Category Archives: Market Intelligence

Why Big Brains Will Beat Big Data in Procurement


Today’s guest post is from Ryann Kahn, Marketing and Communications Manager at Source One Management Services.

Two weeks ago, the Sourcing Innovation blog published an article about how the three cognitive traps stifle global innovation. I couldn’t help but think about how the same points could be made in procurement: data (though usually we don’t have Big Data) can help overcome some common issues, but ultimately Big Brains are more important and effective at the same job.

Take a procurement sourcing project for example.

The commonly used traditional three-bid process is data driven. It implies that if you collect enough (i.e. three pieces of) data, then you are making a good decision. Now, by collecting three bids, you know you are not getting the worst price and service out there and you are encouraging some competition. But without category expertise or a strategic process in place, can you really consider the data alone enough to justify that you have made a wise and innovative decision? Data != strategic sourcing.

But the data itself can often lead to the confirmation bias that the doctor referenced previously. Was your RFP template (or software solution) structured in a way that drove you to the conclusion that you already had envisioned? For example, if you want to remain with your local incumbent supplier, does your scorecard penalize suppliers for not having a location near you? Did you only request pricing on a specific product, which you knew your preferred supplier had the best (or only) price available? Confirmation bias in the sourcing world is real and common. Many companies effectively eliminate competition with better solutions because of the way they structure their questions.

By contrast, a true strategic sourcing process uses data in a Big Brain process.

The first step is a spend analysis of data from contracts, supplier invoices, P-Cards, supplier reports, POs, and more. (That’s a lot of data.) Then we look at market intelligence, historical trending, new products or process enhancements and benchmark data. (Now that’s Big Data!) All of this information is pulled, assessed, and analyzed. But the data alone does not give a full picture of a company’s spend. It takes the “curious, open mind” to uncover the whole story. Data may suggest inadequacies, but only through more in-depth research and thorough interviews with stakeholders and end users will one be able to identify problems and usage requirements.

The next step of the strategic sourcing process is the sourcing strategy. Again, it begins with data collection to cast a wide net of suppliers and determine their capabilities. But the bulk of the work in this stage belongs to the Big Brain: creating the supplier strategy, envisioning an RFx strategy, and planning for an execution strategy.

Even if procurement evolves to join the Big Data bandwagon, data will never be able to replace a human category expert. A category expert comes armed with nuanced knowledge of market trends, characteristics, players, and history, and uses analytical skills to apply that to plain data. Or, as the HBR article says, “When we look at markets different from our own we often have little information”. An expert who has been intimately involved with sourcing a category for years will be able to achieve better results than a novice armed with data, or the most powerful e-sourcing tool, any day.

In the final phases of the strategic sourcing process, implementation and compliance, it is entirely the work of a Big Brain. Experts must ensure that a company is actually achieving the results that were identified in the earlier phases in terms of savings and level of service. These experts may use tools to help collect the data to support the process, but the tools themselves don’t do an adequate job of capturing the data that is important to the unique organizational situation.

Data can, and does, help make good sourcing decisions, but ultimately it’s the Big Brains that lead the way. A Big Brain will always be needed to strategically apply the data (big or small), and be the “curious, open-minded researcher” to make a good decision.

Thanks, Ryann.

MarketMaker4: The Mid-Market’s Market Making Mezzanine

Some of you might say the e-Sourcing space is too crowded. And that certainly was the case in the mid-zeroes — platforms here, platforms there, platforms platforms everywhere. But then came the acquisition frenzy where mid-sized players swallowed smaller players and start-ups before getting swallowed up in turn by the dominant players who, in the last couple of years, themselves were swallowed up by the massive enterprise software providers. As a result, there is an opportunity, especially in the NA (North American) market for a couple of new players – provided, of course, that such players bring new and innovative solutions to the table (that address the needs of a considerable market segment).

As a result, even though the EU (European Union) vendors are starting to enter the sourcing market in a big(ger) way in NA, there is still an opportunity for someone new if they go about it the right way. So, despite the fact that many thought the market almost dead at the end of the zeroes, it was not completely crazy that a small team of e-Sourcing market veterans, including Mr. Alan Buxton who was the CTO of of Trading Partners back in their heyday, decided in 2011 to start a brand new e-Sourcing software start-up and build a new solution from scratch.

Two years later, MarketMaker4 is a strong offering for the mid-market that needs a new, modern, e-Sourcing solution. In particular, those mid-market companies that are late to the e-Sourcing game, those that are still relying on third parties to manage their sourcing events and are ready to bring those events in house, those that are trying to use ERP sourcing solutions, and those stuck on platforms that, due to acquisition, are stuck in integration limbo and haven’t been upgraded in a while.

So what is MarketMaker4? It’s a four-part sourcing solution that consists of:

  1. A modern e-Negotiation platform
    with a best of breed e-Auction and RFX solution
  2. with a built in supplier discovery engine
    built on the entire D&B database which is augmented with your own supplier database
  3. and market data indices that span commodities and currencies
    that let a buyer know current prices, historical trends, and relative market conditions (when the data is available)
  4. that is augmented with real-time product and sourcing support 24/7/365
    through online chat that connects all of the global support representatives around the world that are currently online.

The MarketMaker4 founders, who were involved in the space for over a decade and who worked for both software providers and services providers learned the following:

  1. While some companies will start with services to get going, these companies will eventually decide that pay per drink is expensive and look for software.
  2. The companies switching from services to software will typically select a best-of-breed software provider with little or no services or support beyond the product. As a result, due to limited sourcing and product knowledge on the in-house buying team, the product typically gets under-utilized and the company fails to achieve the ROI they expected.
  3. When the license expires, the company will typically revert back to a pay-per-drink, but limited to high-value categories, or put its faith in a good e-Procurement system, that will reduce maverick buying and, hopefully, with limited RFX capability, lead to better buying habits.
  4. But even if the company moves to a modern e-Procurement system, the company will typically have little insight into current prices or suppliers that they aren’t already buying from.

As a result, the team decided what was really needed for these types of companies was:

  • An e-Sourcing solution that was easy to use by the average buyer,
  • augmented with real-time support and guidance as new buyers get up to speed,
  • integrated with market index and currency index data (that could be linked into cost models), displayed in easy to understand graphical representations, that the buyer could use to understand current prices and likely trends, and
  • extended with a huge database of potential suppliers.

And that’s what they built. And in each component, they added some innovation to the mix.

  • The e-Auction product, which consumerizes the enterprise capability, is one of the most powerful on the market, with one of the most sophisticated, but yet easy to understand at a glance, interfaces out there.
  • While chat-based, they chose to build a support solution that connects all of their services and support personnel around the world who are currently available rather than use a call-center model. (And as they were just acquired by Xchanging, one of the big players in the Procurement market who are leaving MarketMaker4 as a stand-alone product and company, they now have a large network of support personnel around the world who speak multiple languages and can support their customers in their native language.)
  • Their market index solution is linked into the RFX/Auction module so that the buyer can see current component / raw material prices if there is a market index and can see current currency values as well as trends over the last year for every currency a supplier might bid in.
  • And they were the first e-Sourcing platform to integrate with D&B for the purposes of supplier discovery. Up until they did, most integrations were for risk data or data enrichment. As a result of their partnership and early efforts, they have one of the more powerful integrations and in addition to being able to search on name, location, etc. they can also filter on a variety of dimensions, including risk, size, diversity, etc. that even D&B can not filter on through their API.

MarketMaker4’s e-Auction product and supplier discovery products in particular are quite innovative, and SI will dive into them in more detail in the new year.

Still Not Convinced You Need Your Invoices Under Control?

Then, for starters, maybe you need to dwell on the following:

  • You’re probably overpaying your suppliers by 1%
    because that’s what an audit recovery firm expects you are, and why they make a killing auditing the 20% of suppliers that constitute 80% of your purchase volume, because it’s not that hard for these specialist firms to identify overpayments of 0.5% that typically generate 500K or more in a gain share agreement for a few man months of work (or less)
  • There’s a 2 in 3 chance you are being defrauded of 2% of your revenue
    and that you’ll never notice because you aren’t able to verify all invoices
  • Up to 75% of your AP-related overhead is completely wasted
    on manual data entry, supplier inquiries, and other tactical work that adds no value to Finance or Procurement
  • At least 1 in 10 invoices have an error
    which could be as simple as missing payment information or as involved as incorrect pricing on every line item for a 200 line Bill of Materials because the supplier forgot to apply the discount
  • One Million invoices requires at least 100 standard 4-drawer filing cabinets
    if you get 1 M invoices a year, after 10 years, that’s 1,000 filing cabinets — where are you going to store them all??? (With today’s Storage Area Network densities, that’s 1 SAN. Which can be replicated in 3 places at almost zero cost compared to the seven figure cost of replicating and storing 10 Million invoices at three different locations.)

SI could go on, but the reality is that, especially if your organization is growing, it really, really needs to get its invoices under control. To find out how it can do this, download SI’s new white paper on An End-to-End Invoice Automation Framework Benefits & Best Practices, sponsored by Nipendo. (Registration required.) Once you understand the requirements for a true end-to-end invoice automation solution, you will be well on your way.

Is the Emerging Share Economy Going to Disrupt Your Procurement Practices?

My Purchasing Center recently ran a very interesting article from a Senior Consultant of the Hackett Group on “Considerations for Supply Chain and Procurement in the Share Economy” that did a great job of explaining how the Share Economy is disrupting consumer purchasing patterns, and thus demand. However, in SI’s view, it did not do as great a job when it came time to make the case that it would disrupt daily Procurement operations.

In SI’s view, while the share economy may change the approach to certain categories, it’s not going to change fundamental procurement processes, methodologies, or the best practices that a leading Procurement organization brings to the table. We will elaborate on this, but first let’s review the main points of the My Purchasing Center article.

Noting that the share economy is projected to reach 3.5 Billion this year, with no signs of slowing down, the author of the My Purchasing Center article posits that these trends are going to have a significant, innovative, and potentially disruptive impact on Supply Chain and Procurement.

Zeroing on on services like Lyft and Airbnb where legions of people use their own car or living space as an on-demand taxi-service or rental, the author notes that this reduces the demand for additional cars and short-term rental properties. Similarly, services like zip-car, where people can rent on demand, not only reduce the demand for taxis and limos, but for second vehicles altogether, and thus reduce the total demand for vehicles from a manufacturer. This can effect economies of scale, and increase the cost of each vehicle produced if the demand drop is significant.

Then there is the emergence of 3D printing that is now to the point where even non-engineers can assemble a 3D printer, download some software, and produce their own goods at home. When the cost drops, demand for products that can be just as cheaply printed at home may drop but, more importantly, demand for products that can be printed in bulk just as cheaply as needed on the shop floor could wipe out entire categories for a supplier.

And these are valid observations. Demand is going to change, and shift, and it’s going to have an effect on what an organization can and can’t sell and on what a supplier can and can not profitably produce. No argument there.

But, unless it takes us back to a barter economy, it’s not going to have much of an impact on a good Procurement organization. The first thing a good Procurement organization does when it starts a sourcing event for a category is analyze the category in depth to determine the demand for the product or service, the criticality of the product or service, the strategic nature of supply relationships in the delivery of the product or service, etc. to determine what supply strategy is the most relevant, how the sourcing event should be conducted, what technology should be brought to bear, etc. If demand has dropped 50% in a category since it was last sourced and the economies of scale have diminished, then sourcing is going to shift from a lowest TCO approach to a strategic relationship where it can work with the supplier to take cost out of the production or delivery process or, if necessary, innovative a new design that will allow it to use lower cost materials and production / delivery processes. With or without a share economy, the mandate, and function, of Procurement is the same — source each category in the manner which generates the most value to the organization and procure each part or service against the identified strategy.

Do you think SI is missing something? If so, leave a comment.

SI Still Prefers Big Brains to Big Data, But If In Big Data You Trust …

… then trust fully and completely as the one thing that Big Data can do (besides sucking up a lot of your cash for a dubious ROI), when properly mined, is overcome the Three Cognitive Traps that Stifle Global Innovation.

How? We’ll get to that, but first let’s explain what the three cognitive traps are.

The Experience Bias

Referred to as the availability trap by the authors of the HBR post, it refers to the fact that many people assume an element of a culture that they, and their peers, are familiar with is representative of that culture. The example given by the authors is that Brits see Chicken Tikka Masala as representative of typical indian cuisine as that is what is common in the British curry houses. Similarly, most North Americans think that fried rice and egg rolls are representative of typical Chinese cuisine, as that is what comes with just about every combination plate in every Chinese restaurant. In both cases, this is not true.

Similarly, in the business world, most executives in the developed world believe that the urban, affluent, rapidly growing middle class is representative of the population of a market in a developing country they are going after. The urban middle class is only a small percentage of the population in many developing countries, and not representative of the market as a whole.

The Confirmation Bias

The confirmation bias is when we use ambiguous data as clinching evidence of our hypothesis. The example given by the authors was how a European multinational, despite being told by their Indian salesforce that their building material product line was over-engineered for much of the Indian market, refused to listen (believing that the local salespeople just did not have the required skills to sell the product) and sustained years of losses, including two country CEOs, before it saw the error of its ways. Why did this happen? Early on, a global product executive visited India and happened to be present when a single, stellar, salesperson encountered a high-value customer, patiently demonstrated each and every superior product feature, and, after a significant amount of time, finally made the sale. This one data point of success was focussed on despite the fact that there were countless data points of failure.

The Variance Bias

The variance bias (known to psychologists as out-group homogeneity bias) is where we (drastically) underestimate variance in distant cultures, grouping all Chinese consumers into one market segment, for example, while separating New Yorkers and San Franciscans into two completely different market segments (due to our familiarity with both market groups and in-depth knowledge about their differences relative to our understanding of the Chinese marketplace). (There are 1.35 Billion people in China. Do you really think they are all the same? There should be considerably more market segments in China than in the US.)

With enough data, and the willingness to blindly trust the data, Big Data will overcome all three of these biases. Since big data relies on transactions and facts, and not the limited pool of experience associated with a decision maker, the real patterns (and not the perceived one) will quickly emerge. Similarly, outliers (such as the example of the stellar salesperson who got lucky) will quickly be eliminated and bad hypotheses will not be confirmed. Finally, a good algorithm won’t group data that doesn’t belong together and if it takes 25 clusters to properly segregate the data, the algorithm will return 25 distinct clusters for human analysis and interpretation.

However, as you probably guessed, SI doesn’t believe that you need Big Data to overcome these traps. Big Brains will suffice. As the authors note, a curious open mind who does her research can overcome each of these traps. An open mind who realizes she doesn’t know much about a market and dives into it, reviewing local research and local media, will get a more realistic perspective than one which makes snap decisions based upon his limited experience. An analytical mind that looks at the ratio of success to failures in sales efforts will quickly see that the close rate is way too low, something is wrong, and a thorough investigation is needed. And an inquisitive mind that asks if the market has been thoroughly covered will realize that studies and data that only cover urban centres don’t cover the population as a whole and additional research into non-urban lifestyles, tastes, and buying patterns is needed if the company wishes to reach that market.

Big Data is needed if you want a reasonable chance of accurately predicting the weather beyond the next 24 hours, modelling stresses on a spacecraft under different adverse conditions, or brute-force breaking the SHA-256 algorithm. It’s typically not needed to get a good handle on a potential market and good product design. Brains effectively put to use will do just fine in these situations, as they have for hundreds of years.