Bid better: Improving procurement decisions with smarter software tools
Across any industry, procurement and partnership management can be a challenging proposition. The data behind traditional supplier partnerships can be quite convoluted and chaotic. Thankfully, analytical tools have evolved, making navigation through this data much easier to accomplish.
Not so long ago, the first spreadsheet programs appeared on the scene for people who had to work with large data sets. At the time these applications were amazing, shiny, wonderful and new. The idea that we could sum large columns of numbers, update individual figures without having to recalculate everything manually and use the copy-and-paste function was revolutionary for those of us who were accustomed to doing calculations by hand or calculator. These programs seemingly could handle anything we threw at them.
Today’s information, however, is fed by numerous sources, and in exponentially larger amounts. It no longer can be effectively managed or analyzed using spreadsheets. Big data needs a different, equally revolutionary toolset.
I had this epiphany about two decades ago when working as a materials manager in an automotive electronics manufacturing facility. The year before, I had been fortunate to hire a printed circuit board (PCB) buyer who had really exceptional negotiation skills at a time when PCBs represented a major spend category for organizations.
We sent the buyer, Jim, off to Japan for three weeks accompanied by a team from our headquarters and other facilities to negotiate with our principal PCB suppliers. This period predated Skype and even the availability of universally accessible email, and for a time, I didn’t hear much about the negotiations while waiting on pins and needles until the team returned.
The team shared the positive news upon its return: “It went great. We saved a lot of money,” said Jim.
“Wonderful,” I replied. “How much?”
But he couldn’t answer the question, at least not at that moment. I asked the same question again the next day—and again the next day. The buyer was still working on it. And so I continued to ask the next day, and then the next day, and the next.
Turns out, the size and complexity of the bids were preventing the buyer from obtaining an answer. Different suppliers had boards in different sizes, functioning at different efficiency levels and operating at different realms of cost-effectiveness. All of these factors were rapidly moving targets; they changed in the moment depending on new equipment hitting the market, other customers consuming inventory and so on.
Automotive customers are very quality conscious, and each of our suppliers’ products need to be qualified and approved prior to using them. Clearly, you can begin to see our conundrum. And compounding all this complexity were specific discount factors, or expressive bids, which can be thought of as “I’ll give you a better price, if you…” statements such as these examples:
- Supplier 1 offered to discount an additional 5 percent if we committed to purchasing at least n square meters of PCBs.
- Supplier 2 offered to discount the price on one type of PCB if we committed to purchasing at least 80 percent of our requirements for another type of PCB.
- Supplier 3 offered an exceptional price on a number of PCBs that it was not currently qualified to provide us.
- Supplier 4 offered to discount a high-volume PCB as long as we redesigned the board slightly so it would be a better fit on the panel.
- Supplier 5 offered to continue to provide specialty PCBs that only it was willing and able to produce, at no discount. In this case, we had to be willing to give them a portion of the business on other PCBs—from suppliers 1, 2 and 3—that it would discount, and this supplier would also improve flexibility on the specialty boards.
Jim was evaluating all this information, trying to determine the best overall purchasing scenario. He plugged different strategies into spreadsheets, hoping to find the one strategy that was most cost-effective and satisfy the suppliers’ bid requirements. But he found that using spreadsheets to arrive at the best scenario really wasn’t possible—it wasn’t then, and it isn’t now.
Not only was there too much data, but also far too many variables were in play to be able to model them on spreadsheets. What did we end up doing? We made the most of what we had and came up with some pretty good combinations. But we never knew—truly knew—how much money we were leaving on the table by making what we did know to be nonoptimal decisions. If we’d had better tools, we may have had better options to consider, but those tools didn’t yet exist.
A decade later, several companies rose to fill that void—including Emptoris, which is now an IBM software offering. This solution took form as Internet negotiation software powered by optimization engines. Integrated optimization enables finding the optimal total cost by taking into consideration not only price, but also nondirect costs and requirements such as the cost and risk of qualifying a new supplier for a PCB. Optimization allows us to work with big data sets. By using Emptoris, and other tools like it, our sourcing teams were able to review hundreds of complex what-if analyses in a matter of minutes, and they were able to find the best-fitting scenarios.
To work with big data at the time, we had to purchase and install a software package. Now, users have a wide range of options. PCs have become much more powerful, and calculation engines—and data sets—have moved into the cloud. The ability to pull meaningful information out of big data sets, and make informed decisions based on that data, has improved to the point where excuses for leaving money on the table no longer suffice.
Why should you care? Consider this simple example I’ve used: supplier A quotes $1.20 for a product, and supplier B quotes $1.10. Which quote is better? If that information is all the data you have, clearly you’d select supplier B. But what if the two suppliers break down their quotes this way:
As you can see, if sourcing raw materials for anything less than $0.70 is possible, supplier A becomes the more cost-effective option. The more data that is at your disposal, the more you can understand that data and make well-informed decisions.
If your supplier negotiations result in complex, expressive bids, taking advantage of big data–enabling contract management tools such as IBM Emptoris Contract Management may be worthwhile for you. It offers built-in optimization that helps procurement organizations enhance performance, capture sustainable cost savings, mitigate risk, improve compliance and directly impact financial performance.