A recent article in Supply & Demand Chain Executive on What’s on Your CIO’s Mind noted that leveraging analytics to gain a competitive advantage and improve business decision-making is now the top priority for CIOs, according to a new study of more than 2,500 CIOs by IBM.
This is good news for sourcing and procurement professionals if you need a modern spend analysis system, because real spend analysis, which should start every sourcing process and end every procurement process, is just a subset of real data analysis. That means your chances of getting the system you need doubles if you join force with your CIO who could also use a good data analysis system to sift through the vast amounts of data she has to deal with.
Now, you may argue that the needs of the CPO, who needs insight into spend data, and the CIO, who needs insight into energy and application utilization (for example), are very different, but they’re really not. For starters, both have to categorize the data on the relevant dimensions (department and category for spend data, data centre and server for energy utilization, and department and application for utilization), both have to create summary reports (total spend, total energy utilization, and total number of users by application), and both have to create comparisons (year-over-year, department by department, etc.). A real data analysis system will do that and be powerful enough to serve the analytics needs of the CPO and the CIO.
And once you join forces on analysis, you can also join forces on risk management and compliance, which are also big needs for CIO as well. That’s going to be really hard for the C-Suite to ignore when two departments are proclaiming the need for these much-needed applications. Don’t believe me? Try it!
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This series discusses the recent report from CAPS Research on the role of optimization in strategic sourcing. The primary goal is to highlight, clarify, and, in some cases, correct parts of the report that are important, confusing, or incorrect to insure that you have the best introduction to strategic sourcing decision optimization that one can have.
The second chapter did a great job of highlighting the many benefits of optimization from a productivity, cost/price, and decision visibility perspective. In brief, they are:
- Faster Sourcing Cycles
No more fiddling with error-prone spreadsheets. (Remember that 90% of spreadsheets contain errors!)
- More Thorough Analysis
A broader, deeper analysis that looks at more alternatives.
- Higher Quality
Data integrity is much higher.
- Better Planning
Better up-front planning is done before the event.
- Significant Savings
Especially on the first event in a category.
- Cost/Value Trade-offs
You can analyze whether the additional cost associated with a service is worth it.
- New Savings Opportunity
The expressiveness allows suppliers to get creative and find ways of providing you their lowest total cost.
- True Market Baselines
An unconstrained scenario will give you the absolute lowest cost.
- Centralized Knowledge-Base
Your sourcing team can learn from each other and management gets better visibility into cost trade-offs.
- Cost Premiums
You can run historical events through the model and determine the cost premiums paid for preferred awards.
- Cost Drivers
You can analyze multiple events and zero in on cost drivers such as particular locations or raw commodity categories.
- Competitive Feedback
You can let your suppliers know where they are, and aren’t competitive, and why they won or lost a bid.
It also did a good job pointing out that good strategic sourcing decision optimization models also allow qualitative criteria to be analyzed. For example, you can exclude all suppliers with a service level of less than 95% or a product quality less than 8 (on a scale of 1 to 10). The ability to consider non-price decision criteria, used creatively, allows you to model and calculate a wide range of cost vs. value trade-offs and make better overall sourcing decisions. A great example of the power is the user who ran two scenarios where one scenario forced all rubber-based parts against a baseline that allowed the user to gain insight into how the cost of rubber was impacting her costs.
Next Part III: Preparing for Optimization
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