By Malcolm Wheatley
May 3, 2013
Procurement is big business—and produces big data. Manufacturer or bank, government agency or restaurant chain, every organization spends significant amounts of money buying products and services. Raw materials, utilities, office equipment, stationery, and services such as legal advice, advertising, and facilities management. Roll it all together, and the amount is always impressive, equating to a significant percentage of overall revenues.
But what, precisely, is all that money spent on? In the typical organization you’ll often struggle to find detailed answers. Hence the growing appreciation of the role played by procurement analytics in shining a spotlight into areas of expenditure that are often murkier than might be desired.
“Analytics tools have advanced and improved significantly—but their usage within procurement remains extremely limited, and their purpose isn’t clearly understood,” says Mickey North Rizza, a former Gartner analyst and vice-president of strategic services at Chicago-based BravoSolution, a supply chain consulting and services provider. “People can struggle to see the value in it.”
Yet in the right hands, say insiders, that value is undoubted. For by obtaining a clear picture of what they are spending their money on, the prices they are paying, and the suppliers they are dealing with, organizations can concentrate their spend where it is most effective.
In short, the use of procurement analytics unlocks the opportunity to:
• Reduce costs by consolidating expenditures on fewer providers, thereby exerting greater leverage in purchasing negotiations.
• Avoid wasteful expenditure through over-specification—where materials are ordered to a higher standard or specification than is actually required—and the use of ‘off-list’ non-preferred suppliers.
• Improve buying efficiencies by enforcing compliance with pre-agreed pricing, discount, and volume-based price break structures.
The Data Forensics Problem with Procurement
In any discussion of procurement analytics, it’s necessary to begin with explaining why procurement analytics is necessary in the first place. How come organizations don’t simply know what they buy, in the same way that businesses know what they sell, and who they sell it to?
There are several problems, say experts.
One is organizational: in the typical organization, many people and functions hold the purse-strings. Consequently, a business’s professional buyers—and their buying systems—aren’t always involved in sourcing decisions, and don’t always capture spending data.
Legal advice, for instance, is often bought directly by the in-house legal team, who prefer to deal with lawyers they know, rather than lawyers selected by buyers. Marketing, likewise, often has a preference for particular marketing and advertising agencies, figuring that the cheapest agency may not be the best or most creative. Ditto for expenditures on maintenance, facilities management, janitorial services and consulting firms.
Coding and classification is another problem. Product designers and engineers have a tendency to describe the same thing in multiple ways. To an analytics tool, “Bolt, steel, 8mm diameter” is not intuitively the same as “Steel bolt, 8mm dia” or “Bolt, 8mm dia, steel.” Likewise, the same item may be represented by several product codes, depending on where it is used. Such discrepancies are like petri dishes for dirty data that inhibits insight.
“What underpins effective procurement analytics is a clean dataset,” says Alun Morris, a senior e-sourcing consultant at Wax Digital, a global e-procurement provider based in Cheshire, U.K. “It’s all about cleansing data to a standard, cross-referencable taxonomy, whatever that taxonomy may be.”
At Tata Steel, for instance, a global rollout of procurement analytics based around SAP Business Intelligence found anomalies such as an enormous over-supply of crane wheels, intended as spare parts for the overhead gantry cranes at the company’s steel mill at Port Talbot. The wheels had been ordered under different part numbers, it turned out, and by different ordering systems.
Their collective value? A million dollars, reports Nicholas Reeks, then director of procurement development at Tata Steel Group, and now a director within the IT function, responsible for delivering analytics to sales, marketing and procurement.
“We just kept reordering—until the business intelligence application showed us that we didn’t need to,” says Reeks.
Mergers and acquisitions pose another challenge. Here, GEC-Marconi, a British-based internal defense and telecommunications company, which was an early adopter of procurement analytics, is something of a poster child. Possessing no fewer than 168 separate materials requirement planning and procurement systems across its sprawling empire, the company routinely found itself buying the same electronic component from multiple suppliers under multiple part numbers.
While 168 separate systems may be something of an extreme, even two or three ERP or procurement systems can lead to situations where enterprises unknowingly spread spend over multiple suppliers, failing to grasp the opportunity to consolidate spend onto one product code and one supplier.
That said, procurement analytics is about much more than spend consolidation, and applying leverage on suppliers to reduce prices. Proponents of procurement analytics point to three separate areas where better insights into procurement practice can illuminate ways to achieve lower prices, better discounts, better payment terms—or some combination of all of them.
Use case No. 1: How well do we buy? Purchasing doesn’t take place in isolation. Underpinning vast numbers of transactions are price agreements, contracts, internal guidelines and service standards.
But how well do actual purchases correspond to these supposed norms? Is the organization meeting its goals in terms of consolidating around individual preferred products and vendors? Are employees buying from preferred vendors—or is ‘maverick procurement’ rife? How well do achieved prices correspond to the standard costs built into costing models for budgeting and profit estimation purposes? How long is the procure-to-pay cycle—by category, vendor, and business unit?
In short, through a combination of aggregating individual transactions and trawling through individual purchases comparing actual buys with contracted terms and conditions, procurement analytics applications shine an uncompromising light on non-complaint behavior.
And the opportunities are self-evident: a greater degree of control over the procurement process, and a closer match between negotiated contracts, standard cost assumptions, and actual expenditure.
Use case No. 2: What do we buy? Armed with the right information, there’s a lot that a savvy buyer can do to reduce costs. In terms of conducting dialogues with suppliers, for instance, there’s often scope for consolidating volumes of similar materials or services onto fewer suppliers. Or bundling together different items into ‘packages’ of materials or services, for which suppliers offer prices as a whole.
Internally, there are also dialogues to be had. The specifications and grades of purchased materials can be examined for opportunities to purchase lower-specification items which will still be fit for purpose. It’s also usually worth checking to see if there’s maverick spending taking place, where individuals within the organization are placing orders on non-approved (and more expensive) suppliers.
The challenge? Getting the right information to inform those dialogues—which is where procurement analytics comes in. But without basic data on what is bought, from which suppliers, at what prices, and on what terms, it’s difficult to get beyond first base.
“You have to go back to the basics,” sums up Jeff Nielsen, a 30-year procurement veteran with a stint as CIO of a hospital in San Francisco. Nielsen now works as a procurement suite service manager with UNIT4 (formerly Coda), a software company based in Harrogate, U.K. “You start with a very fundamental question: Are you getting value for money from what you’re buying? And from that, everything stems—what are you buying, who are you buying it from, and what are you paying?”
Use case No. 3: How well do our suppliers perform? Sourcing exercises—and the procurement contracts which stem from them—are complex things. They stipulate prices to be paid, quantities to be bought, expected levels of quality, invoicing and payment terms, discount levels, volume-based price breaks and a wealth of other stipulations to which suppliers should adhere.
But when it comes time to execute these contracts, do the suppliers in fact adhere to them? And if not, is that non-adherence costing money? Are discount levels not being applied, or volume-based price breaks being ignored? Do invoices contain an inappropriate level of errors? Do suppliers routinely deliver late, or ship only partial quantities?
Yet again, without procurement analytics, it’s difficult to build up a picture of supplier performance.
And performance can cover more than simply the mechanics of buying. Increasingly, companies want to uncover the environmental impact of their supply chains, leading to specialist environmental-centric reporting products, such as Ecodesk.
“We are increasingly being asked about the environmental credentials of our products,” Matt Wilson, supply chain sustainability leader at pharmaceutical manufacturer GlaxoSmithKline. “The challenge is working with procurement and our suppliers to help them understand the role they play in our value chain environmental impact. Glaxo has chosen to report its carbon, water and waste emissions openly via Ecodesk, and we are asking our suppliers to do the same.”
Stated simply, out-of-the-box ERP is becoming better at offering basic procurement intelligence. Analyzing spend according to an individual vendor, for instance, won’t be a problem. Spend by budget-holder, too, is routine. But spend by budget-holder and vendor? And spend by budget-holder and vendor at item level? At some point, ERP gives way to the need for something more substantial—a need that is further fuelled by the complications when businesses have multiple ERP systems, or multiple instances of a single ERP system.
For pure spend analysis, businesses typically prefer a dedicated spend analytics package, sourced from one of a number of specialist suppliers. On-premise or cloud-based, these are often sold with the promise of delivering results more quickly than could be possible with a ‘full strength’ business intelligence solution.
That said, the difference is a question of labeling, agree most experts.
“Spend analysis is simply a flavor of business intelligence,” says Wax Digital’s Morris. “A procurement-centric flavor, to be sure, but a flavor nonetheless.”
And spend analytics, in its purest form, is precisely that—while business intelligence software packages can include within their analyses elements of data sitting well outside traditional spend analysis systems, such as supplier performance data.
“Go beyond simple spend data, and you can capture supplier metrics such as supplier performance metrics—delivery ‘on-time and in full’ figures, invoice accuracy, contract performance, product quality data and so on—and consequently make better sourcing decisions,” says Morris.
In the end, suggests Tata Steel’s Reeks, the ultimate decision rests on the scale of the procurement analytics challenge faced, in terms of data volumes, data sources, and data cleanliness.
“Scaling up, the data challenges increase,” he points out. “There are questions such as: What are the most effective structures for the data? How do we cleanse the data? What visualization tools will most help to understand what it’s telling us?”
But the prize is worth it, says North Rizza. “Look at the very best businesses, and you see procurement analytics delivering genuine procurement intelligence—identifying where the supply base needs to go in order to support the business, and adding value to the basic sourcing decision.”
Malcolm Wheatley, a freelance writer and contributing editor at Data Informed, is old enough to remember analyzing punched card datasets in batch mode, using SPSS on mainframes. He lives in Devon, England, and can be reached at email@example.com.