Part II: Market Intelligence: The Foundation for Opportunity Analysis | Sievo

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Part II: Market Intelligence: The Foundation for Opportunity Analysis

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Thought-Leadership Piece

In this 6-part series, guest blog writer Michael Lamoureux, a freelance procurement expert, explores and takes a deep dive into spend forecasting. With years of deep domain craft in sourcing and supply chain, he shares the secrets of what it takes to keep up in today’s fast moving economy. Read Part 1, Part 3, Part 4, Part 5, Part 6.

In our last article, we talked about the deficiencies of the descriptive analytics provided by the first-generation Business Intelligence tools.  We talked about the fact that while these tools were revolutionary when they were first released, they had their deficiencies, which became more prevalent as time went on.

The ability to build an OLAP cube that could allow invoice data or AP data for a year to be sliced and diced across categories, suppliers, geographies, departments, and even component products and services and then mapped quarter over quarter and even month over month was quite powerful, even if it did typically take a quarter (or more) to build the OLAP cube.  For the first-time sourcing professionals could get a relatively complete picture of historical spend over the prior year and use that to their strategic advantage in event selection, demand projection, and negotiation.

But once the organization had worked its way through its top N categories that represented 80% of the spend, signed contracts with the top M suppliers that provided 60% to 80% of the products in the top N categories, identified the biggest spending departments and worked with the lead buyers in those departments to teach them sourcing best practices, they hit a brick wall. The single OLAP cube could only do so much, and as per our last post, due to the limitations of the first-generation data warehouses and business intelligence tools, building another one that supported a different set of views, that may or may not be more useful, could take months.

And even if the organization did build a second cube and find some potential opportunities on the historical data, there’s no guarantee that the opportunities are still real six months after the fact.  First of all, the demand trend needs to be similar to what it was six months ago.  If demand has dropped sharply, the best of opportunities will vanish overnight.  And if demand has risen sharply, while the opportunity might be much larger, the demand satisfaction strategy will likely need to change significantly.

But even if the trends are similar, that doesn’t mean that the opportunity still exists, or that it ever existed in the first place.  In order to appropriately qualify, calculate, and extract an opportunity, even with the best of strategic sourcing programs, one needs market intelligence.  Moreover, one needs market intelligence that can be directly tied to the categories in question.

For example, there is only an opportunity if the current (expected) market price is sufficiently less than the current (average) price the organization is paying for the good or service, and only if it possible to obtain the needed good or service in sufficient quantity at that price.  Sometimes the market price can only be obtained if the product is bought in a sufficient quantity, and other times only if small quantities are bought (because traditionally limited demand meant that production lines were optimized for smaller output batches).  And sometimes the market price is only for a commodity version of the product or service, not for a customized or improved version.

This is yet another reason why an organization needs good market intelligence.  Without good market intelligence, not only won’t it know what the market price is, but it won’t know where the true market opportunities are.    All analytics can do on its own is identify spend and spending patterns, performance and performance patterns, and where the biggest discrepancies within the patterns are.  For example, where the price paid for a product is across the board.  Where the demand sourced across the supply base is erratic.

But with market intelligence, an organization would know when the spend presented an opportunity for savings through consolidation or spend leverage, better performance through shipping re-allocation (for better on-time delivery) or lean transformation across the supply base, or both through supply base re-allocation to more strategic suppliers.

So where do you find this market intelligence?  And how do you integrate it?

Price data for products and services can be obtained from de-facto market indices, government contracts, and GPOs.  These days, on-line sites like Amazon and Alibaba can provide an organization with a de-facto baseline index for commodity products.  This baseline cost data can be augmented with cost data from government contracts and bids on government contracts as this data is public.  Finally, for deep insight into the best pricing that can be obtained, an organization can tap into the knowledge of a GPO that amalgamates millions, if not tens or hundreds of millions, of spend against a product or service.

But not everything an organization buys is a commodity or service for which there is a lot of pricing information readily available.  Many organizations buy custom products that there is no public product pricing for. So, what does an organization do?  It builds should-cost models that allow it to understand how much the product or service should cost.  It builds the product up as a bill of materials, energy requirements, labour requirements, and the should cost is all of these component costs plus a production overhead and a fair margin.

For every raw material, somewhere there is a commodity index that can be used to generate a raw cost.  Similarly, it is possible to pull a relatively accurate energy rate because, depending on where the product is being produced, either the energy rates are public or the market is deregulated, in which case there are exchanges rates can be pulled from.  Most governments maintain information on labour rates for different industries and average labour rates can easily be calculated.  Finally, government and third-party data sources can be consulted to determine average plant overheads in any industry, and then all an organization needs to do is pluck in a fair margin to complete a should-cost model.  The price will then be less than what the organization is paying, indicating a good savings opportunity.  About equal, indicating the organization should stay on the same course, or greater, indicating the organization has found a supplier that is doing better than expected.  In this last case, the organization should invest in what should be a strategic supplier to ensure costs stay down over time.

In other words, with a bit of seeking and integration, for every primary product in every category, an organization can use market intelligence to determine market pricing and compare that to the average price being paid by the organization.

But this is not the only market intelligence an organization should be seeking.  Sometimes organizations can save by making appropriate performance improvements.  As long as it can identify areas where performance improvements are possible in reasonable timeframes and at reasonable cost levels, performance opportunities can be golden.  And here’s another area market intelligence can help.

If on-time delivery, either in-bound or out-bound, is an issue, an organization can use freight marketplaces to determine average shipping timeframes for various transportation modes as well as determine which carriers consistently meet, or beat, the average for each transportation mode.  Similarly, if the organization consistently seems to get stuck with too much obsolete inventory that has to be sold at a loss, the organization can source data from reputable analyst firms or industry groups to determine average product life-cycles and determine when it should plan for end-of-life to reduce product line obsolescence.

In other words, market intelligence is the true foundation for opportunity identification and when combined with analytics, it is the foundation for opportunity valuation.


About the Guest Writer

Michael Lamoureux, aka the doctor, is the Editor-in-Chief of Sourcing Innovation (.com), a resource for sourcing, procurement, and supply chain professionals who are interested in improving themselves and the overall performance of their organizations. A regular contributor to Spend Matters, he is a Computer Science PhD who has been heavily involved in the Sourcing and Supply Chain Space since 2000 and the e-Commerce space since 1997. As a freelance procurement consultant with extensive expertise in sourcing, procurement, and supply chain processes, he aims to continually push innovation in and beyond the supply chain space. With particular expertise in analytics, modeling, and optimization, he is able to dive much deeper into technology and core issues, striving to help businesses with their internal knowledge transfer, positioning, and planning problems. 

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