Category Archives: Technology

CVM: Not Just About Supplier Diversity Anymore!

CVM Solutions (acquired by supplier.io), one of Spend Matters’ Nine Vendors to Watch in 2008, appears to be on a quest these days to conquer the sourcing space, with their intent to offer supplier data enrichment, supplier relationship management (SRM), spend analysis, and a soon-to-be-launched procure-to-pay process management solution. According to Jason, CVM is a “best-kept secret” because of their extensive data enrichment services, supplier portal, and their new uber-workflow and process management engine that creates a level of control and visibility that he’s not seen elsewhere.

Now, I don’t know if they’re the “best-kept” secret, but when it comes to their data management capabilities and their new process management engine, they’re certainly a well-kept secret. Their extensive data management solution allows you to track extensible & customizable information on each supplier of a generic, location-based, contact-based, business registration, financial, capability, diversity, contract, sourcing, SRM, administration, and documentary nature – including scanned attachments which can be easily uploaded by specifying meta-data, printing off a cover-page with a system-generated bar-code, and faxing the document (with a cover-page) to a CVM provided fax number. (Reducing the number of steps in the traditional scan, convert, upload, tag, index.) In other words, their data management capabilities are in the same class as Aravo, a vendor I’ve written about before. However, due to their extensive supplier information databases (as the largest provider of diversity information in the US with enrichment data being culled from over 330 external databases and over half of the Fortune 500 as customers), they can also quickly and easily enrich your data, eliminating the need to integrate a third party’s data stream into your supplier data management / supplier information management solution (SIM), which might reduce the Total Cost of Ownership for a company that needs extensive amounts of third party data (as long as their pricing for enrichment services remains competitive with Austin Tetra and D&B.

For an initial release, I was also quite impressed by their new procure-to-pay process management engine. Although it’s not competitive with either your best-of-breed e-Procurement suites or your best-of-breed e-Sourcing suites when it comes to procurement and sourcing capabilities, with proper use, its flexible design allows you to accurately track all of your ongoing projects, and their current status, with respect to each supplier and each category and manage the process which, for most companies, probably involves at least three or four different systems (spend analysis, e-Negotiation, contract management, e-Procurement, e-Payment, order management, etc.). For example, their default assessment / supplier selection / contract draft / contract approval / transition workflow allows a category manager to document where each sourcing project is, and, when a supplier is selected, track where the project is with respect to contract drafting, approvals, and issuance. This information is then integrated with the data repository, where you can define alerts to notify you when certain quotas are reached / not reached in a time period and when a contract is about to expire. Steps can be added to, or removed from, the workflow, as required by your organization, and it allows you to continue using a collection of best-of-breed products from multiple vendors but still track your project status in one-location (which I believe is critical because there isn’t a single suite vendor with more than 3 solutions that is Best of Breed in everything – and when a best-of-breed solution can squeak out even 2% to 3% more on 100M+ spends, in today’s economy, I don’t think you can justify not going with a BoB solution).

I was also impressed by the usability of the application from a buyer’s perspective and a supplier’s perspective – which is important when you want to capture tier 2 diversity information from your suppliers in addition to asking them to use the tool to enter and maintain their basic information. The only thing I didn’t like was the response time – many screens took an average of 3 to 4 seconds to load in the demo (and I’m not willing to blame the internet as I have high speed cable on a 15MB rated network and can sustain 1MB/sec download times on my machine with ease). If you’re going to be maintaining large databases, you’ll need to ask about their SLAs and insure that you have enough processing power and bandwidth dedicated to the application. (Fortunately, with today’s hardware prices, you can do that at very affordable prices!)

I was, as regular readers might guess, not impressed with their “spend analysis”, or, more appropriately, with their classification of their spend reporting program as “spend analysis”. It’s a good reporting package with over 30 pre-configured reports that has all of the day to day reports that managers and accounting and tactical purchasers are going to need … but when it comes to the true analysis capability required by your power buyers, it’s just not there. In other words, like many of the spend analysis applications on the market, it will satisfy all of the requirements of management and your tactical purchasing team, but none of the requirements of your power buyers. However, it is seamlessly integrated with their platform, so if you were willing to augment it with a stand-alone power tool for your power buyers (like BIQ [acquired by Opera Solutions, rebranded ElectrifAI]), it would allow them to focus on true analysis and not have to worry about meeting the reporting requirements of management or the tactical buyers AND allow those individuals to quickly and easily access spending reports augmented with supplier information, diversity data, and standard classification systems such as NAICS and SIC. Plus, if you maintain complete contract pricing information (which is quite easy to do in their solution for commodities), it can automatically generate “offender” reports for accounts payable when you’re over-billed or accounts receivable when you reach the discount volume. So, if you can get a good deal on it, the “spend reporting” solution could be worth the price when you calculate the opportunity cost of a power user of a companion BoB application generating standard reports for management and accounting vs. digging for new opportunities. Plus, and this could be a key selling point for them, they have an add-on Federal Reporting Module that can be configured to automate Federal and State Agency reporting down to the contract level – and anyone who has had to prepare these reports knows how time-consuming they can be.

Finally, they’re also getting into Risk Assessment Reporting, which, after some of the recent supply chain disasters we’ve seen in recent years, is likely soon to be a must for larger corporations. When it comes to talking-the-talk, they’re certainly ahead of much of their competition in understanding “risk” and the importance of measuring it, planning for it, and proactively dealing with it. When it comes to walking-the-walk, their reports appear to be more-or-less comparable to their competition, especially since they can pull in D&B risk scores and supporting data. However, these days, I don’t think a financially based risk-assessment tells the whole story. I think they need to integrate more sources of information, especially for small businesses (like Austin Tetra is doing), to arrive at a more complete risk picture. It’s good for a first offering, but I’d like to see how it improves over the next year before locking in any long-term agreements. This is an emerging market in the sourcing services sector, and I’m not sure if anyone really has a good lock on what the right solution is.

In summary, I think they are a well-kept secret when it comes to supplier data management and, now, sourcing and procurement workflow management, I definitely think they have a lot of potential on the SRM/SPM side and the risk side as well, and even though they don’t have true “spend analysis” (just like the vast majority of vendors who make the claim), I think that their “spend reporting”, and their federal reporting module in particular, is quite good from a usability perspective, especially for non-technical management, accounting, and tactical buyers. They’re definitely a company to look at and keep an eye on, but like other extensive suite providers, they’re not best-of-breed in everything (no matter how good the “eye-candy” UI looks).

Actions for Big Supply Chain Improvements

Supply Chain Digest recently ran their top 10 list of “the easiest actions for big supply chain improvement”. Leave off the “easy” and I’ll agree, since some of the actions they recommended weren’t that “easy” and they left off a couple that were.

Their list was the following:

  • Centralize Transportation Management
    (At least they admit this isn’t easy!) As the author notes, the potential freight and overhead savings are huge – and you can make better informed sourcing decisions.
  • Take Control of Inbound Freight
    There is money to be saved on both inbound and outbound freight and all freight should be looked at objectively. Although it’s true that sometimes a large supplier can get you the best deal, it’s often true that often they can’t. Remember, you can leverage freight across your suppliers. They can only leverage freight across the customers they handle freight for. This means that often you’ll have the leverage with the freight provider – so you should use it.
  • Enforce Routing Guide Compliance
    You only save money from a good plan that was carefully constructed from a detailed analysis if it is implemented. Make sure that your sourcing and logistics professionals understand that when it comes to approved plans, it’s the company’s way or the highway for them.
  • Use Labour Management in Distribution Centers
    It’s important you have the staff you need when you need them. Not enough staff when a truck arrives causes delays that could lead to lost sales – too much staff when there are no trucks to load or unload costs you money!
  • Profile SKUs and Orders to Reslot the DC
    A simple analysis of this data can lead to simple improvement opportunities in slotting and warehouse layout that can drive big productivity improvements. Be lean.
  • Revisit Safety Stock Levels and Policies
    Too much inventory leads to markdowns and losses – not enough leads to missed sales and even more losses. Monitor stock levels regularly and update levels and policies as needed.
  • Analyze Supplier Lead Time Variability
    Find the variability, develop corrective action plans to reduce it, and implement them.
  • Use E-Auctions
    Many companies are leaving huge amounts of money on the table by not utilizing this technology when it makes sense to do so.
  • Constantly Compare Actual Total Landed Costs with Forecasted Costs
    If you want to realize your savings, you have to insure you get the savings you negotiated.
  • Start a Lean or Six Sigma Initiative
    … but be smart about it! Don’t go overboard, especially at first, as all you’ll end up with is Sick Sigma, and that won’t help at all.

To that I’d add the following:

  • Invoice Analysis
    Get a real spend analysis tool, mine your invoices, and see if you’re paying what you’re supposed to be. SKU analysis is good, but in many categories, like office supplies, computers, and electronics, you’re probably overpaying – and it won’t take much effort to find savings. If you need help, there are a number of consultancies that specialize in these efforts.
  • Enforce Supplier Contracts
    Route adherence is important, but buying off the contract your sourcing team painstakingly researched and negotiated to get you the best buy is even more so. Many organizations have maverick spend of 40% or more. That’s 40% of your negotiated savings gone … before the product is even shipped!
  • Use Decision Optimization!
    Not only is this the best way to optimize your freight spend, but it’s the best way to optimize your total spend – especially if you select a solution that allows you to optimize all of the aspects of the buy at the same time. e-Auction is good … but it’s not always appropriate, and not always going to net you the best buy from a total value management perspective.

Basically, there’s a lot more to supply chain optimization than just Inventory, Warehouse, and Distribution optimization – which, with the exception of e-Auctions, this article focusses on. If you truly want to see a big improvement, you have to start at the time a need is first identified and analyze the sourcing, procurement, and distribution cycles from the time the first requisition is placed to the time the last unit is delivered to the customer. You don’t know where your largest inefficiencies are or which single improvement is going to have the greatest impact – and often the largest impact comes from aligning your processes to insure the optimized award is received and delivered at the right time and at the right price. There’s no silver bullet – but a continual process of analysis and improvement will do wonders.

Can China Be Innovative? IBM Says “YES”

Can China be Innovative? I asked this question here on this blog about a year and a half ago after doing a fair bit of reading and research on the subject – which led me to the conclusion reached by Denis Simon of New York’s Levin Institute, that China risks becoming a good 20th-century industrial economy just when it needs to figure out how to be a 21st-century knowledge-based economy if it doesn’t move in the right direction.

The reason for this is that it takes more than a new science policy (as mentioned in the Economist article Something New: Getting Serious About Innovation, registration and subscription required), additional funding, a stemming of the “brain drain”, and a protection of intellectual property rights to build a knowledge-based economy – it takes a culture, and more specifically, a culture that fosters innovation, not conformity.

But it seems like IBM, who moved it’s global procurement headquarters to Shenzhen, China back in the fall of 2006, thinks that China is far enough down the road to open its first supply chain innovation center in Beijing. Dedicated to helping companies worldwide integrate and transform their global supply chain capabilities, the center will leverage the company’s expertise in supply chain research, business consulting services, software capabilities, and it’s own Integrated Supply Chain experience (which brought the company from the brink of bankruptcy in 1993 to a company that saved 6.2B in 2006) to create new solutions for companies around the world.

According to the press release, the Beijing Supply Chain Innovation Center will collaborate with companies to develop innovative solutions that include:

  • Virtual Command Center
    a SOA (Service Oriented Architecture) supply chain visibility solution that integrates and synchronizes supply, demand, and logistics information
  • Carbon Tradeoff Modeler
    that helps companies include carbon output foot-printing in their supply chain optimization efforts
  • Supply Chain Optimization
    tools and modelers that enable companies to design and operate agile and adaptable supply chain processes and networks

… and is available to be leveraged by any IBM client world-wide — immediately.

It’s a very interesting development. It means that the pockets of innovation are becoming larger and that China might be capable of accelerating down the innovation highway faster than one would expect. However, given that China, like India, contains a great disparity between its urban centers, which are rapidly giving rise to a new middle class, and rural areas, which are only beginning to taste the “new” China, it also means that China might be exacerbating some problems as it solves others. I don’t think we’re far enough down the road to make any calls yet, and this leaves me with my initial thoughts: it will be very interesting to see how this plays out over the next few years.

Spend Analysis: Another Book Review … And This One’s NOT Positive!

Pandit and Marmanis recently published a book titled Spend Analysis: The Window into Strategic Sourcing that has received a fair amount of praise from prince and pauper alike. Since I am currently in the process of co-authoring a text on the subject (now that my first book, The e-Sourcing Handbook [free e-book version on request], is almost ready to go to press), I figured that I should do proper due diligence, obtain their book, and read it cover to cover. I did – and I was disappointed.

Although the book would have been interesting ten years ago, good seven years ago, and still have some relevance five years ago, today it adds very little insight. In fact, the book is filled with fallacies, incorrect definitions, and poor advice.

Problems start to surface as early as the third paragraph (page 5) where the authors attempt to ‘simplify’ the definition of Spend Analysis, stating that “spend analysis is a process of systematically analyzing the historical spend (purchasing) data of an organization in order to answer the following types of questions“. There are at least three problems with this ‘simplification’:

  • Spend analysis is NOT systematic. Sure, each analysis starts out the same … build a cube … run some basic reports to analyze spend distribution by common dimensions … dive in. However, after this point, each analysis diverges. Good analysts chase the data, look for anomalies, and try to identify patterns that haven’t been identified before. If a pattern isn’t known, it can’t be systematized. Every category sourcing expert will tell you that real savings levers vary by commodity, by vendor, by contract, and by procuring organization — to name just a few parameters.
  • Good spend analysis analyzes more than A/P spend. It also analyzes demand, costs, related metrics, contracts, invoices, and any other data that could lead to a cost saving opportunity.
  • The questions the authors provide are narrow, focused, and only cover low hanging fruit opportunities. You don’t know a priori where savings are going to come from, and no static list of questions will ever permit you to identify more than a small fraction of opportunities.

From here, problems quickly multiply. But I’m going to jump ahead to the middle of the book (page 101) where the authors (finally) present their thesis to us, which they summarize as follows:

A complete and successful spend analysis implementation requires four modules:

  • DDL : Data Definition and Loading
  • DE : Data Enrichment
  • KB : Knowledge Base
  • SA : Spend Analytics

Huh? I don’t know about you, but I always thought that spend analysis was about, well, THE ANALYSIS! A colleague of mine likes to say, when aggravated, “it’s the analysis, stupid”. And I agree. A machine can only be programmed to detect previously identified opportunities. And guess what? Once you’ve identified and fixed a problem, it’s taken care of. Unless your personnel are incompetent, the same problem isn’t going to crop up again next month … and if it does, you need a pink slip, not a spend analysis package. DDL? Okay – you need to load the data into the tool – but if you don’t know what you’re loading, or you can’t come up with coherent spend data from your ERP system, you have a different problem entirely (again, you’re in pink slip territory). Enrichment? It’s nice – and can often help you identify additional opportunities, but if you can’t analyze the data you already have, you have problems that spend analysis alone isn’t going to solve. Knowledge base? Are the authors trying to claim that the process of opportunity assessment can be fully automated, and that sourcing consultants and commodity managers should pack their bags and head for the hills? Last time I checked, sourcing consultants and commodity managers seem to have no difficulty finding work.

So let’s focus on the analysis. According to the authors,

an application that addresses the SA stage of spend analysis must be able to perform the following functions:

  • web-centric application with Admin & restricted access privileges
  • specialized visualization tools
  • reporting and dashboards
  • feedback submission for suggesting changes to dimensional hierarchy
  • feedback submission for suggesting changes to classification
  • immediate update of new data
  • ‘what-if’ analysis capability

I guess I’ll just take these one-by-one.

  • Web-centric? If the authors meant that users should be able to share data over the web, then I’d give them this one … but the rest of the book strongly implies that they are referring to their preferred model, which is web-based access to a central cube. I’m sorry, that is not analysis. That is simply viewing standardized reports on a central, inflexible warehouse. We’ll get back to this point later.
  • They got this one right. However, the most specialized “visualization tool” they discuss in their book is a first generation tree-map … so maybe it was just luck they got this one right.
  • Reporting is a definite must – as long as it includes ad-hoc and user-driven analyses and models. Dashboards? How many times do I have to repeat that today’s dashboards are dangerous and dysfunctional.
  • Feedback submission for suggesting changes? There’s a big “oops!” Where’s the analysis if you can’t adjust the data organization yourself, right now, in real time? And if you have to give “feedback” which goes to a “committee” where everyone else has to agree on the change, which typically negates or generalizes the desired change – guess what? That’s right! The change never actually happens, or if it does happen, the delay has caused it to become irrelevant.
  • Feedback submission for suggesting fixes to the data? How can you do a valid analysis if you can’t fix classification errors, on the fly, in real time?
  • If the authors meant immediate update of new data as soon as it was available, then I’d give them this one. But it seems that what they really mean is that “the analysis cube should be updated as soon as the underlying data warehouse is updated“, but considering that they state on page 182, “in our opinion, there is no need for a frequent update of the cube” (note the singular case, which I’ll return to later), and then go on to state that quarterly warehouse updates are usually sufficient, I can’t give them this one either.
  • I agree that what-if analysis capability is a must – but how can you do “what if” analysis if you can’t change the data organization or the data classification, or even build an entirely new cube, on the fly?

The authors then dive into the required capabilities of the analytics module, which, in their view, should be:

  • OLAP tool capable of answering questions with respect to several, if not all, of the dimensions of your data
  • a reporting tool that allows for the creation of reports in various formats; cross-tabulation is very important in the context of reporting
  • search enabled interface

Which, at first glance, seems to be on the mark — except for the fact that the authors’ world-view does not include real-time dimension and data re-classification, which means that any cross-tabs that are not supported by the current data organization of the warehouse are impossible. Furthermore, it’s not the format of the reports that matter, but the data the user can include in them. Users should be able to create and populate any model they need, whether it’s cross-tabular or not. Finally, we’re talking about spend analysis, not a web search engine. Search is important in any good BI tool, but if it’s one of the three fundamental properties that is supposed to make the tool ‘unique’, I’m afraid that’s a pretty ordinary tool indeed.

The authors apparently don’t understand that spend analysis is separate from, and does not need to be based on, a data warehouse. Specifically, they state (on page 12) that “data warehousing involves pulling periodic transactional data into a dedicated database and running analytical reports on that database … it seems logical to think that this approach can be used effectively to capture and analyze purchasing data … indeed … using this approach is possible“.

It’s possible to build a warehouse, but it’s not a good idea for spend analysis. The goal of warehousing is to centralize and normalize all of the data in your organization in one, and only one, common format that is supposed to be useful to everyone. Unfortunately, and this is the dirty little secret with data warehouses, this process ends up being useful to no one in the organization, which is why most analysts simply download raw transactions to their desktops for private analysis, and ignore the warehouse. But the authors don’t stop there. In a later chapter, they go on to imply that the schema is very important and that selection of the target schema for spend analysis should be carefully chosen based on several considerations (page 177), namely:

  1. are your domains adequately represented?
  2. will your schema be evolving to support a centralized PIM system?
  3. is your company global? is internationalization an important requirement?
  4. is any taxonomy already implemented at a division level?
  5. has the schema been maintained in recent months?

To this, all I can say is:

  1. Doesn’t matter. What matters is that the analyst has the data she needs for the analysis she is currently conducting.
  2. Who cares? There should be no link between your PIM and your SA system. PIM is just another potential data source to use, or ignore, as your analysts see fit.
  3. Whatever. If you have a good ETL tool, you can define a few rules to do language and currency mapping on import.
  4. Irrelevant. We’re talking SA, not ERP.
  5. I would think it would have been, since the only way in the authors’ worldview to change spend data representations is to change the underlying schema of the warehouse!

The authors cheerily state (on page 14) that “a good commodity schema is at the heart of a good spend analysis program because most opportunities are found by commodity managers“. But hold on just a minute! If most of your opportunities are being found by your commodity managers using a basic A/P spend cube, then they’re limiting themselves to very simple low hanging fruit – which is picked clean in the first few months in a typical organization that makes a commitment to spend analysis. That’s why the traditional spend analysis value curve drops to almost zero within a year – meaning that if you don’t recover the cost of the effort in the first three months, you’ll never recover it. An A/P cube is just the beginning of the discovery process, not the endpoint.

The authors also make a strong argument for auto-classification, stating that (on page 100) “the reader must note that classifying millions of transactions is a task that should be done by using auto-classification and rules-based classification technology” and that “unless you license spend analysis applications, data scrubbing can be a very manual time consuming activity which requires a team of content specialists“.

Actually, nothing about rules-based classification mandates that the rules must be built by a robot, and there are many reasons why that can be a bad idea (not the least of which is the fact that robots are far from infallible). Classification rules can be built easily and effectively by hand … by a clerk … even in a very large organization with many disparate business units. Once built, this set of rules can then be applied in a fully automated way to every new transaction added to the system. So let’s not confuse “automation of creation” with “automation of application,” please. Of course, you do need a good, modern, spend analysis tool that allows for the creation of rules groups of different priorities, and you need a rules creation mechanism that’s easy to use and easy to understand.

Have you ever wondered why skilled consultants can build and map a spend cube to 90% accuracy very quickly? Well, here’s one tried-and-true “manual” methodology that builds terrific “automated” rules:

  1. map the top 200 GL codes
  2. map the top 200 vendors
  3. map the GL code + Vendor for vendors who sell you more than one item, or items in more than one category, depending on the level of detail you need

If you want to, you can get to 95-97% accuracy by extending to the top 1000 GL codes and the top 1000 vendors — if you really believe you are going to source 1000 vendors (and of course you’re not). To check your work, you’ll need to run reports that show you:

  • top GL’s and top commodities by vendor
  • top vendors and top GL’s by commodity
  • top vendors and top commodities by GL

Simply keep mapping until all three reports are consistent, and you are as accurate as you’ll need to be — and you’ll have the advantage of having built your own mapping rules, that you understand. The alternative, which is error-checking the work of an automaton (a process that must be done, because no robot is perfect), is difficult, tedious, and error-prone — and it must be repeated on every data refresh.

When the authors state (on page 116) that “manual editing is sufficient, but it is also extremely inefficient … it is not scalable with respect to the size of the data“, this is flatly untrue. The creation of dimensional mapping rules is wholly unrelated to the volume of the transactions — the same effort is required for 1M transactions as is required for 100M, and most spend datasets can be mapped very effectively with dimensional rules only. The only exception is datasets whose only component is a text description; and here, too, the authors’ “scalability” argument falls apart, since human-directed phrase mapping can divide-and-conquer quite effectively.

To top it all off, the authors go on to violate the first rule of spend analysis, which is “NEVER, EVER, EVER EXCLUDE DATA”. They take great pains to classify all of the errors that can occur in the ETL process and then bluntly state that (on page 109) “if you have errors in category iv (root cause is undocumented and cannot be inferred), then you have two alternatives … the first alternative, if possible, is to exclude these data from your sources … errors of category iv are unacceptable and could jeopardize your entire analysis … so they should be eliminated“.

No, NO, NO! YOU MUST ACCEPT ALL OF THE RECORDS and YOU MUST DO SOMETHING SENSIBLE with the records that don’t fit into your notion of reality. For example — create a new Vendor ID, and family it automatically under Not Found, or Missing. Dropping data jeopardizes your analysis much more than creating an “Uncategorized” or “Missing” data node. What if errors represent 15% of your spend? Then you’d be reporting that you are spending 85M on a category when you are spending 100M. Your numbers won’t add up … and when the CFO files a SEC filing on data that is later found to be incomplete by the auditors, guess whose head is going to roll?

And before I forget, let’s get back to that web-centric requirement where the authors imply that all of this means web-based access to a central cube (singular case). Throughout the entire book they refer to “the cube” (such as when they state that “in our opinion, there is no need for a frequent update of the cube“) as if there’s only ever one cube to be built. Turns out there isn’t just one cube to be built — there are dozens of cubes to be built. Some power analysts build 30 or 40 commodity-specific invoice-level cubes (what are those? you won’t learn that from Pandit and Marmanis), and regularly maintain a dozen of these every month — not every quarter (as the authors recommend).

The only real hint that the authors give that multiple cubes might be useful is where they state (on page 51) that “some companies are taking the approach of creating different cubes for different uses, rather than packing all possible information in a single cube for all users … for example, all users might not be interested in auditing P-Card information … rather than include all of the details related to P-Card transactions in the main cube, you can simply model the top-level info (supplier, cost center) in the main cube … then … create a separate ‘microcube’ that has all of the detailed transactional information … the two cubes can be linked, and the audit team can be granted access to the microcube … the microcube approach can be rolled out in a phased manner“. Or, in short form, you can have multiple cubes if you have too much data, and the way you do it is to create ONE main cube, and then micro-cube drill-downs for relatively non-important data. I don’t even know how to verbalize how wrong this is — it completely inverts the value proposition. (Now, to be fair, they also state that “ideally, the cubes should be replicated on the user machine for the purposes of making any data changes“, but they give no definition as to what form these cubes should take or what changes are to be permitted, so we are left assuming their previous definition, which is secondary micro-cubes and only minor, meaningless, alterations, since the dimensional and rule-based classifications require “approval”).

At this point, you’re probably asking yourself – did the authors get anything right? Sure they did! Specifically:

  • Chapter 4 on opportunity identification had a good list of opportunities to start with. Too bad most of them are the low-hanging fruit opportunities easily identified with out-of-the-box reporting and that there’s no real insight on how to do serious untapped opportunity identification when there isn’t a pre-canned report available.
  • Chapter 5 on the anatomy of spend transactions had a good overview of the formats used in various systems … but if you’re a real analyst, you probably know all this stuff anyway.
  • Chapter 7 on taxonomy considerations had good, direct, simple introductions to UNSPSC, eOTD, eCl@ss, and RUS. It’s too bad these schemas are relatively useless when it comes to sourcing-based opportunity identification.
  • When the authors pointed out (on page 8) that there is still widespread low-adoption of spend analysis, they are correct … but when they state that it’s because we’re talking tens or hundreds of millions of transactions, it’s irrelevant and wrong. For any specific analysis, there’s probably only a few million or tens of millions of transactions that are relevant, and a real spend analysis tool on today’s desktops and laptops can operate on that number of transactions without issues. There is no need for a mainframe.
  • When they state that the categorization of errors is critical because not all errors are equally costly to fix, they’re right … but the data warehouse is irrelevant. Just add a new mapping rule and you’re done. Two minutes, tops. What’s the big deal? Oh, I forgot — in the authors’ world, you can’t add a new mapping rule on the fly.

To sum up, when the authors state in their preface (on page xv) that “if implemented incorrectly, a spend analysis implementation program can become easily misdirected and fall short of delivering all of the potential savings“, I wholeheartedly agree. Unfortunately, the authors themselves provide a road map for falling short.

Don’t Wait for the Burning Platform (Start Your Procurement Transformation Now)

Today’s guest post is from Robert A. Rudzki, a former Fortune 500 senior executive of supply management who now advises other companies through Greybeard Advisors LLC, a strategic management consulting firm. Bob has authored several business books including the critically acclaimed Beat the Odds: Avoid Corporate Death and Build a Resilient Enterprise and Straight to the Bottom Line. Bob also writes the Transformation Leadership blog for the Supply Chain Management Review. Bob can be reached at rudzki <at> greybeardadvisors <dot> com.

 

A few years ago, US financial institutions were making so much money that their procurement departments were having great difficulty. They could not get any serious time commitment from their executive staff to discuss procurement and supply management opportunities.

I know that’s true, because I heard it directly from several chief procurement officers at insurance companies and banks, who approached me after I made a presentation on the West Coast. These CPOs were, to state it mildly, very frustrated in their jobs and with their senior management. They had a sense that there was real opportunity, but couldn’t get their senior management’s attention.

Today, the executives of many of those same companies probably wished that they had started paying attention to procurement and supply management back when they did not NEED to. In fact, the best advice for senior management, including senior supply management, is this: don’t wait until you are standing on a “burning platform”. Start the procurement transformation process now.

It may be easier, in some corporate cultures, to tee up a business case for change when things are going poorly; for example, when your company is on a “burning platform”. It’s a real sign of good leadership, and forward-thinking management, however, to decide to transform when you have no immediate urgency to do so.

One of the implicit challenges in building a case for procurement transformation in financial services is the atypical cost structure. Where are the direct materials (other than people) – that typically occupy center-stage in strategic sourcing? To a manufacturing eye, the banking industry cost structure appears strange – essentially all people and the so-called indirect spend. But, as some of you may know, indirect spend offers a larger percentage cost reduction opportunity – often well above 15% – when addressed with a robust strategic sourcing and negotiations management process (“SSNM” in Greybeard Advisors’ parlance).

Several of my colleagues at Greybeard Advisors have deep experience applying strategic sourcing in the financial services industry. The benchmarks from their experiences confirm the enormous potential to impact the bottom line at financial services companies.

Similarly, we have applied strategic sourcing in numerous “non-traditional” areas of spend at manufacturing companies, including spend for financial and marketing services. There are sizeable percent cost reduction opportunities – again, if approached with a genuine SSNM process.

Opportunities abound – but they don’t just happen by putting numbers and analyses on a PowerPoint chart. It takes real leadership, and a carefully thought-out transformation roadmap.

Thanks, Bob!