Category Archives: Spend Analysis

Procurement Fundamentals — A Path to Innovation

Today’s guest post is from Bernard Gunther of Lexington Analytics.
He can be reached at bgunther <at> lexingtonanalytics <dot> com.

Every year, I look forward to going to conferences with the hope that I will get a chance to see a great deal of innovation and learn something new. Most years, I am largely disappointed. Is there innovation? Yes. But much of what is being presented relates to operational excellence, operational success or even operational “good-enough.”

Why is this? It’s a lot harder to innovate when you are still struggling to set up standard practices. Procurement systems and processes tend to be a patchwork of different approaches for different spend areas all jumbled together. Many organizations are just catching up to best practices. Let’s take the “101” starting point for any procurement organization, the basic spend analysis of vendor payments — understanding how much you are spending with each of your vendors. Recent surveys indicate that less than half of companies have a system for this. For those companies without a system, they seem to be doing ad hoc dumps of data from their AP system into Excel or a data warehouse with no consistency in the analysis. We all know this is not a best practice in procurement. If companies are not doing the basics well, they have little time to focus on innovation.

It’s not surprising that there are so many presentations about operational successes and so few about innovation at sourcing conferences. Operational success is a key element of a strong function and can deliver significant value, but should it be considered innovation? At a recent conference, I attended a wonderful presentation on Negotiation Fundamentals. One would think that everyone in a purchasing group would be well versed in this and applying the fundamentals regularly. But if you look around procurement organizations, you find that many people are not applying the core disciplines of procurement in effective ways.

Is there support for innovation at organizations? Successful companies are continually investing in innovation and developing new products and processes. These new products rarely just happen and not every new product idea is a success. This means that a procurement group interested in innovation should be doing three things:

1. Look for innovation. Innovation usually comes from new companies but it can also come from unexpected areas. But, in order to recognize it, you’ve got to be open to it. I remember when my grandmother came to visit us one summer from Germany. Like many older people she wasn’t open to trying new things. Her attitude was, “I don’t know that food, so I don’t want to try it,” or, “We have that at home too.” Because she wasn’t open to new things, she didn’t see anything new. She wrongly concluded at the end of her stay that food in America was just like Germany. Are you saying the same thing to innovation?

2. Invest in innovation. Is it 1% of your budget? 10%? Is it 5 projects? Is it 3 new vendors allowed in? Don’t know? If you don’t know, how are you making it happen?

3. Allow for “failure”. A group that is innovating is going to have failures, or “less-than-total” successes. But that’s okay if your environment rewards some risk taking. If not, your people will only attempt things they know will succeed — which is not innovating, it’s following. You need to be able to work on projects and initiatives that aren’t perfect. Success is usually the product of many such small failures. There are far too many projects / programs / implementations that are deemed too big to fail by the owners. Projects promising innovation in a company may get viewed as another procurement initiative ready to fail — over promising and under delivering. This atmosphere is rarely one that fosters innovation.

If you are already innovating — wonderful. But I suspect that most organizations would be delighted if Purchasing were to deliver better operational performance. If your organization is not ready for true innovation, perhaps focusing on operational success is the way to build your organization’s credibility. By demonstrating your ability to add value through the fundamentals, you are setting the stage for future innovation. When you do innovate, you can present it as delivering more of what the organization already values.

“Spend” Analysis helps the Service Chain too

It would appear that Business Intelligence, once restricted to leading edge spend analysis providers, is starting to permeate the services supply chain. As noted in a recent Industry Week article on “Creating Visibility Throughout the Service Chain”, customer dashboards, key performance indicators (KPIs), and customized reports have long been available to internal account teams but now, however, leading edge organizations are making these same tools and data available to their suppliers and customers through business portals with aggregated information from multiple enterprise and transactional data systems. This is because business intelligence in the service chain not only generates efficiency, but also creates opportunities for real customer loyalty and business growth.

As the article notes, a real analytics solution that provides a user with the ability to truly “slice and dice” data across multiple business hierarchies offers a number of benefits, which include:

  • the quick determination of how well the most strategic and / or largest revenue sites are being serviced
  • the mapping of actual service delivery performance to perceived customer satisfaction
  • the actual equipment utilization against contract terms
  • the proactive monitoring of equipment usage and synchronization of information with actual inventory
  • the ability to gather the data required to capture a true “operational index” or “readiness-to-serve indicator”

The last benefit is of particular importance. The success of the service chain relies on the ability of a service organization to quickly and efficiently serve their customer. If service is bad, the customer will go elsewhere. It’s that simple. And the only way to to gage the true “readiness-to-serve” of the organization is to get a multi-dimensional view of the data. This is because overall equipment utilization (OEE), MTBF, first-time fix rates, on-time delivery performance, service-event resolution times, call center performance, service supply-chain order-fill-rates, warranty compliance, and invoice accuracy, among other service metrics, all contribute to an organization’s “readiness-to-serve”. To extract and aggregate this data from a classic reporting tool would be a humongous project that required multiple rounds of data extraction and Excel manipulation. But in a true data analysis tool, you could slice, dice, aggregate, disaggregate, normalize, derive, re-derive, restructure, aggregate again and get the report you needed in ten minutes.

And this is something that can only be done by a real spend analysis solution, because “spend” analysis has to go beyond the spend, and be able to analyze all of the data related to the spend. Otherwise, you get an incomplete picture, and that can cause more harm than good.

Opportunity Analysis: The Challenge is Having Accurate and Usable Spend Information

Today’s guest post is from Bernard Gunther of Lexington Analytics.
He can be reached at bgunther <at> lexingtonanalytics <dot> com.

Sourcing Innovation‘s “Seven Grand Challenges for Supply & Spend Management“, lists the seventh challenge as “Opportunity Analysis”. As a practitioner, I can report that bad procurement data is the biggest obstacle to successful opportunity analysis. By “bad” I mean procurement data that exists when the items are purchased / invoiced is not captured and made available for future analysis. The ongoing data analysis is rarely designed into the procurement process making the analysis hard to do and therefore rarely done.

I find it surprising that half of the large companies I deal with don’t have a formal process for analyzing their AP data. Though they may dump transactions from their AP system into a spreadsheet or a data warehouse, the data is raw and unprocessed and not consistently analyzed or well understood. This is not proper spend analysis, it is flying blind. If the quality of procurement information is so lacking for AP data — the most basic spend data — imagine how bad it is for invoice level data where pricing accuracy can be determined

Accurate and usable procurement information requires source transaction data, ways to enhance that data, and processes to get value from the enhanced source data. The data should be collected and analyzed as part of the regular purchasing process. Data analysis should be designed into the process flows. I will illustrate some of what’s involved to answer the simple question, “Did I pay the right price for an item?”

At a high level, the source data includes:

  1. “Transaction level” information on each purchase that includes: what is purchased, the unit pricing, the amount bought, who bought it, and data to link each transaction to the order, the payment and the contract. The specific data available will vary depending on the commodity. Airline information is different than computers, which is also different than facilities management.
  2. Contract information structured so that each item on every invoice can be priced and stored in a way that links them to transactions.
  3. Payment information which identifies the vendor being paid, who bought the item, which transaction detail links to the payment.

Data Enhancement: Making the raw data meaningful.

  1. Commodity assignment. For an item level cube, the commodity assignment will be more detailed than an AP cube and may be based on the description, the part number, or other attributes of the item.
  2. Pricing context. Each item purchased should link to the contract price, historical pricing, benchmark pricing (internal and external), and other information that puts the unit price paid into context.
  3. Cross item information. Some of the pricing comparisons need to be done across multiple items rather than against a single item. An analysis of the mix of team members on a consulting engagement or a legal matter would be an example.

Data Processing: Converting the meaningful data into actions that save money.

  1. Every month or quarter, the data needs to be collected, enhanced, and analyzed. The analysis should be able to answer such as:
    • Did we pay the contract price?
    • How much of the spending was off-contract?
    • How did the demand shift?
    • How much of the spending was on items that were not intended to be purchased?
    • Which organizations are responsible?
    • How much extra spending did this cause?

    Each company should be able to answer these basic questions in hours, not days or weeks. The data should be in-house and it should not require work from the vendor beyond originally providing the data as part of the invoicing process.

  2. Periodically, the team needs to answer questions like:
    • For a recent price change, what happened to the spending? If we applied the old pricing to the new spending pattern, how much would we have spent? Is this what was expected?
    • Is the mix of items we buy “optimal”? How much could we save by optimizing our demand?
    • How has the market price changed relative to our pricing? Is there enough shift that we should re-bid our spending?
  3. Use the data to generate savings, for example:
    • Request refunds for overcharges
    • Add more items to the contractual pricing terms so we can monitor the pricing moving forward
    • Shift the demand to generate savings
    • Negotiate with the vendor for lower prices

    And, on and on for different ways to leverage the information

This all sounds relatively easy. But it’s not happening today. Let me illustrate from a client example of office supplies. I don’t mean to pick on office supplies vendors, but this is a category with part numbers and contracts so it provides a good starting point for this type of analysis.

The client bought office supplies online through a punch out mechanism from their PO system. The vendor processes the orders, ships the items, and presents invoices for payment. The invoices are approved in the PO system and the vendor is paid per the contract. The contract was written 2 years ago and allowed for fixed (discounted) prices for the top 500 items being bought. When the contract was signed, 250 items were on the list. The new contract offered price reductions on certain items, which the sourcing team projected, would save 12%. Since the contract signing, most prices have been stable, with some exceptions for paper.

As the program was implemented by the client, there were a number of problems with the data:

  • For 20% of the items purchased, the item numbers recorded in the PO system did not match the item numbers in the contract. This was largely a problem of how the PO system recorded the data
  • The client could not state what percentage of the spending was for items with contract prices and what percentage was off-contract. The client needed to ask the vendor for this analysis.
  • The client had agreed to price changes, but did not track those changes and could not calculate the impact of those price changes on overall spending. Again, they had to rely on the vendor to track the pricing and do the analysis.
  • The buyers had shifted their demand, so that of the 250 items in the contract, over 75 were not being bought anymore and of the top 250 items being bought, there was no contractual price for almost 100 of the items. The vendor was waiting for authorization to add 50 new items onto the contract list (with better discounts).

This was all fixable. Fixing it generated incremental savings of 5% and improved the relationship between the client and the supplier. But it didn’t happen until we, the consultants, highlighted the problem and the opportunity.

Generally, we find that procurement data is a mess. And it shouldn’t be. But, this is why it’s a challenge.

Thanks Bernard!

the doctor’s Seven Grand Challenges for Supply & Spend Management

Seven deadly sins
Seven ways to win
Seven holy paths to hell
And your trip begins

Seven downward slopes
Seven bloodied hopes
Seven are your burning fires
Seven your desires….
  Adrian Smith / Bruce Dickinson

In my last post, which announced the cross-blog series that this post is officially kicking off, I reviewed the seven grand challenges for IT over the next twenty-five years, as laid out by Gartner back in the spring. Although they ranged from the ridiculous to the sublime, and contained a fair amount of overlap when closely analyzed, it’s a worthwhile exercise to undertake every now and again, because in order to develop a useful solution, you need to identify what is needed and the path you should be on.

This inspired me to propose a set of “seven grand challenges” for supply and spend management, in the hopes that it would get you, dear reader, to think about what is important, what problems should be solved, and where we should go. Considering how important supply management is in these troubled times, I hope that all of my fellow bloggers chime in with their ideas on what’s good, what’s bad, and, what’s downright ugly in supply chain today — because the first step in solving a problem is properly identifying it.

So, without further ado, to kick off this cross-blog series, here are the doctor‘s proposals for the seven grand supply and spend management challenges:

  • Optimization
    There are a number of challenges here. The first challenge is getting people to use solutions that are already out there. There are currently a number of offerings that address strategic sourcing decision optimization, distribution network optimization, and freight optimization quite well, and that, when properly applied, can save the average company up to 12% above and beyond the best solution obtained with auctions. The second challenge is integrating the different problems (sourcing optimization, freight optimization, network optimization, etc.) into a common framework that allows the tradeoff effects of each decision to be adequately modeled and understood in the big picture. The third problem is addressing the emerging non-quantitative regulatory and compliance requirements such as RoHS, WEEE, and GHG emission limits in a consistent and value-oriented manner within the optimization model.
  • Supplier Enablement
    This is something we still don’t have a good handle on. Beyond “supplier enablement is the provision of technology based solutions that enable the supplier to be more productive and better serve the buyer”, there isn’t yet a general consensus of what this technology needs to be, as most companies have not yet embraced B2B 3.0. I’ve argued before that, today, it’s a combination of catalogs, networks, e-Document exchange and management, and supplier portal technology, and I still think that is a good start, but enablement should go beyond enabling the exchange of information, it should improve the supplier’s operations overall.
  • Integration of the Physical, Information, & Financial Chains
    For most companies, these are three different chains. Some companies that have embraced RFID, GPS, and e-document management have taken the first steps to integrating the physical and information flows, but the technology is still emerging, the integration isn’t smooth without extensive integration and customization between a number of different solutions (and only Fortune 500 companies can even afford to consider this), and we have only started to look at the financial supply chain and how to best integrate it with the information supply chain. I think it will be a while before solutions that truly support a holistic view will emerge, especially considering that even the gorillas in the space don’t have end-to-end sourcing and procurement.
  • Solution Globalization
    Let’s face it … supply chains today are truly global, but the solutions are not. Most “internationalized” solutions are only available in a smattering of languages, most “internationalized” solutions are not plugged into real-time currency exchange feeds — and few developers have thought about the need to maintain/display multiple conversions (including the rate at the time of purchase, the projected rate, the current rate, etc.), and most “internationalized” solutions don’t help you understand how to do business with the country of interest.
  • GHG Tracking and Reduction
    Most enlightened countries have woken up to the fact that, even though we don’t know precisely how damaging each ton of GHG and / or carbon we emit is, we do know that it’s damaging and that we have to reduce our emissions. The first step is to get a baseline of the emissions produced by your operations, but for many companies, this is a multi-year effort. Better product and service solutions are needed. Also, although there are multiple proposals on the table to reduce emissions, there are few total value management models out there to help us select the right ones.
  • Risk Prevention
    Not only is risk not going away, but it’s getting worse by the year. Supply chains are getting more complex by the year, and the likelihood of something going wrong is steadily increasing. Solutions that can help a company identify risks, in real time, and identify possible mitigations and actions required to implement them, are desperately needed.
  • Opportunity Analysis
    Costs are skyrocketing, but consumer discretionary spending is stagnant at best. They key to a successful supply chain is cost reduction and avoidance, and this requires continual opportunity analysis. I envision this starting with modern spend analysis, but it needs to go beyond true spend analysis to continual innovation, since the greatest cost reductions will come from true revolutions, and not just the shrewd identification of category-based overspending. I envision that this will start with the integration of PLM with Life Cycle Analysis and Next Generation Analytics and then morph into something that none of us can envision today.

Now, I realize that these are pretty much the same problems we have been facing for the last five to ten years, but I suspect that it will be quite a while before they are solved due to the overwhelming complexity of today’s supply chains.

When the series is done, I’ll compile the “master list” of challenges and, if any of my fellow bloggers can convince me there are bigger challenges out there, revise my list.

Supply Chain Digest’s Eight Step Forecasting Process Using Demand Planning Software

Every now and again I like to address the forecasting process because, as a sourcing and procurement professional, you are often negotiating contracts against a perceived volume leverage based as much off of a forecast as it is based on historical data. In Part I we reviewed judgmental and statistical forecasts and explained why you need to balance both methodologies when generating your forecast, in Part II we addressed commodities forecasting and how you need to base it on the right data and the right factors, and in Part III I directed you to “Forecast Less and Get Better Results” on SupplyChain.com that demonstrated that the conventional wisdom that companies need to project forecasts and plans far into the future at a highly granular level is not necessarily right. Then, in Forecast with Foresight, I pointed you to a Supply & Demand Chain Executive article on a study about “re-thinking demand management” that noted that active/predictive demand management is necessary for good forecasting.

Part of active/predictive demand management is good demand planning. Good demand planning involves good demand planning software, so it was nice to see the Supply Chain Digest editorial staff print a short guide on how to attack the process, even if the first two steps didn’t fully address the problem.

The process, which was still quite good, that they presented was:

  1. Load Historical Data and Create Master Data
    Identify the key data elements that need to be considered and load them.
  2. Clean the Historical Data
    There are almost always problems with the quality and completeness of the data loaded into the system. E.g. “demand” may not be true demand, because it is taken from “sales” data, and will not include “stock-outs”.
  3. Generate a Statistical Forecast for Existing Products
    Use demand planning software with built in statistical models to find a “best fit” that will give you a starting forecast.
  4. Prepare Forecasts for New Product Introductions (NPI)
    Use the demand planning software to identify products with similar sales trajectories which will be used as the starting forecasts for the NPIs.
  5. Override Statistical Forecasts with Judgmental Input
    Use data from sales channels, knowledge about changes in market conditions, and expert insight to smooth the forecasts into the most realistic forecasts possible.
  6. Adjust the Baseline Forecasts for Promotions
    In certain industries, like consumer goods, promotions can have a huge impact on sales volume and need to be factored into the baseline forecasts.
  7. Manage Vendor Managed Inventory (VMI) and Collaborative Planning, Forecasting and Replenishment (CPFR) Processes
    Be sure to communicate data to both customers and internal managers responsible for these programs.
  8. Generate a “One Number” Forecast
    Integrate forecasting into a Sales and Operations Planning (S&OP) that brings together executives from key areas of the company to ultimately agree on a single forecast number and execution plan that will drive both the demand and supply sides of the enterprise.

The one change I’d make would be to replace the first two steps with the following:

  1. Do a Spend Analysis
    A spend analysis project, performed by a spend analysis expert that uses a real spend analysis tool, will load all of your relevant data, cleanse it, normalize it, and properly classify it in multiple spend cubes. The resulting cubes will allow you to perform the analyses necessary to identify which data is relevant, which data is statistically significant, and, more importantly, which products require significant forecasting efforts and which products are relatively stable year after year. Products with relatively stable sales do not need significant forecasting efforts, because expected demand can be easily determined from the spend analysis. On the other hand, products with variable sales, especially those products with a seasonal demand that are heavily influenced both by manufacturer promotions and competitor’s promotions for similar products, require detailed forecasting efforts.
  2. Load the Relevant Data
    Once you have identified those products that require forecasting efforts, you can load the associated data that is needed to run the statistical models, to determine the effects of planned promotions, and determine the appropriate demand forecasts.