Category Archives: Market Intelligence

MBAs, Sourcing Innovation Feels Your Pain!

MBAs, let’s address the elephant in the room77. You’re drinking from a firehose65 right now while burning the candle on both ends60. It’s a perfect storm31. Profits are down, your visibility into the quarter is a little fuzzy37, and you don’t want to get thrown under the bus71. The organization is going through a re-org84 in an attempt to right-size16 and correct the perception, right or wrong, that there were too many Chiefs and not enough Indians56.

By way of housekeeping22, you know you need to increase mind-share with the customer85, but you don’t have enough boots on the ground20 to scale your marketing efforts. You need to treat the big deals as one-offs40, but you are facing some headwinds39 with the COO who wants to standardize. You need to put your game face on69 and manage expectations52, but you don’t know what to do and you don’t want the R33. But if we take the 30,000 foot view50, unless you want to be re-org’d out, you better take the R33.

Hope is not a strategy46, but you think that’s all you have. You don’t know the next steps73 or the competition killer76 that will allow your organization to move up and to the right83 and regain its profitability and its fame. You don’t have a go-to-market64 strategy, any paradigm shift1 would be outside your core competency38, and you don’t know what your next deliverable81 is.

But it doesn’t have to be that way. If you eliminate the ready, fire, aim45 approach, put a stake in the ground35, remember the old 80-20 rule51, put aside the basic blocking and tackling68, and do a level-set67, you’ll realize there is a radical solution. Procurement.

And while it’s good to put a face to the name49, it’s even better when you realize that it is the next-generation, turn-key, plug-and-play17 solution that will finally deliver the ROI5 you’ve been searching for. Sure you will get some push-back66 when you suggest it for the first time, but Procurement has it covered from soup to nuts70. Of course you should do a little more due diligence there43, but Procurement really is the cash cow25 you’ve been searching for.

And while you can expect that Marketing will sound like a broken record55 when you bring this up and continue to push their agenda, you need to circle back to that9 later, push your agenda, and loop them in82 when they are ready to cooperate. Once you’ve done your research and identified leading organizational best practices80, you’ll know that Procurement is the way to stop leaving money on the table44 and close the loop72 on organizational success.

There’s a reason that strong Procurement support has gone viral59 in leading organizations and that’s because Procurement’s constant quest for value-add63 increases profits. And while previous business fads have failed, this time its different79 because Procurement has to eat its own dog food28 and is very incentivized41 to buy premium.

MBAs, be sure to come back tomorrow as we explain, in your language, why you have to support procurement.

One Hundred and Seventy Seven Years Ago Today …

The American Statistical Association (ASA) was founded in Boston, Massachusetts (which makes it the second oldest continuously operating professional society in the US).

If you were ever wondering why there are so many experts so adept at manipulating statistics to support whatever viewpoint they are selling, it’s because you’ve had the best professionals working together to improve the art for even longer than you have had professionals working together to improve science and engineering. (The IEEE is only 53 years old, and the associations that formed — namely the American Institute of Electrical Engineers and the Institute of Radio Engineers — would only be 132 years and 104 years, respectively.)

And for those of you relatively new to Sourcing Innovation, particularly those who would like more insight into lies, damn lies, and statistics, we’d like to remind you that, in the archives, Pinky and the Brain (back in the days when they were locked in the basement of a building in Massachusetts that was the headquarters of a major sourcing company before it was acquired) gave us a lesson in statistics that exposed some of the, well, lies. Enjoy!

Is Bad Procurement Responsible for the Proliferation of Snake Oil?

Once upon a time in China, medicine was based on ingredients found in nature. One such ingredient was snake oil (from a water snake) that was used to treat joint pain (which we now call arthritis and bursitis) because it gave relief when rubbed on the skin at a painful site. This is because real snake oil contained a lot of omega-e fatty acids which not only reduce pain but also reduce inflammation. While not necessarily a powerful medicine, it is a medicine nonetheless.

When the Chinese worked on the first transcontinental railroad (in the US), they introduced this medicine to fellow workers (which included native and working class Americans) who had never had such a medicine. They were, naturally, impressed by it … as were salesmen looking to make a quick buck with this “miraculous pain cure” which, at a time when western medicine was relatively weak, was desired by the population at large. So, long before the creation of the FDA (1906) when anyone could sell anything they wanted, these salesmen created fake snake oils (which often didn’t contain any oils from snakes) that they hawked as “cure-alls”. These roaming charletans, who often paid assistants (who would travel separately, act sick and injured, take the cure-all, and immediately exclaim cure), did well, until people who bought the tonics realized they din’t work. But by then they were on to the next town.

However, people did travel and eventually realized that all these salesmen and untrained / unlicensed doctors were, as we still say today, quacks (which is short for the Dutch quacksalver who would use home remedies to “cure” everything, whether there was any real basis for the cure or not), and gave a name to these cheaters and the product they sold. Because the first cure-all tonics were called “snake-oil”, any medicine with a ridiculous claim was “snake oil” and any peddler of such a ware was a “snake-oil salesmen”. However, these salesmen and products are not restricted to the consumer world, and soon those claiming to have the ultimate solution to all of a business’ process, sales, or cash management problems, etc. were deemed snake oil salesmen, selling processes or products that didn’t work. And we still have these salesmen today, often pitching great investment opportunities (especially in high tech, which we call silicon snake oil) that don’t really exist.

But to make matters worse, we don’t just have snake oil salesmen to deal with, we have snakes in our business (which the snake-oil salesmen claim they can deal with, but we’ll get back to that). What kind of snakes? KPI snakes. As the procurement dynamo explains over on procurement.world, most enterprises are chock-full of Cobra KPIs.

Why Cobra KPIs? Blame the British for that and their folly of offering a bounty for every dead snake the locals brought them in 19th-century Delhi. If you were an enterprising, but struggling, vaishya, and you received rupees for every dead snake, and the British were handing over rupees by the snake, you’re going to want to get your hands on as many snakes as possible. What’s the easiest way to do that? Breed them in droves, and kill them in droves.

As the British discovered, Cobra KPIs are those that eventually rise up, strike, and inject you full of poisonous venom. All the British got for their efforts were drained coffers and more live snakes then they wanted (when the merchants released them all on the streets when the bounty was cancelled). And if your organization has Cobra KPIs, all it will get for its efforts is loss — lost time, lost money, and lost opportunity.

Organizations, including Procurement organizations, are chock-full of these Cobra KPIs. Like FTE per dollar of spend, supplier count per category/region, p-card spend per total spend, sourced spend over total spend, etc. (Why? It’s not how much is spent on Procurement resources, it’s the value they generate. It’s not how many suppliers, it’s the right number of suppliers, which varies by product, region, and numerous other factors. It’s not how the bills are paid, it’s whether or not the bills are for the right stuff. And sometimes it’s best to renegotiate with incumbents and seek out value-add innovation versus unrealizable or unsustainable year-over-year cost reductions.)

But even worse, because organizations are sometimes too obsessed with KPIs, they overload themselves with overly intensive tracking, scorecarding, and reporting initiatives that take time away from value-generating sourcing activities. And then they decided they need a solution to automate this or reduce the stress. So they go to market. And who responds? Snake-oil consultants who offer their next generation tracking, scorecarding, and reporting platforms that, for a “very reasonable” one-time set-up fee (in the tens or hundreds of thousands, depending on how big the organization is and what they think they can get), and an ongoing “expensable” license and maintenance fee that just fits under the P-card limit on a monthly basis, the organization will be provided with an automated system that will do all the data collection, normalization, integration, scorecard generation, trending, and reporting the organization needs. And if needs change, changes can be made for a “modest” daily consulting fee (of only a few thousand a day). It’s the KPI cure-all! And it’s snake oil. Snake oil that the organization proliferates by defining too many KPIs, which always contain too many wrong KPIs, that entail too much work that needs to be automated.

In other words, don’t define KPIs needlessly. And don’t define any until you not only understand what the goal of the KPI is, but how the KPI will help the organization achieve that goal. (With reference to our examples above, it’s ROI per FTE above a threshold, it’s a supplier range for each product-region pairing determined by way of stakeholder collaboration and analysis, p-card spend under management above a threshold, and spend under management — where spend under management is any spend where a conscientious decision was made to tackle the spend that way after an analysis. Now, the ROI calculation will have to carefully thought out and reviewed regularly, the supplier ranges revisited regularly, and the threshold reviewed to make sure it doesn’t trigger a red light on emergency spend, but at least these metrics are designed with a value focus in mind.)

(And if you’re not careful, you might end up with real snakes! Just ask the airline industry, that, just two days ago, made Samuel L. Jackson’s nightmare of motherf*ckin’ snakes on the motherf*ckin’ plane a reality for some passengers of the Air Mexico Torreon-Mexico City flight! [One Source!] If you have too many useless KPIs, as Douglas Adams tried to warn us years ago [as you can wait forever for lemon-soaked paper napkins and never get enough], the attendants will be too busy running through time-wasting checklists rather than making sure that only authorized, paying, passengers are in the cabin!)

 

Trade Extensions is Redefining Sourcing, Part VII

In Part I, we not only told you that Trade Extensions unveiled the upcoming version of their optimization-backed sourcing platform at their recent user conference in Stockholm, recently covered by the public defender over on Spend Matters UK, but we also told you that, with it, Trade Extensions are redefining the sourcing platform.

Then, after discussing a brief history of sourcing platforms, and common limitations with most of the platforms on the market, we dived into many of the advancements Trade Extensions, despite already having one of the few third-generation sourcing platforms on the market, is making to take sourcing to the next level. Numerous usability enhancements, composable workflows, centralized fact sheets with even more powerful processing, easy repeat events, and better user and collaborator management. We also noted TESS 6 is coming with a whole new approach to in-platform analytics, but held back because we first need to provide a history of spend analytics (in Parts IV and V) and its shortcomings.

In our last post, we noted that the first thing Trade Extensions has done is to create a whole new analytics rule language which makes the definition of cleansing rules, enrichment rules, and the application of existing mappings and mapping rules a breeze. A single natural language rule with a single cleaning, enrichment, or mapping sheet can process an entire file of millions, tens of millions, or even hundreds of millions of transactions (as long as you don’t expect to view more than a million at a time). A few rules can link purchase orders, invoices, goods receipts, and payments and make it easy to see where the missing records are. Even a junior analyst can do it with ease.

But this isn’t the only significant advance that TE has made. Realizing that the hard part is the creation of an initial set of mapping rules for a new or not yet analyzed data source, they’ve created a new approach to mapping. Getting the message that, as per yesterday’s post, the traditional secret sauce isn’t always enough and a more generic recipe is needed, they’ve adopted the generic recipe outlined by the doctor and taken it to the next level. (At Trade Extensions, all amps go to 11.)

Often, when p-card data or invoice stores are the only data that is available, all the user has is a vendor (short) name and an associated receipt with abbreviated line item info or a vendor name and line item detail. And all the mappings have to be on the product description field. In this case, rules need to be built up as mappings on single words or phrases, with double word or phrase overrides for more complex mappings with further triple word or phrase overrides for sub (sub) categories, and so on. In this case the user will define a key phrase (or regular expression on a key phrase), look at the mappings, create a modification for special cases, and then move on in an attempt to identify another key phrase (that will trap a large number of transactions).

This works, but it’s a slow and cumbersome process. The small sample a user selects to try and manually identify keywords through a quick scan might not be representative, and the user might pick relatively low frequency words or phrases for the first few dozen, or hundred, rules and get nowhere fast*. So what’s the solution? AI? Definitely not. If there’s not enough data for you to always make a good decision, why would you blindly trust an algorithm (that may have been tuned on completely different data sets)?

The solution is AR (Automated Reasoning) and guided rule construction. In TESS 6, during the creation of an initial mapping file, the buyer can select a text column and the system will identify the most common words and phrases, in descending order, and guide the user through the selection and creation of appropriate mapping rules. The user will see, in a transaction file with a lot of office supplies, that “file”, “paper”, “pens”, and “boxes”, appear frequently; “copy paper”, “ballpoint pens”, and “file boxes” appear less frequently; and “xerox paper copier”, “bull pens”, and “junction boxes” appear even less frequently. Each time a general rule is created, the user can drill into the (potentially) affected transactions, see the next most common set of (sub) words and phrases, and, if necessary, easily define an override rule of higher priority, and then either drill in further, or back up to the unmapped set. The user can, very quickly, map the transactions until 90% to 99% are mapped … to an acceptable accuracy.

And, moreover, the rules are always run on a file in order of the least number of affected transactions. Since each override rule is designed to apply to a smaller set of transactions, running the defined rules in order of the least number of affected transactions means all the (super) special cases are defined first. It also means that if a rule defined first would fire first, that the initial rule was not defined as generic as the user believes it was or the nature of the data has evolved over time (and the appropriate mapping rules should be evolved as well). In addition, these mappings can be created in conjunction with one or more classification columns, such as vendor (if two different vendors use the same language to described what are two different products to the buyer) or some other categorization code (from the accounting system or the ERP).

Since many words and phrases are common, the reality is that even a million records can often be mapped 95%+ rather accurately (on a first pass) with only a few thousand rules. This can be done, by hand, in a few days, and be considerably more accurate than even the tenth pass over the data by a current generation automated mapping solution which has to be trained and corrected for weeks by the offshore data centre until enough accuracy is obtained that you could even consider looking at a spend report.

The rule language is easy. The interface is even easier. And the guided manual mapping capability puts analytics into the hands of every buyer. And since it’s all fact sheet based, the data can come from anywhere, anytime, be analyzed on the spot, and pushed anywhere it’s needed. It can come from the procurement system, be analyzed, and a subset used in an optimization scenario, or a set of award scenarios can be combined, analyzed and reported on. Data can flow back and forth with ease, and be classified and manipulated with ease.

It’s what analytics in a sourcing platform should look like. And it’s only in TESS 6.

* which is not a desirable state of affairs for spend analysis

Trade Extensions is Redefining Sourcing, Part VI

In Part I, we not only told you that Trade Extensions unveiled the upcoming version of their optimization-backed sourcing platform at their recent user conference in Stockholm, recently covered by the public defender over on Spend Matters UK, but we also told you that, with it, Trade Extensions are redefining the sourcing platform. But we did not tell you how, as we first had to review the brief history of sourcing platforms.

Then, in Part II, we built the suspense even more by taking a step back and describing the key features that are missing in the majority of current platforms — namely usability, appropriate workflow, integrated analytics support, repeat event creation, limited visualization, and limited support for different types of users and collaborators. While not everything a user would want, these are certainly among the features a user will need to realize the full power of advanced sourcing.

Then, after making you wait two days, in Part III we finally discussed how Trade Extensions are not only redefining the sourcing platform, they are redefining the advanced sourcing process itself! Customized advanced sourcing workflows, the next level of enterprise software usability, better user and collaborator management, easy workflow and event duplication, improved fact sheet management, and a new integrated analytics capability that makes analytics the other side of the advanced sourcing coin.

But instead of detailing the new integrated analytics, in Parts IV and V, we took another step back and walked through a brief history of analytics, taking care to detail all of the issues with manual analysis — limited reports, even more limited value, accuracy issues, and mapping nightmares — and automated analysis — with poor clustering, poorer rules, and forced taxonomy, as there was no way to understand just what TESS 6 will be giving you without describing what is sorely missing. And now we are here. And here we will discuss how TESS 6 puts analytics side by side with optimization.

First of all, Trade Extensions has added a whole new analytics rule language. The current platform supports the construction of advanced formulas and rules for optimization using an advanced formula language, which can also be used for analytics, but the language was not designed for cleaning, mapping, and enrichment. The new rule language makes it easy to clean data by replacing erroneous values with valid values using validity tables, which can be stored and manipulated in standard fact sheets. All the user has to do is select the columns to be cleaned in the current sheet, the sheet with the valid data values, and specify the column mappings. Enrichment is just as easy. Pick the values (columns) that are required, pick the relationship columns (that allow the values to be related), and create another rule. A single rule. Each rule, expressed in natural language, will run over an entire (set of) sheet(s) and makes rule creation simple and elegant.

But most importantly, it not only makes mapping (which can be done in the same manner as cleaning and enrichment) easy, but also initial mapping easy.

As per our last post, initial mapping can be a nightmare, unless you have the secret sauce, which, as has been discussed many times here on SI, is:

  1. map the vendors
    as many vendors only supply a single category
  2. map the GL codes
    since they are usually for a category subcategory, even though they are not that useful for spend analysis
  3. map the vendors plus GL codes
    since this gets you a subcategory or a sub-subcategory
  4. map the exceptions
    where something is mapped according to GAAP and not according to a meaningful spend category
  5. map the exceptions to the exceptions
    where something gets tacked on to a category because that’s where it seemed to fit

And if you have the vendor and GL code data, and a tool that makes it easy to map the vendors, GL codes, and (override) combinations, and then drill into each mapping and find, and map, exceptions, this works rather well. But the tool doesn’t always support this (or the concept of rule priority, as the rules have to be applied in reverse of their creation order, or there are mis-mappings), and you don’t always have the vendor or GL code, especially if the file is from a P-Card system, procurement system, or other non-accounting system. So the secret sauce is a bust.

And when the secret sauce is a bust, most systems fall flat on their UI faces. That says you need a secret sauce that is not file or system dependent. And the means to implement it.

Fortunately, there is a secret sauce that is system independent, and it’s not that hard to make. (Although you’d think otherwise as the doctor has been trying to teach the recipe for years, with no success … until now.)

If you look at the abstract baseline of the above algorithm, it’s:

  • map a primary catch-all categorization fieldso that all transactions can be mapped to a category, even if it is to “Other” which identifies transactions that need (better) mapping
  • map a secondary catch-all categorization fieldso that if the primary field is empty, the second field maps an otherwise unidentified transaction
  • map the primary and secondary field pairingswhere doing so is more accurate than either field on its own
  • map exceptions to the field mappings
    based on a third field
  • map exceptions to the exceptions
    based on more detailed rules and/or other fields

which, if need be, abstracts even more to:

  • create a (set of) baseline catch-all rule(s)
  • create a (set of) baseline catch-all backup rule(s)
  • create a (set of) baseline rule(s) that work on field pairs and/or dual descriptor pairings
  • create a (set of) detail rule(s) that catch exceptions
  • create a (set of) detail rule(s) that map any special cases (that materialize as exceptions to the exceptions)

And when the rules are run in consistent reverse order of definition, you get consistent, accurate mappings (that can be corrected if new exceptions arise and that can fall in an “other” category if new, otherwise unidentifiable, transactions appear)). And this is key.

Why? Come back tomorrow for Part VII!