How to Utilize Data to Make Management Decisions
Consistent implementation of analytics leads to higher performance levels
“Figures don’t lie, but liars do figure.”
“Lies, damned lies and statistics.”
Although the definitive source for the first quote is uncertain it has been attributed to several individuals including Carroll D. Wright and Mark Twain. Mark Twain popularized the second quote in the U.S. Anyone who has worked with data knows firsthand how you can come up with a different conclusion depending upon how the data is presented, understanding the first phrase very well. However, numbers and data have a very persuasive power and thus the relevance of the second quote.
Managing a feed or grain business takes lots of different skills. People and delegation management skills, customer relations skills, financial management skills, time management skills, and a myriad of others. One area that all managers deal with is utilizing data to make management decisions. It has been said that “what gets measured gets done,” and this is perhaps the key factor in why data analytics leads to a higher performance level by those organizations who religiously implement it. In this column, we will examine data from all angles and look at a number of the “hows and whys” of management using facts and figures.
What is data?
Data is (or “are” if you actually want to be grammatically correct — as the word data is the plural of datum) typically the results of measurements and can often be visualized using graphs or images. In fact, preparing a graph from many of the types of data we will discuss below is a great way to look at and visualize progress or to measure performance against a goal. There can be errors in data (we are all human) and anomalies in the tables/graphs can lead you to those errors, allowing you to get them corrected. In addition, presenting data in graphical form is a good way to share thoughts with your employees.
Data is necessary in making effective decisions and solving problems specifically because it has no “personal” agenda. Data is neutral — or as our quote at the beginning of this column suggests, “figures don’t lie.” However, remember the second phrase. As humans, we are the ones who hold differing opinions that often shape decisions which lead to differing results. Thus it is critical that all of the members of your feed and grain firm’s management team be able to read and correctly interpret the data you are using. This means familiarizing yourself with a few basic data terms and concepts such as mean (average), standard deviation, counts and benchmarking. This will ensure that your team members do not “find” something in the data that is not valid but simply supports their own personal views.
Let’s define these terms briefly (several are from the area of statistics — did you know that 2013 is the International Year of Statistics?):
Mean (average): for a sample or population, the mean is the arithmetic average of all values; calculated as — adding up (summing) all the numbers you have and then dividing by how many numbers you have. Formula:
[Julie: ADD FORMULA HERE]
Where: [Julie: add glyph with a capital X with a line over the top here] = mean of all values in the data set
Σ x = sum of all data values
N = number of data items in sample
Example: data set of observations = 56, 62, 53, 55, 59, 57
n = 6; [Julie: add glyph with a capital X with a line over the top here] = (56+62+53+55+59+57)/6 = 342/6 = 57
Standard deviation: shows how much variation or dispersion exists from the mean (how spread out the numbers are). A low standard deviation indicates that your data points tend to be very close to the mean; high standard deviation indicates that your data points are spread out over a large range of values. Formula:
[Julie: ADD FORMULA HERE]
Where: σ = standard deviation
Σ = sum of
X = each value in the data set
[Julie: add glyph with a capital X with a line over the top here] = mean of all values in the data set
N = number of values in the data set
Example: Using the data from our average example above
[Julie: Add in four lines of equations here]
This is interpreted as follows: a bit more than 68 percent of our data falls within one standard deviation above and below our mean (in a statistical sense, 1 standard deviation either side of a mean encompasses 68 percent of your values). Three quick graphs illustrate our point:
Figure 1 shows observations with a small standard deviation, and Figure 2 shows numbers with a large standard deviation.
Figure 1. Figure 2.
[Julie: ADD GRAPHICS 1 & 2 HERE]
Figure 3 below shows a “normal distribution,” sometimes called a bell curve for the shape of the graph. There are many cases where your data will tend to be around a central value with no bias to the left or right.
[Julie: ADD GRAPHIC 3 HERE]
Counts: the number of observations or times of an occurrence
Benchmarking: setting standards or goals, and measuring your firm’s performance against either these predetermined numbers, numbers from similar firms in the feed and grain industry (perhaps from your state feed and grain association) or your company’s historical performance.
What kind of data?
Financial information is the first area we will look at. This information comes from your financial statements — primarily your firm’s income statement and balance sheet. We will discuss a number of these below (using a hypothetical firm Forrestville Feed and Grain) — how to calculate them and their importance.
Liquidity Ratios: these are ratios calculated from your balance sheet and measure the liquidity of your company (your ability to meet short and long-term debt obligations)
Current ratio: A test of the liquidity of your grain and feed business — can you meet your debt obligations if you liquidated your current assets?
Formula:Total current assets/Total current liabilities
Example: $5,691,714/3,769,347 = 1.51
Forrestville Feed and Grain has $1.51 of current assets to meet each $1.00 of its current liabilities.
Quick ratio: Some people call this the “acid test” of liquidity for a company, as it eliminates inventory from the calculation. Thus, it measures perhaps a more true “working capital” relationship of cash, accounts receivable, any prepaid expenses and notes receivable to meet your current obligations.
Formula: Total Quick Assets/Total Current Liabilities (where “Quick” Assets = Total Current Assets — Inventory)
Example: Quick Assets = $5,691,714 – 1,278,910 = 4412804/3,769,347
Quick Ratio = 4,412,804/3,769,347 = 0.117
Forrestville Feed and Grain has $0.117 of quick assets to meet each $1.00 of its current liabilities. In this case the business only has 11.7 cents to meet every dollar of current liabilities. The Forrestville example is a good example of why it is important to consider more than just one ratio. While the current ratio was looking strong, the additional calculation of the quick ratio revealed a less optimistic scenario. This business has a lot of inventory as part of its current assets. If management is confident that the inventory can be quickly turned into cash then there may be no need to worry about a quick ratio of .117. However, if market conditions are such that the inventory is not very liquid then further attention may be needed.
Debt to Equity ratio: This measure looks at how your firm is leveraging the debt you are carrying against the capital employed by your company’s owners.
Formula: Total Liabilities/Owner’s Equity (sometimes called “net worth”)
Example: 5,130,213/8,208,340 = .625
Forrestville Feed and Grain has $.625 of debt for every $1.00 it has in equity. Lower values of this measure are favorable – indicating less risk to the business. A debt-to-equity ratio of 1.00 means that half of the assets of your business are financed by debt and half by shareholder’s equity. A value higher than 1.00 means that your assets are financed more by your creditors than the owners of the firm.
Efficiency ratios — help you to measure the company’s efficiency of turning/utilizing your inventory, sales, assets.
Days sales outstanding: this ratio shows both the average time it takes to turn your receivables (what people owe you) into cash and the age (in terms of days) of a company’s accounts receivables. Basically, it will tell you how effectively you collect the money your customers owe you.
Formula: (Total Accounts Receivable/Total Credit Sales) x 365
Total Accounts Receivable (from your balance sheet) = $227,912
Total Credit Sales (from your income statement) = 1,823,296
Number of days in the period = 1 year = 365 days (some people use 360)
DSO = ($227,912/1,823,296) x 365 = 45.63 days
Forrestville Feed and Grain takes approximately 46 days to convert their accounts receivables into cash. This can then be compared to their terms of “Net 30 days” – a common credit term that tells customers their accounts are due in 30 days – before Forrestville starts charging interest on a customer’s account. This means that their average customer takes 16 days beyond the stated terms to pay.
Inventory Turnover Ratio: this calculation measures how quickly you are able to move (or turn) your merchandise (inventory).
Formula: Net Sales/Inventory
Example: Net Sales = $9,563,178 (from Income statement)
Inventory = $2,034,718 (from Balance sheet)
9,563,178/2,034,718 = 4.7 times
Forrestville Feed and Grain is able to rotate its inventory 4.7 times each year.
Profitability ratios – show how successful your company is in generating returns or profits on the investment that has been made in the business.
Return on Sales (or profit margin) = (Net Profit/Net Sales) x 100
Formula: (Net Profit/Net Sales) x 100
Net Profit = $765,351 (from your Income statement)
Net Sales = $9,563,178 (from Income statement)
Return on Sales = (765,351/9,563,178) x 100 = 8.00
Forrestville Feed and Grain has an 8 percent profit margin.
Return on Equity or Net Worth: This calculation measures your firm’s ability to generate returns on the capital invested by the owners of your company.
Formula: (Net Profit/Net Worth) x 100
Net Profit = $765,351 (from your Income statement)
Net Worth = $8,208,340 (from your Balance sheet)
Return on equity = ($765,351/$8,208,340) x 100 = 9.32%
Forrestville Feed and Grain generates a 9.32 percent return on the capital invested by its owners.
One Internet source you might find useful to compare/contrast some of the above financial metrics is the following website:
Your feed and grain business generates lots of data which you can utilize to measure the efficiency of your business. Measurements here include how efficiently your firm is using its assets — think things like number of tons of feed manufactured/hour or bushels of wheat delivered/truck. In fact many of these types of metrics can be developed strictly for your own use — you can be creative and almost any asset or resource can be used as the basis for a measurement. The focus should be on — is it something you can track and measure; and then, can you utilize this data to improve the efficiency of your business? Ratios are great ways to look at these sort of things, as ratios typically measure something like:
Output/unit of input
Where the output is something related to your business volume or sales and your input is some sort of effort or asset. When set up in this manner, ideally you want the numerator (top number in our fraction) to increase and the denominator (bottom number in our fraction) to decrease. Or — combinations thereof — meaning you want output to increase while keeping input the same. Both instances generate a positive result — the ratio increases — indicating increased efficiency!
Feed Mill Measures
Let’s look at a couple that are commonly used in the feed business (source of several of these measures is “Feed Mill Key Performance Indicators,” by Charles Stark from North Carolina State University.
Tons/run: Number of tons of feed/run. A feed mill that manufactures only mash feed should measure the tons per mash run, and one that pellets feed should track tons/pellet mill run. A higher number of tons per run indicates that your feed mill minimizes the number of times you switch to a different feed type — which will increase the tons of finished feed you produce.
Actual versus scheduled time: This measure is the number of hours your feed mill is operated each week compared to the scheduled time. You can calculate “Scheduled time” by multiplying the number of shifts per week by the hours per shift. If your mill is working more than the scheduled number of hours – then you can take a look at what caused the extra hours. Contributing factors might be feeds which are difficult to pellet due to formulation, lack of ingredients, equipment breakdowns, delivery problems or low employee productivity.
Downtime hours: This piece of data measures the time each week your feed mill is not manufacturing feed. Downtime may occur due to planned maintenance, but often downtime is unscheduled and due to breakdowns, lack of ingredients or full load-out bins waiting for delivery trucks. Analysis can then focus on how to reduce downtime and its causes.
Number of tons of feed manufactured/hour (or day or month): provides a measure of your productive capacity. When you start tracking this, you can see what your average is, and look to ways to increase this measure and what the bottlenecks are.
Number of tons of feed manufactured/employee: this calculation indicates how efficient your labor is in producing finished product.
Grain Elevator Measures
Number of bushels throughput/month (or year): this may be more a measure of size than efficiency. However, similar to the measure of feed production above – tracking this will give you a feel for what the norm is for your business and allow you to look for more efficient ways to handle grain.
Number of bushels throughput/bushels of storage: a measure of how many times you “turn” your facility. This will obviously depend on your location in the grain trade – as some elevators are used more for grain storage (country operations) and some are used more for throughput (terminal elevators or unit train operations). However, the point is to set some benchmarks for your facility and track them over time. This allows you to set standards against which monthly or annual performance can be measured.
Market data can be analyzed to determine how well your firm is doing relative to competitors in your marketplace and what is happening over time to your customer base.
Market share: Generally this is not a difficult calculation to make, and it is useful to track it over time. In the feed business, you can estimate this by livestock specie type — taking dairy, cattle or hog numbers, etc. from the USDA Census of Agriculture and then calculate how many of those animals you are feeding based on the feed tonnage you sell. Those in the grain business can do a similar calculation by looking at planted or harvested acres of crops in the counties you serve (generally published by your state department of agriculture in their annual statistical summary), then multiplying by average yields, and then calculating the number of bushels of those crops you handle each year.
You will likely need to make some assumptions, as market areas in the case of both feed and grain firms generally do not follow county lines; however, you will typically know the reach that your firm has.
Customer profiles: This sort of data is useful to track, so as to look at your customer base. Measures to follow might include: average acreage farmed (for grain operations), average head of livestock (for feed mills), average age, or distance from your place of business. It is also sometimes helpful to separate this type of data into categories such as top third, middle third and bottom third as this can shed light on where your business is coming from. Another categorization that is useful to track here is the 80/20 rule — the adage that for many businesses 80% of their business (or close to it) comes from 20 percent of their customers. Looking at the characteristics of the customers that provide 80 percent of your business can be a very instructive exercise — as you need to know what is happening to those people and continue to track their desires and plans.
Good Data, Bad Data, Too Much Data, Not Enough Data?
What data you collect, how often you collect it and how you utilize it to manage your feed and grain business is your choice. Some data is very useful to have (good data), other data is just that — “data,” and may not help you manage your firm efficiently. Sometimes we even get bad data (data can be poorly collected, or in some cases the variables are not relevant for your business at this point in time) — in which case you may make a decision that turns out to be incorrect. Other times we find ourselves overwhelmed with too much data — though more typically we find ourselves looking for or wishing for more data in order to make a better or more informed decision.
A commonly used idea in business management is the idea of moving from data to information to knowledge to wisdom. With recent technological changes in computing and electronic communication we find ourselves with increasing amounts of data, but that data is in its raw form. As a manager you must first ensure the data is transformed into information. You can do this by setting up systems to regularly produce tables and graphs as well as generate key summary statistics and ratios. Next you and the rest of your management team need to turn this information into knowledge. This is not a one-time event, but rather you need to be doing this regularly. A key component of doing this regularly is that you will be evaluating trends in your business — which is critical to good decision making. Over time, as you are more and more comfortable with the information it will have an increasing important impact on your decisions and you will be at the wisdom stage. Good luck in becoming a wise manager!