Managing by the Data

Remember Dale, our farmer from the Repeatability post. We talked about him not complaining, and that was the problem we identified.

He had been hauling to the same elevator for fifteen years. Good people. Solid operation. Nothing he could point to as a major issue. But sometime around the middle of October, something started to feel off. Not every load. Not enough to argue about. Just enough to notice.

A 15.2  moisture hat felt like it should have been a 14.8. A dock that showed up where it had not before. Small things, spread out over time, but consistent enough to stick in the back of his mind. He did not say anything. He did not have proof. He just had a pattern he could not quite pin down.

That is where most operations miss it. Because the proof was there.

Every ticket he brought in all season carried the story, moisture, test weight and dockage. Time of day. Who was on shift. The elevator had been recording the evidence in real time. They simply were not looking at it in a way that would have made Dale’s quiet concern visible. The pattern existed. The signal was there. The conversation that could have reinforced trust before it slipped was sitting in a stack of tickets and a spreadsheet nobody was really using.

That is a “Managing by the Data” problem. And it is a lot more common than most grain operations want to admit.

Managing by the data is not about software. It is not about dashboards, analytics platforms, or whatever the latest tech vendor is pushing at the grain show this year. It is about discipline. You decide what matters. You collect it the right way. You keep it somewhere useful. You look at it on a regular frequency. You act on what it tells you. Then you go back and check whether the action worked. That simple loop is what separates operations that actually learn from operations that just stay busy.

Know What Is Important to Collect

Before you talk about spreadsheets or systems, you need to answer one question for each part of your operation. What does “good” look like here, and how would I know if it was drifting? That question, answered honestly, tells you what to measure.

For receiving, the important data includes moisture, test weights, BCFM, damage and dockage trends, throughput by hour and by shift, and grading consistency across your graders. If you are not comparing grading results across your inspection staff on similar grains, you will not know the spread is happening until a farmer like Dale brings it up. In some cases, you will not hear about it at all. The farmer will simply adjust his behavior next season.

For drying, tracking plenum temperatures along with inlet and outlet moisture and temperatures is a good place to start. Compare that data against discharge moisture targets and track the trends. Record and compare the variation in discharge temperatures to ambient temperatures and ask if your target is being met. Compare this against your bushels per hour drying throughput. Very few operations track or use this data in a way that lets them trend it across a season or compare this October to last October.

For storage and aeration, you want temperature cable readings by bin and date, fan runtime logged against outdoor conditions, and any quality events recorded with the bin number, the date, and what the grain condition was going in. Heating, crusting, insect activity, or other problems are not just events. They are signals about airflow, fill practices, and how well the aeration plan matched actual weather.

For loadout, ask if you are meeting all contract specs and how effective your mix and blend really are. Track delayed truck shipments, rail or barge demurrage, and any end user rejections, discounts, or complaints with as much specific detail as you can collect. End user feedback is valuable. Most operations hear it, talk about it for a few minutes, and never write it down in any organized way.

For maintenance, track downtime by piece of equipment with cause and duration, the split between planned and unplanned work, and parts spend by asset. Parts spend by asset is a quiet leading indicator that many people overlook. When the parts spend on a specific leg or dryer starts creeping up month over month, the machine is telling you something. Listen before it tells you in the form of a catastrophic failure.

The discipline here is deciding what you are going to track before the season starts. Do not try to reconstruct it in January when someone asks a question you cannot answer.

Collect It Consistently

Data collected differently depending on who is on shift is barely more useful than no data at all. A moisture reading taken at a different point in the receiving process, a dryer log with half the fields blank on night shift, a maintenance record with no cause code recorded, these are not records you can analyze. They are noise.

The enemy of consistent data collection is the combination of extra effort and lack of standard procedures. If recording the data is harder than skipping it, people will skip it. Forms should be short. Fields should be obvious. The recording step should happen at the natural pause in the workflow, such as when a ticket prints, a bin goes empty, or a shift changes. It should not live as an extra task that might be remembered after the fact if things slow down.

This is standard procedure for data collection. It is the same idea as standard work anywhere else in the operation. Define the right way. Train people to it. Check that it is holding.

Store It in a Way You Can Use

Paper logs in a binder are better than nothing. They are not much better. The test of good data storage is simple. Can you pull up the last thirty days of dryer performance in five minutes? Can you compare grader A’s moisture readings to grader B’s on corn over the last two weeks? If the answer is no, then you are collecting data but you are not really using it.

For most grain operations, a well designed spreadsheet is enough to start. One tab for each area of the operation. Consistent column headers. Dated rows. No merged cells. No color coding as the only way to find something. The goal is a format where sorting and filtering actually work, so when you want to look at bin 14’s temperature trend over the last six weeks, you can do it in two minutes without digging through stacked printouts.

More sophisticated operations may move toward an integrated grain management system or a simple database. That can be useful, but do not let the pursuit of a better system become the reason you are not using the data you already collect. A clean, maintained spreadsheet beats an elaborate system nobody keeps current every single time.

One rule is worth following. When you set up your storage format, think first about the questions you are going to ask later. If you know you are going to compare grader performance, make sure grader name is its own column and not buried in a notes field. If you are going to trend discharge drying temperatures over time, make sure you set up designated sampling and testing intervals. The format should serve the analysis rather than fight it.

Sort It and Analyze It

Raw data does not tell you much. Sorted, trended, and compared data will tell you almost everything you need. The most useful analytical habits for a grain operation are not complicated. You do not need a statistics degree. You need four consistent moves.

• Trend over time. Is dryer performance improving or slipping across this season compared to the same period last year? Is average moisture variance at receiving getting tighter or wider as the season goes on? Trends give you the earliest warning. They show you drift before it becomes a crisis.

• Compare across people and shifts. Are moisture readings tighter on morning shift than afternoon? Is one grader running consistently higher on test weight docks than everyone else? Comparison across people is one of the most powerful and most uncomfortable analyses you can run. It is uncomfortable because the answer sometimes points at a training gap or a personnel conversation. It is powerful for the same reason.

• Compare actual versus target. How often did loadout moisture land outside the contract spec this month? In which direction, and were you giving bushels away or risking rejections? How frequently did dryer output hit target versus miss high or low? Actual versus target is the simplest scorecard in the business. Most operations are not keeping it in any formal way.

• Flag outliers. When a data point is far outside the normal range, it is almost always worth a short conversation before it becomes a complaint or a failure. The bin whose temperature jumped four degrees overnight. The dryer run that saw corn being dried one and a half points below target. Outliers are the data’s way of tapping you on the shoulder. Most of the time they are nothing. Occasionally they are the thing that, if caught early, saves you a significant headache.

  
  

Act on It

Data without action is record keeping. There is some value in record keeping, but it is not management.

The discipline here is to connect the analysis to a decision. You must be explicit about who makes that decision, by when, and what threshold triggers it. When the dryer log shows discharge moisture variations for two consecutive weeks, who looks at that? Who decides whether it is worth a walk through the process or a maintenance check? What is the line between “worth watching” and “worth acting on today”?

Most operations do not have those answers written down. That means the decision depends on who happens to notice and whether they feel like doing something about it that day. That is not managing by the data. That is managing by whoever is paying attention.

Build a simple set of decision rules for your most important metrics. You do not need a complicated chart. You need a short list in plain language. For example, if dryer discharge moisture have a plus or minus spread of one point from target week over week, the manager walks the dryer that day. If damage readings between graders are showing over half a point variation on loads from the same farm, the supervisor reviews probing and grading procedures with both individuals. If a bin temperature cable ticks up three degrees in twenty four hours, that bin goes on the morning meeting agenda.

Simple rules, followed consistently, are what turn data from a record into a management tool.

Follow Up

This is the step that closes the loop. It is also the step most often skipped.

You noticed variations in dryer discharge moisture. You reviewed the process. You increased sampling times from thirty to fifteen minute intervals. You are not finished. You have to go back to the data to see if the action worked. Did discharge moisture return to the normal range and stay there? Or did it come back down for a week and then drift up again, which would tell you that you need further investigation.

Follow up has two parts. First, did the action work. Check the metric after the change and see if it moved in the right direction and held there. Second, does the standard need updating. If this problem can come back, is there now a check in place that catches it earlier next time?

Without follow up, you are running a series of disconnected reactions. With follow up, you are building an operation that actually learns. Every time you close the loop, you add a little more institutional knowledge about how your specific facility behaves and what it takes to keep it running the right way. That knowledge compounds across seasons. It is the difference between an operation that has been around for thirty years and has thirty years of wisdom, and one that has been around for thirty years and keeps making the same five mistakes.

A schedule that works day to day

Daily, operators record key readings at the natural pause points in their work. Moisture out of the dryer. Temperature cable highs by bin. Throughput. Any downtime events with a cause note. A supervisor reviews yesterday’s numbers before the day starts and flags anything that looks off.

Weekly, a manager reviews trends across the week. Dryer performance, sampler consistency, loadout accuracy, maintenance events. The manager identifies anything worth a walk through the process or a simple five whys conversation. This review does not need to take an hour. Fifteen minutes with a well designed summary is enough if the data is clean and current.

Monthly, the team compares this month to last month and to the same month last year. Maintenance spend by asset. Quality events. End user feedback. Grading trends. The group decides which problem gets worked on next.

Seasonally, the operation does a full review of harvest data against targets. What did we hit. What did we miss. What did we learn. What will we do differently next year. This is where the data pays its largest long term dividend. The real value is not in the moment of collection. It is in the accumulated understanding of your operation that builds year over year.

Back to Dale

Dale’s feeling that his moisture grades moved around was already showing up in the data. There was half a point of spread between two inspectors on similar corn, widening across the season as the part timer became more comfortable with their own method instead of the standard one.

In an operation that manages by data, someone looks at grading  consistency every week. They see the spread widen in week three of harvest. A supervisor has a short conversation, goes back to the standard probe pattern and grading SOP, and runs a quick side by side with both graders on the next few loads. The spread closes. The data confirms it. The standard is reinforced before it turns into a ticket dispute.

Dale gets consistent grades all season. He does not know why. He does not need to. He just knows that the numbers on his tickets feel fair every time. When his neighbor asks where he takes his grain, Dale does not hesitate.

That is what managing by the data looks like from the farmer’s side of the scale. It is not a dashboard. It is not a software platform. It is the discipline to know what matters, collect it correctly, look at it regularly, act on what it tells you, and close the loop.

If you do that season after season, your data stops being a record of what already happened and starts being a map of how to keep getting better.

Thank you for reading and for being part of this conversation. Whether you are an elevator operator, a processor, or simply someone who cares about how grain moves from field to market, reviewing the fundamentals is always time well spent. Your feedback shapes this blog, so feel free to share your thoughts or experiences.

Regards,

Grain Guy Fifty