Modern grower nursery and greenhouse operations require analytics to help drive improvements.

There are several aspects to an effective analytics program, that can be viewed as layers or categories of functionality. Each higher layer depends on the lower layers.

Some of the layers are part of a normal greenhouse software or nursery inventory management system. The higher layers, and thus more computationally complex, are sometimes done in other tools and environments.

Transactions

At the lowest, most fundamental level of your data program is transactions. Usually this is done by your greenhouse or nursery inventory software system.

Transactions are the individual ‘events’ that occur in your business. Transaction events mark the occurrence of something important that your business needs to track.

Grower software provides mechanisms to record the aspects of the transactions that you need to track.

For growers, these events could include

• Transplanting or producing an item the potting line
• Receiving some buy in material
• An inventory size bump up
• An inventory move or space action
• A sales quote or order, a pick or pull slip
• A dig
• A dump or shrink record
• An invoice or credit
• A customer payment

The transactions are the little actions taken every day that record the activity of your business.

Transactions form the basis for your corporate financials, and operational metrics you use to drive improvements.

Properly recording transactions also drives more accurate availability, an important sales and customer relationship tool

Transactions can be tedious because in them are all the little important details. But it’s those details that drive analytic and business intelligence views of your data.

If the transaction details are wrong or missing, data metrics derived from them will be silently biased or wrong. Putting risk into data driven decision making.

Astute growers know that work processes must include the consistent and accurate recording of the necessary details of the transaction. As we will see, ‘telling the system’ will reward you in many ways.

Aggregations

The next layer of a beneficial data program is the aggregations.

Aggregations are mathematical calculations that involve data from multiple transactions.

For a very simple example, summing all the item sales records for a given week can yield gross item revenue and quantities shipped for a given week.

The total for quantity and for revenue is a simple aggregation – literally aggregating, or mathematically combining (in this case a summation), across many records to get an individual answer.

In this simple example we aggregated all transactions across a given date range.

The date is a characteristic of the transaction. Characteristics are also referred to as ‘dimensions’ or meta data about the transactions, and are typically stored in additional fields in the database or system you use. Having dimensions in your data definitions allows you to aggregate across those as well to gain different insights.

Dates are one important dimension by which your data can be aggregated. Other typical grower data dimensions for aggregations are things such as Customers, Customer Types, Items, Item Categories or Sizes.

Further, aggregations can be done across multiple dimensions, typically combining a Date or Time dimension with one of the others like plant category to yield revenue by plant category by week for example.

Aggregations are used in many places.

A grower’s financial statements are aggregations across a given month or year for defined financial categories. There are many calculated operational metrics a grower needs that are calculated across these dimensions, such as labor $ per hour or unit or average price for item categories by customer type.

Synthesis

The third layer of an effective data analytics program is synthesis. In this context ‘synthesis’ means combining different data sets to yield new insights from your data.

Synthesis takes different but related data sets and brings them together and then performs calculations, aggregation and analytics on them.

Synthesis required understanding how the different data sets can be linked accurately and then used for synthetic metrics.

An example is linking payroll data with production line data to yield labor rate metrics for the costs of production line activities.

The production line transaction data set would contain quantities, times, items and people counts. The payroll data could contain people, dollars and other payroll aspects.

Linking data sets and performing aggregations and calculations where you can derive key metrics for those production activities.

Prediction

The fourth layer of an effective data analytics program is prediction. Prediction takes the relevant data from your history and current situation with stated knowns and assumptions and calculates some future value.

For example, a production budget is a prediction derived from your existing data, your production plan, your product meta data, and your labor metrics and raw materials costs.

Another common example is a sales forecast, where a grower would take historical data or trends, and add knowns (new customers or item situation changes) along with assumptions about the forecast period that would increase or decrease historical norms. From these linked data sets, calculations are made that predict the future value of sales quantity or revenue.

Conclusion

Each of the layers can have representation in your grower nursery or greenhouse software, and with complementary tools like Excel, databases and other analytic programs. Further, information from each layer can be realized in tools like Excel, PowerBI or Tableau.

An effective analytics programs yield benefits in managing operations, visibility to issues or opportunities, and iterative prediction to help solidify goals, expectations and communications. All of this ultimately helps yield better results for your business.

If you have ever wished for better, faster analysis and information then we can help. Contact us today to find out how we can help you improve your view of your business.