Whether you’re a small business owner or you work in Supply Chain, a crucial tool to identify challenges and opportunities within your organization is to regular spend data analysis.
Without such level of analysis, companies can lose visibility as to where the money is going. Oftentimes it’s only the accounting department who know’s where the money is going, but it’s the Operations, Supply chain, C-Suite, Sourcing, Purchasing, etc. that are making spend decisions!
This unsustainable disconnect between the information and the decision makers of the company can lead to increased “Spend Leaks” or spending money on things you don’t need, and shorting your company of the funds needed for more important endeavors.
What is Spend Analysis
Spend Analysis is the process of collecting, cleansing, classifying, and analyzing spend data.
- Collecting Data
- The first step to Spend Analysis is to find the data. Most companies already have databases full of transaction data that could be used as a starting points for the data analysis
- Whether the data has been systematically or manually collected, it is crucial to identify how accurate it is.
- If it’s been manually typed, it could contain a plethora of errors and misspellings.
- If it’s systematically recorded, it might be useful to know the sources of the data in order to make sure we understand their real meaning and assumptions attached to the numbers.
- Depending on the particular questions you are trying to answer, the data has to be logically classified. This is the big problem Artificial Intelligence is trying to solve, but until it is fine tuned, it is mainly a human logic exercise.
- Classifying data needs to take into account the context of the data, where it comes from and who is going to need to understand it. Data can be logically categorized, by product families, geography, product importance, spend category, cost, complexity etc.
- The final step is turning this data into actionable intelligence and conclusions that can be used to make decisions. Oftentimes however, when we arrive at this point, we find that there are further questions we might want to draw from the data.
- If the data tells us that we are spending the most amount of money in a particular family of products. We might want to find out which of those high spend products are low profit in order to maximize the spend/profit equation.
Data Driven Decisions
We can’t predict the future of business, but as technology gives us more and more tools, we’re coming to a point where we use it to approximate with a certain degree of confidence what is most likely to happen in the near future by creatively using data.
For example, an ice cream truck owner, might be able to approximate in loose terms how good his sales will be by the temperature outside and the area of town he’s in.
Common sense might indicate that the hottest it is the more ice cream he will sell, and that the residential area park, might have more sales than the industrial block, but the data could not only give you these numbers but it could also help him decide exactly at what point it becomes unprofitable to do it.
Let’s assume that the salesman needs to spend $50 dollars in gas, $50 in raw materials, and 5 hours of his day commitment to selling his ice cream. He could also decide to focus on a different business that can give him $200 dollars profit for the same 5 hours without spending any further money. What is the minimum temperature that he needs to see outside to know with a certain degree of confidence that he will make more money on his ice cream truck as opposed to his other business?
If on average he sells $200 dollars at 70 degrees, and 500 dollars at 90 degrees. We can assume that for every degree hotter it gets, he’ll sell on average $15 more dollars. At 70 Degrees he’ll only make 100 dollars in profit ($200 in sales – $50 gas – $50 raw materials) so it will be more profitable to go to his other job and make $200 dollars instead of $100. At 75 degrees he could expect to make 175 in profit, and at 77 degrees he could expect to pass the break even point by about 5 dollars.
Conclusion, only if he sees that the temperature outside is higher than 77 degrees he should take his ice cream truck, otherwise he should focus on his other endeavors, and depending on the data variability, and external conditions, he might want to modify the decision as well (holiday, seasonality, school hours, etc.)
These are the kind of basic decisions that could be found by using data analysis. Now if you combine that particular inquiry, with the flavor sales data, route analysis, truck performance analysis, etc. (depending on the data available) we can begin to optimize the operations to maximize profits and reduce unnecessary wastes of resources.
Depending on the types of answers we’re looking for, there’s different tools and methodologies needed to find the answers. Sometimes a simple pie chart might give us the answers we’re looking for, while other times we have to spend days looking at the data in every way imaginable actively cutting it a million different ways to draw one actionable insight.
While in most cases for the general public excel is enough to find the answers needed, depending on the size of the project, the amount and source of the data, and the resources available, it might be needed to bring in stronger software than excel to understand and present the data such as Tableau or Power BI. Depending on the data itself, it might take hours and hours of work just to clean the data to start making sense of it.
These are often the barriers that prevent this exercise from becoming a constant practice. Be warned that neglecting to understand the spend data, leads to overspending and missed opportunities.
Even if there are no specific questions that needs answering, this type of analysis can lead to previously unseen opportunities that could improve the bottom line.
If you are unsure about where to start, I’d suggest finding a few people from your organization that might be able to help out.
If that’s not possible, reach out and hire someone to give your data a quick look to ensure you’re not missing any obvious opportunities.
Whichever way you do it, your bottom line will thank you.
For questions, comments, or advice feel free to contact me at Hugo@DailyGameTheory.com