Better than Average

A lot of time is spent trying to measure yield, when it comes to natural products understanding what inputs generated how much output is key to improving performance.

At the highest level we can take the total output and divide by the total inputs. As long as both are in the same unit of measure e.g. kgs or meters we end up with a percentage or yield. Happy days.

Often when dealing with natural products we want to know more than just the average, typically there is some sort of grading, classing, or sizing that goes on along with rejection of inferior product. Typically, more than one of these things is happening at once.

This problem is known as disaggregation. When it comes to sales and planning (S&OP) having an understanding of the disaggregation is key to success. This is where Power BI starts to shine, what starts as a simple question about yield quickly extends to size distributions and grade splits. Visualising these characteristics is far more engaging and meaningful that a spreadsheet full of numbers. Users can spot trends and anomalies easily before deciding if plans need to change.

The other challenge is that we don’t always have the luxury of common units of measure. Raw products turn up in bins, hanks, crates, barrels and buckets. Finished products go out in nets, trays, shippers, pallets, cartons and cases. How we convert these different units is beyond this blog and specific to each industry or business. The caution here is that if we use averages too much, we lose the yield accuracy.

If we want continuous improvement, we need accuracy, we need detail. Because improvement isn’t about the average, its about making more of the good stuff and less of the bad. The average just sits somewhere between the two.

If you want to learn more about how your inputs are yielding, get in touch

One Source of the Truth!

I first became familiar with this term over 10 years ago at T&G Global. It was my first role as an analyst, and one of my responsibilities was to prepare forecasts. These forecasts served as the foundation for many other functions—planning, sales, logistics, and finance. Each of these teams had different needs and would challenge the assumptions used in preparing the forecast. I quickly learned that I needed to provide answers tailored to each audience. They all wanted similar information, just presented in different ways—sometimes even in different units of measure, such as bins of apples or packed cartons (TCEs, for those familiar with the apple industry).

The solution was Excel and its data model feature. I didn’t know it at the time, but learning to use Power Query and the data model, along with writing those first basic DAX formulas, would eventually lead me to start my own business.

Back then, I was simply trying to create pivot tables. Ones that would let me answer everyone’s questions while maintaining a single source of truth. That goal has remained the same with every business I work with today. Data can get messy, so paying attention to detail and checking assumptions with experts in the business is key to ensuring the results can be trusted. Because without good data, how can you make good decisions?

If conflicting data in your business is causing frustration, get in touch