Monthly Archives: November 2010

Analytics V: Spend “Analysis”

Today’s post is by Eric Strovink of BIQ.

As an engineer who originally entered the supply management space in 2001 to build a new spend analysis system, over the last 9 years I’ve watched marketing departments consistently “dumb down” the original broad and exciting definition of spend analysis that I remember from those days, to something really quite ordinary. For example, here are the steps required for classic data warehousing:

  1. Define a database schema and a set of standard reports (once, or rarely)
  2. Gather and transform data such that it matches the schema
  3. Load the transformed data into the database
  4. Publish to the user base
  5. Repeat steps 2-4 for life of warehouse

And here are the steps required for what has come to be termed “spend analysis”:

  1. Define a database schema and a set of standard reports (once, or rarely)
  2. Gather and transform data such that it matches the schema
  3. Load the transformed data into the database
  4. Group and map the data via a rules engine
  5. Publish to the user base
  6. Repeat steps 2-5 for life of warehouse

Not much difference.

You might ask, how can spend analysis vendors compete with each other, when the steps are so simple, and when commodity technologies such as commercial OLAP databases, commercial OLAP viewers, and commercial OLAP reporting engines can be brought to bear on any data warehouse? Well, it’s been tough, and it’s especially tough now that ERP vendors are joining the fun, but they compete in several ways:

  • Our step 4 is better [than those other guys’ step 4].
  • [briefly, until it failed the laugh test] Our static reports are so insightful that you don’t even need anyone on staff any more.
  • [suite vendors’ (tired) mantra] “Integration” with other modules
  • “Enrichment” of the spend dataset with MWBE data, supplier scoring on various criteria, and any other ways that might exist to try to add checklist features for analysts that may broaden interest in the spend analysis dataset beyond simple visibility.

It’s all very discouraging, but the doctor and I will continue to point out that spend analysis is not just A/P analysis; it can’t be done with just one dataset; and it’s not a set of static reports or a dopey dashboard, even though some vendors and IT departments would like to think it is. Spend analysis is a data analysis problem just like any other data analysis problem, and it requires extensible and user-friendly tools that empower people to explore their data for opportunities without third-party assistance. Those data come from multiple sources, not just the A/P system; many datasets will need to be built and analyzed; and from them, hugely important lessons will be learned.

The above notwithstanding, building a single A/P spend cube is a useful exercise. If you’ve never done it before, you will find things that will save you money. But that’s just the tip of the iceberg.

Previous: Analytics IV: OLAP: The Imperfect Answer

Next: Analytics VI: Conclusion

Share This on Linked In

To Make Your Supply Chain More Socially Responsible, Find the Value

An article late last year in the McKinsey Quarterly on “making the most of corporate social responsibility” — a topic that is at the forefront of everyone’s minds given the recent headlines about the rash of Foxconn suicides — made a great point: if you want CSR (Corporate Social Responsibility) initiatives to take off, find the value. Without it, you’ll be limited to pet projects, propaganda, and philanthropy — and while the latter can be good if you donate to the right organization, you’re not really doing anything as an organization if you’re just passing the buck.

The article suggested that the way to develop an approach that can truly deliver on lofty ambitions and achieve real success for the business and for society is through smart partnering which focuses on key areas of impact between business and society and develops creative solutions that draw on the complementary capabilities of both to address major challenges that affect each partner, and it made some good points. By combining strengths to overcome each partner’s weaknesses, two organizations can often make more of an impact than one.

But if you read closely, and think about the examples the article presents, the real key to success is finding a solution that brings short and long term value to society and to the business. If it only brings value to society, as soon as times get tough, funding for the initiative will be the first item cut from the budget. If the only real value is to the business, the recipients of the initiative won’t be that interested in participating and the company risks being, correctly, accused of propaganda and / or greenwashing. But if the initiative helps society and the business in the short term and contributes lasting value to society and the business in the long term, then the initiative will be a success (and the company will look like a hero in the eyes of the media, which will generate even more success for the company as it will increase its brand value).

The Unilever examples provided in the article are prime examples of how value insures success. In the Kericho example (in southwestern Kenya), where Unilever applied sustainability principles to the production of tea and focussed on productivity, sustainability, and environmental management, even though Unilever had to invest more money up front, Unilever won in the long run as they gained greater control over a critical supply of raw material while improving productivity. And the initiative was a success for society as the farmers made more revenue and increased their skills and living standards. Both parties win from the initiative, so both will continue to support it through the long term.

Share This on Linked In