fundamentals haven’t changed, but the amount of work required to coordinate and manage the effort has. Not to mention the knowledge required to strategically place your advertising and messaging to stand out amidst the noise, which consists not only of a constant stream of advertising and messaging from your competitors but analysis, third party reviews, and random comments. It’s a media jungle, and unless you have a team of full time pros to manage it 24/7, you need help. Even if you do have a team, you probably need guidance.

A good Media Relations Team will help you:

  • Identify the Right Channels
    Which traditional print and online web publications are right for you?
    What are the right channels to advertise your coverage?
    Who are the right people at these outlets to reach out to?
  • Tailor the Message
    While you need to craft and own your message, you also need to recognize that different individuals at different publications who control different channels are interested in different parts of the message you have to deliver. To get your message heard, sometimes you have to focus in on the part that will get a crier’s attention.
  • Spread the Message
    Parts of your message have to spread through others, but thanks to the social media revolution, other parts have to be spread by your organization through social media channels. Managing these can be a full time job, and not the best use of your limited resources. This is best left to an expert.

In other words, you need help, but the help you need is not Public Relations. It’s Media Relations.

And if you really need someone to talk to in order to help you elicit your messaging in a collaborative fashion, hire a subject matter expert (SME) whom can also offer you project management, product development, or thought leadership consulting services. This will jump start those efforts as the subject matter expert will not only be fully familiar with your messaging, but with your modus operandi as well. As a result, there will be little to no learning curve for the SME when it’s time to start the project management, product development, or thought leadership creation. This will pay off in spades as you’ll get your project, product, and/or thought leadership done faster, hit the market faster, and see a significant return faster.

So when it comes to getting help, get the right help. Even if you don’t thank me for it.

Enterprise Software Companies DO NOT Need Public Relations! (Re-Post)

This post initially ran five years ago, but since the PR frenzy is back (as a result of the M&A frenzy), this needs a re-post!

Since we’re on the topic of what really grinds the doctor‘s gears, another thing that really grinds the doctor‘s gears is the incessant insistence by public relation companies that they need to be ingrained in all communication activities undertaken by an enterprise software company. To this I say, BullCrap!

Let’s start by defining what public relations is. As can easily be read on Wikipedia, public relations is the practice of managing the spread of information between an organization and the public. Let’s dwell on this. It’s the management of information flow between the organization and the public. Now let’s dwell on what enterprise software companies do. Enterprise software companies sell software made by their organization to their client organizations. Now let’s dwell on this. They move software from one organization to another organization. Not to the public. As a result, the accompanying information flow is between two organizations, not between the organization and the public. So where does public relations enter the mix?

Let’s dive into what modern Public relations organizations do, or at least try to sell perspective clients, to see if we can make any sense of this.

  • Audience TargetingWhile it’s important to sell to the right audience, enterprise software companies have a pretty good idea of who their audience is. It is companies with a potential need for their software that is their audience, and not only does marketing have a pretty good idea of what their audience is, it is their job to know what that audience is.
  • MessagingMessaging is of the utmost importance, especially with so many other vendors also hawking their wares, and in a world where many customers are looking for partners, or at least software providers who can offer a complete solution (software, services, and training), the messaging often has to be perfect. But this is why you have Marketing — this is their primary job.
  • Social Media MarketingSince many of the decision makers at a potential customer are on social media, this is an important channel in which to place your messaging. With so many social media networks (LinkedIn, Facebook, Twitter, etc.) and so many different individuals in the target organizations to target (employees, directors, C-Suite, etc.), this is a lot to manage, and secondary to the messaging and audience targeting responsibilities of Marketing. So it makes some sense to get some help here — but this help should come in the form of organizations that specialize in social media marketing for B2B organizations, not Public Relations firms that specialize in information flow to the public for B2C organizations.
  • Media RelationsThis is important for any organization that does business and needs to get its message out to the world, even if it is just the corporate sector. However, this relationship should be controlled by marketing, not some third party with a watered down message.

Now it’s no secret that the doctor does not like PR, for a host of reasons (chronicled in his Blogger Relations series), but this has nothing to do with his like of PR. This has to do with his dislike of many PR firms telling enterprise software companies that they need to be embedded in all of their communication processes and work with those companies in a collaborative and consultant manner for months and months to define their targeting, messaging, (social) media, and relations strategy and do all of the work that should be done, or at least managed, by Marketing at a very high cost to you. Not only are you shelling out 10’s of thousands of dollars for them to walk you through an exercise where you do all the work (because, let’s face it, they don’t have a clue what you’re selling, what’s unique about it, or how to uniquely position it), but you’re losing two, three, and sometimes even four quarters of momentum while you go through this drawn out exercise to get a message that your marketing team, possibly with the help of some subject matter experts, could figure out in a matter of weeks! It’s the oldest consulting trick in the book after making up a fad you don’t need — take your money to listen to you elicit what you need. (If you need to talk through your strategy to elicit your messaging, the doctor is certain a quack psychologist will be cheaper.)

So Fire That PR Firm and put your money where you need it:

  • Subject Matter Expert Consultingto help you figure out what is distinct about your solution and missing in your solution space
  • Thought Leadership and Expert Writing Servicesto help you get your message crystallized and down on (white) (e-)paper and in appropriate training materials for your clients
  • Social Media Campaign Managementto manage your messaging through social media and on-line channels

Just like you shouldn’t get taken in by companies selling infinite scrolling websites that you don’t need, you shouldn’t get taken in by companies selling your collaborative PR services that you don’t need either.

Big Data: Are You Still Doing it Wrong?

The only buzzword on par with big data is cloud. According to the converted, or should I say the diverted, better decision are made with better data, and the more data the merrier. This sounds good in theory, but most algorithms that predict demand, acquisition cost, projected sales prices, etc. are based on trends. But these days the average market life of a CPG product, especially in electronics or fashion, is six months or less, and the reality is that there just isn’t enough data to predict meaningful trends on. Moreover, in categories where the average lifespan is longer, you only need the data since the last supply/demand imbalance, global disruption, or global spike in demand as the data you need for the current trend before that is irrelevant … unless you are trying to predict a trend shift, in which case you need the data that falls an interval on each slide of the trend shift for the last n trends.

And if the price only changes weekly, you don’t need data daily. And if you are always buying from the same geography, dictated by the same market, you only need that market data. And if you are using “market data” but 90% of the market is buying through 6 GPOs, then you only need their data. In other words, you only need enough relevant data for accurate prediction. Which, in many cases, will just be a few hundred dat points, even if you have access to thousands (or tens of thousands or even hundreds of thousands).

In other words, big data does not mean good data, and the reality is that you rarely need big data.

But you know that AI doesn’t work without big data? Well, their are two fallacies here.

The first fallacy is that (real) AI exists. As I hoped would have been laid bare in our recent two-week series on Applied Indirection, the best that exists in our space is assisted intelligence (which does nothing without YOUR big brain behind it, and the most advanced technology out there is barely borderline augmented intelligence.

The second fallacy is that you need big data to get results from deep neural networks or other AI statistical or probabilistic machine learning technologies. You don’t … as long as you have selected the appropriate technology appropriately configured with a statistically relevant sample pool.

But here’s the kicker. You have to select the right technology, configure it right and give it the right training set … encoded the right way. Otherwise, it won’t learn anything and won’t do anything when applied. This requires a good understanding of what you’re dealing with, what you’re looking for, and how to process the data to extract, or at least bubble up, the most relevant features for the algorithms to work on. But if you don’t know how to do that, then, yes, you might need hundreds of thousands or millions of data elements and an oversized neural network or statistical classifier to identify all the potentially relevant features, analyze them in different ways, find the similarities that lead to the tightest, most differentiable clusters and adjust all the weights and settings to output that.

But then, as MIT recently published (E.g. MIT, Tech Review), and some of us have known for a long time, many of the nodes in that neural networks, calculations in the SVM, etc. are going to be of minimal, near zero, impact and up to 90% of the calculations are going to be pretty much unnecessary. [E.g. the doctor saw this when he was experimenting with neural networks in grad school over 20 years ago; but due to the lack of processing power (as well as before and after data sets to work on) then versus now it was a bit of trail and error to reduce network size]. In fact, as the MIT researchers found, you can remove most of these nodes, make minor adjustments to the other nodes and network, retrain the network, and get more or less equivalent results with a fraction of the calculations.

And if you can figure out precisely what those nodes are measuring and extract those features from the data before hand and create appropriately differentiated metadata fingerprints and feed those instead to a properly designed neural network or other multi-level classifier, not only can you get fantastic results with less calculation, but less data as well.

Great results come from great data that is smartly gathered, processed, and analyzed — not big data thrown into dumb algorithms where you hope for the best. So if you’re still pushing for bigger and bigger data to throw into bigger and bigger networks, you’re doing it wrong. That’s the wrong way to do it. And the only way you can call it AI is if you re-label AI to mean Anti-Intelligence.

Comprehensive Category Management: Are You Still Doing it Wrong?

As we said five years ago (and probably even earlier than that), spot buying individual categories at market lows or evening running reverse auctions at opportune times is NOT category management. And for that matter, neither is a strategic sourcing event that throws everything in the category into a strategic negotiation, especially if the category is metals and you are including the kitchen sink.

And you might be thinking that the doctor needs a psychiatrist because how could it not be category management if you are addressing the whole category? Category Management isn’t just about grouping all seemingly related items and running an event. Category management is about grouping items that have related characteristics that allow the items to be sourced effectively under the same strategy.

For example, while it might make theoretical sense to group printers, ink, and paper together —- because you use them together, from a sourcing point of view, ink and paper often go better with office supplies and printers with hardware. You can probably get them thrown in for free with a server purchase. But that’s just the start.

For example, if you source a lot of metal parts, you should probably start by grouping them by primary metal, since the price of steel, aluminum, etc. will largely dictate the price of those parts. Furthermore, it might even make sense to not only source all of the parts from the same supplier but even buy the metal on behalf of the supplier with your better negotiating power and/or credit rating.

But that’s just the start. Then you have to make sure the parts are (best) produced using similar processes, because giving a part to a supplier that is only easily produced by laser cutting when the supplier only has traditional machining / cutting is not going to be a good decision. Even though the volume will lower their cost of metal, the extra work will increase the cost per unit.

So sometimes you will need to group the category into sub-category by metal and production style and get bids separately and together (from any supplier that can offer both) and do a multi-level analysis to find out the best approach. (And this is yet a another reason that SI has been telling you since DAY ONE that you need an optimization-backed sourcing platform as this is the only way you can effectively analyze all the options.)

And sometimes you will have to ignore items with a large demand or core material component because they are cheaper when sourced as part of a different category buy as they can be produced by other suppliers or bundled for a larger volume-based discount.

For example, consider an organization-wide UPS replacement. They are technically a power transformer with a battery, but you wouldn’t source them from the manufacturer that manufactures custom transformers for your on-site renewable solar and wind farm since you’d source them from your hardware supplier who supplies you with the rest of your office electronics as they would be buying such units in bulk from a manufacturer who produces them in bulk and gives you a better deal.

Comprehensive category management is looking at a category from a holistic perspective and finding the right segmentation to get the best overall value through the right sourcing method at the right time.

It’s not just a one-time slice-and-dice, it’s a continual analysis of the category from a multi-dimensional and current market perspective to make sure each time an event is run, the right strategy is used across the right sub-category of products and services which are offered to the right prospective supply base.

And it requires up-front market analysis before the event as well as optimization-backed analysis during. So you need a good analytics platform, preferably with some automation that can constantly pull in market data, analyze it to current cost, plot and predict the trends, and provide the necessary market intelligence that can be compared to a best-practice knowledge base that will indicate the event type that has been the most historically successful under current conditions. (And in the spirit of our recent Applied Indirection series, this is not AI, this is RPA with parameterized suggestion look-up.)