Spend Analysis, Solutions

Spend Analysis Demystified - Part 3: Spend Analysis

Spend Analysis Demystified is a series of articles where Sievo’s source-to-screen reveals the hidden world of advanced spend analysis. Read Part 1 and Part 2.

Congratulations! Your company recently recognized the great untapped value of your data. The CPO commissioned a project to look for a spend analytics vendor in the ultimate goal of unlocking the opportunities of your procurement data. Be it supplier rationalization, identifying preferred vendors, PO coverage, group buying, supplier performance, your organization has endless possibilities unleashing the value in data so you can stay competitive. Besides, you’ve probably heard the phrase ‘Data is the new oil’.

Analytics is the detailed examination of data and applying proper statistical analysis of data to unravel hidden insights which in turn leads to supporting decision-making, or let me rephrase that, ‘data driven decision making’.

Fast forward into the project, data has been extracted from multiple ERPs, standardized, cleansed and now in the process of being properly categorized via your provider's collaborative classification process. It’s like oil being refined. As that stage in the process finally complete, you are now locked and loaded to do analytics work. The experience can be compared to freshly minted coins in your hands or the great smell of freshly washed and pressed clothes and even the great smell of the pages of that newly purchased bestseller book. You can safely say, now we can start analyzing and unlocking value. So if data is the new oil, then analytics is the combustion engine.

To help you with your first procurement analytics execution, I have listed 8 analytics insights that every procurement professional must have in order to be competitive.

Spend overview

This is the summary of how much spend you have this year, what is it in comparison to last year, last quarter, rolling 12, ytd etc. Where is the spend increasing and why? How is spend trending over time and is this maverick spending or not?

To know how much you are spending and for what reasons is to know how much cost you are incurring, both in indirect or direct categories. The ability to see your spend and how it compares to a particular time frame is a no-brainer. This method has been perfected by stock market analytics platforms that show the price of a stock vs. the day before, wtd, mtd, qtd, ytd etc. The particular KPI visualization indicates trend whether you're up or down and can respond to the slicing and dicing that you are doing on the data.

This figure also needs to be paired with the other substantially important indicators for example: number of active suppliers, average payment term, PO coverage, contracts expiring in the next 3 months, etc. What ends up in these highlights depends on what your organization values as important and crucial. This gives the procurement user a sense of the spending levels and supplier landscape.

Spend distribution by supplier

A classic way to represent spend is by looking to the pareto principle by Vilfredo Pareto. This allows you to see how much of your spend is controlled by your supplier base. It also allows you to find out if certain spending categories are controlled by a few or worse one supplier. Having one supplier commanding a big portion of your categories' spend exposes you to the risk of losing your leverage during negotiations. If a particular supplier has a majority control of your category spending, it has to be a conscious and duly informed decision; otherwise you would not be able to negotiate from a position of strength.

Slicing this by category, geography and for a particular time-period is crucial. Many companies try to do this, but it is a very strenuous task of creating a spreadsheet table, doing a running sum calculation and then a percentage calculation. You need a provider that has an out-of-the box solution to this.

Visualizing the spend-flow

Analyzing procurement data can be daunting. There are so many dimensions you can slice the data with. I have seen many data visualization executions in the hope of making sense out of how an organization is spending. The questions I encounter can range from how much we are spending in what categories, who are my top suppliers for each category, what is the relation of that to my organization units, which geographic suppliers do we spend most on and which sub categories are those.

Trying to interpret this in a visual form is tasking, some will create multiple bar charts sorted descending, pivot tables of top suppliers, categories and also a fully customized spend bar chart where x, y axis can dynamically be changed. Suddenly you end up with a bar chart and table heavy dashboard. A best-practice approach is to visualize spend as a flow-process. Applying data-storytelling allows you to present complex spending pattern into non-intimidating manner to the layman. This would ensure that the insights are provider using an effective delivery method – data visualization.

Price opportunities

The beauty of procurement analytics is that it can help you unlock hidden value and one of these is in the price of purchasing for direct spend materials. When you are able to diversify spend for a particular material to not just one but multiple suppliers, you would be able to gauge the price of purchasing the material and compare that across suppliers to arrive at the best supplier price. What’s better is that if your service provider has data benchmark data on the average price for a particular material which doesn’t necessarily need to come from your data. Looking at price alone is not enough, so my advice for any opportunity related reports should always balance with other factors like on-time delivery, quality, fill-rate etc.

The benefit of price opportunity analysis is that you are able to use the information in terms of negotiating prices for sub-optimally priced suppliers so they can match the best supplier price without sacrificing other supplier key performance indicators.

Everybody loves a waterfall

Your CFO or CPO will probably ask you where we have made significant cost reductions due to the savings programs you've implemented. Spend reduction can either be good or bad and it is good when it can be tied up to your executed savings plans. However it can also be the result of seasonality, price fluctuation, currency etc. To know where to attribute the spend change will allow you to provide gap analysis and have confidence when explaining the results to your stakeholders. For this I recommend a waterfall chart as shown below.

Remember that very well regarded purchasing process you have? How do you know if all people spending in your company is following it? The first step is to identify spending as it relates to the process. If a particular invoice has been paid and you can see it linkedto a purchase order, then that is spend in control. I can say spend in control is quite vague especially when you make the mistake of just using a PO field linked to an invoice as a measure for your PO coverage or contract number field linked to your invoice.

The better way is figuring out if an invoice was flipped to a PO for most part is very useful information but with the absence of that, you need to be able to match a PO to an invoice properly. This way, you can be assured that that process is stable and trustworthy. By knowing PO coverage, you will be able to identify maverick spending and address it properly since this information can be further sliced and diced to category, business unity and even supplier level.

Supplier segmentation

If you want to navigate the vast ocean of suppliers you have with the hope of optimizing the supplier base, you need to be able to segment the suppliers based on variables which you think are useful for your needs. An example is looking at your biggest suppliers and seeing their growth in spending vs. last year so you can have a matrix of high growth, high spend, low growth, low spend and then cross reference that with your category data.

Segmenting suppliers can also use variables such as supplier ratings, single source suppliers, critical suppliers, preference level etc. The segmentation has to be done properly in order for you to execute strategic vendor management. Your procurement analytics vendor should be able to provide this information and they need to have the on-the fly segmentation capability which requires advanced analytics.

Geo-spatial analytics

Many analytics platform will have some sort of geo-spatial analysis capability, however, you will find it frustrating that the use of the mapping data does not bring the added value to the user. One of the things you can improve is with the use of colors and sizes of the data point when you have a mapping object in your analytics canvass. It could be as simple as supplier location spend value (which is size) and then spend growth for supplier vs. last year (color). This way you would be able to say, hey why is Portugal suddenly lighting up on the map, I wonder what they are spending there on? Another example is when you search for a particular material, say corn, then you would be able to see where the suppliers for corn come from and figure out possibilities of supply chain optimization.

These are just a few examples on how to create insight driven analytics, there are tons more. Always remember, the more you can simplify your approach in presenting very complicated concepts through data, the more you will be effective in communicating the insights in the data. The moment you have delivered simplified and palatable analytics insights for the purpose of aiding decision makers in making decisions about procurement, is the moment you achieve competitive advantage. The world around you is drowning in data, reporting pukes and messy spreadsheets flying around you. There are vast spend analytics vendors out there, but there are a few who can deliver the insights you need designed specifically for certain user groups in your organization.

With Sievo, you can be assured that the data is in good hands. Sievo helps you view and understand your procurement data.

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