Operational Level 2 - Understanding Dollar Accuracy

 

This section may be overly technical for some folks, and that's all right. Please keep in mind, Mango makes it easy to "just fix it" without getting into the weeds of exactly how Dollar Accuracy is measured (and its importance to proper cost of goods accounting). However, we do encourage everyone to read through this section so general concepts like Boundary Accuracy are understood. If you are an owner, auditor, CPA or enjoy the science of retail accounting then this should be fun stuff! 

Gross Profit Equation
When working with inventory value, always keep in mind the Gross Profit Equation:

Gross Profit = Sales - COGS (cost of goods sold)  COGS = Beginning Inventory Value + Purchases - Ending Inventory Value

While correcting inventory value issues through your Level 2 Checklist you are affecting your store's ending inventory value, which is linked to gross profit (equation above). If you are changing inventory value without a corresponding sale or purchase then you are also changing your store's Gross Profit dollar-for-dollar. That is, as ending inventory goes up, cost of goods goes down which increases gross profit and vice versa.

Inventory value corrections (and errors) can have a dollar-for-dollar impact on your store's reported (and taxable) gross profit. Make sure significant fixes are reported to your ownership and/or CPA. For example, if you are going to blow away $110,000 in Store Supply inventory value then it will likely reduce your store's reported Gross Profit by $110,000!

How does Dollar Accuracy Differ from IRA?

If Operational Level 1 measures quantity-on-hand accuracy (IRA) then why we need to measure Dollar Accuracy as a separate metric? Isn't Dollar and quantity-on-hand accuracy similar? Let's look at some examples to illustrate why they can be significantly different. A customer buys two cans of white spray paint in two different sheens, flat and semi-gloss. An untrained cashier, seeing two similar-looking cans rings up both under the same SKU (a third of untrained cashiers will do it). This creates two quantity-on-hand errors: the flat and semi-gloss SKUs are now inaccurate (one SKU is 1 high and the other is 1 low) and have an IRA of 0%. However, because both cans are worth the same amount in cost dollars, the error "nets out" (e.g. one SKU is $2.31 too high and the other is $2.31 too low so the net change is zero dollars) and inventory dollar accuracy is preserved. This is why a store with 80% IRA can still have very high Dollar Accuracy; because many of our cashiering and counting errors net out in dollar terms (some variances go up and some go down). In fact, about two-thirds of our counting variances net each other out in inventory dollars. A sandpaper SKU is showing 37 sheets on hand. But the entire box cost is accidently assigned to the individual sheet SKU. The sheet quantity-on-hand matches when counted so IRA is 100%. But, in dollar terms, the SKU is way off. If the box costs $20 then there's a $700 dollar variance in just one SKU—now imagine the entire sandpaper class is set up like this, it might have perfect IRA but be tens of thousands of dollars off in inventory dollar value. It happens more than you would expect! A store buys bin tags and check-out bags as store supplies from its warehouse. The line-items and costs are broken out separately on its invoice and the bookkeeper makes the appropriate expense-item journal entries. However, the bin tags and check-out-bags are automatically received into the store's inventory (QOH is incremented), a routine and expected occurrence, thereby increasing inventory value, decreasing cost of goods sold and increasing taxable gross profit. Counting these SKUs for IRA purposes is an entirely wasteful process since this errant dollar value should be be eliminated entirely. A store sells live plants but does not receive the vendor's invoices at the SKU level (does not create PO's) so the plant SKUs incrementally show negative quantity-on-hands. These negative quantity-on-hand SKUs create negative inventory dollar value when multiplied by their corresponding costs. Most stores elect to exclude negative value from ending inventory but many stores do not and some even exclude it in some places (RIV for example) but not in other applications, or some exclude it monthly but not yearly. Furthermore, when quantity-on-hand adjustments are made to "zero out" negative value, it creates a shrinkage imbalance (called swell) which tends to mask true shrinkage and oftentimes (if negative value is excluded from RIV) leaves owners and CPA's scratching their heads because there is a large disconnect between reported shrinkage and corresponding inventory value change. Counting these SKUs and zeroing them out for IRA purposes runs the risk of corrupting shrinkage and cost of goods accounting!  The examples above show how quantity-on-hand accuracy (IRA) alone is not an adequate method nor metric for achieving and measuring inventory dollar accuracy. However, having a good IRA at 80% or better (the Level 1 goal) is a prerequisite to achieving Dollar Accuracy.

Mango's Dollar Accuracy metric is computed by measuring the percentage of "Boundary" issues (cost/quantity errors, store supply inventory and negative value) then subtracting the net dollar variance percentage as your store processes its Count Sheets. Cleaning up any Boundary issues is a big part of achieving Dollar Accuracy in your store because Dollar Accuracy can never be higher than your store's Boundary Accuracy metric.

What is Boundary

Boundary is a Dollar Accuracy concept in Mango that might be best understood with an analogy: Let's say your profession is a painter and you are called to paint the exterior of an old house. When you get to the job site, you see the 'ole house needs a little more than just paint, it also needs some siding replaced and one side of the house is completely covered in mud. So, before you can even begin to get out the paint and brushes, you have to get out your hammer and saw and a power washer to prep the house for painting. Although this prep work isn't "painting" it is mandatory to ensure a quality paint job. Dealing with Boundary issues in Mango is similar to dealing with the painting prep work in the analogy above. You will use different tools (Boundary Exception Reports and Inventory Maintenance) to prepare your store's inventory file (if it first needs it) before continuing on with measuring and improving Dollar Accuracy through your Count Sheets.

How is Boundary Accuracy measured?

Dollar Accuracy is measured using the net dollar variance method as your store processes its Count Sheets. This dollar variance is then subtracted from your store's Boundary accuracy metric to produce a composite Dollar Accuracy figure. Wow, that's a mouth full. First, let's take a look at what we mean by Boundary and and then we will hit Net Dollar Variance. Boundary As we've discussed above, Boundary has to do with errors best fixed through processes other than counting and PIP, these are SKUs with issues your Count Sheets can't fix. Boundary issues come in three flavors: Cost & QOH Errors Store Supply Value Persistent Negative Quantity on Hand (QOH)      Cost & QOH Errors - Example $37,334.24 Above we see an example Boundary Exception Report dealing with obvious average cost errors which are inflating inventory value and reducing cost of goods sold over $35,000.      Store Supply Value - Example $7,866.37 The SKUs above are not inventory (they are expenses or capital goods) and do not belong in your store's inventory balance nor cost of goods equation.        Persistent Negative Value - $42,047.92    The SKUs above are persistently (month after month) showing negative on hand. This is a result of a purchasing and receiving issue or the SKUs should be set to Keep Stock Info = N to remove the negative quantity-on-hands. Measuring Boundary When summed together, the three boundary examples above total $87,248.53.    If our example store's total inventory value is $400,000 then these boundary issues represent 22% of total inventory value and which results in a 78% Boundary Accuracy metric.     Note: the keen observer will notice how negative value counts against boundary accuracy. This might seem punitive for stores excluding negative value from ending inventory, however, we occasionally see of inconsistencies in the way the same organization reports ending inventory value. Exclusions can vary from store-to-store (within the same organization) and from year-end to month-end or from printed reports to on-screen summaries. Furthermore, when a store excludes negative value and then corrects negative value, it creates a dollar-for-dollar imbalance in shrinkage as it relates to cost of goods sold, as negative value changes are not excluded in shrinkage (RPH), but the change from negative value is excluded from inventory value. In summary, Boundary, the first component in Mango's Dollar Accuracy metric, is the dollar value of errors, store supply and negatives as a percentage of your store's reported system inventory value. Once cleaned up, your store can maintain a 99% - 100% Boundary Accuracy metric.

How is Net Dollar Variance measured?

Net Dollar Variance, the 2nd component in Mango's Dollar Accuracy metric is similar to IRA where variances are measured as your store processes its monthly Count Sheets. However, where IRA is measuring quantity-on-hand accuracy (using the good count method) on random SKUs (RC Count Type), net dollar variance is using all Count Types and the SKU's variance times it's cost to determine the dollar impact of a variance. Example Let's say we count a section of hammers in our store and find a quantity-on-hand variance on two SKUs. A wooden-handled hammer SKU is short two pieces at $5.00 each. Our quantity-on-hand variance is -2 and our dollar variance is minus $10.00 (-2 x $5.00). Then we find two additional fiberglass-handled hammers costing $6.00 each which makes a dollar variance of plus $12.00. When we combine (net) these two SKUs together we get a net dollar variance of plus $2.00 ($12.00 overage and a $10.00 shortage). We can then divide this variance by the total inventory dollars counted (Let's say we counted $100 of total hammer inventory) and therefore get a Net Dollar Variance figure of 98% ($100 - 2$) / $100. Net Dollar Variance vs. IRA Example: It's important to understand the difference in how IRA and Net Dollar Variance are measured.    In the example above, 300 SKUs were counted with a total dollar value (QOH x Cost) of $50,000. Out of the 300 SKUs, 253 matched the computer when counted giving us an IRA of (253/300) = 84%. When we look at the dollar variance (QOH Variance x Cost) we see minus $1,500.00. This means we found $48,500 out of $50,000 reported giving us a ($48,500 / $50,000) 97% net dollar variance.  IRA - variances are tallied pass/fail (good count method), all variances are treated the same. It's a great metric to use where SKU-level piece accuracy is important such as suggested orders, root-cause variance analysis, and and customer fulfillment. It is not the best financial measurement because small errors in o-rings (for example) are treated the same as high-dollar variances (gas grills or huge errors!) Dollar Accuracy - plus and minus variances cancel each other out. Small dollar variances (foam paint brushes) do not matter as much as large dollar SKUs (lawnmowers). Consistent Dollar Accuracy is mandatory for proper business valuation, tax accounting and financial reporting. Dollar Accuracy is not good for measuring piece accuracy because one error (a SKU accidently typed in for QOH for example) can mask an otherwise accurate store. The Nature of Dollar Accuracy  The nature of net dollar accuracy is that two-thirds of total store variances tend to net out to zero in dollar terms. Furthermore, a significant portion of our inventory items are low-cost and SKU-dense such as o-rings, electrical boxes and miscellaneous hardware. Counting these categories may uncover many QOH variances, but will never result in an impactful dollar variance. To find and affect Dollar Accuracy you must direct counting towards impactful items which are (not coincidentally) selected on your Count Sheets. If you remember your Count Types from Level 0 training then you will notice two (XX and TI) types primarily used to measure and improve Dollar Accuracy. XX – Double X. These items have not sold in over 2 years. You should make sure these SKUs are not reordered if QOH is zeroed out. TI – Top Inventory. These SKUs have a high inventory value.  High QOH SKUs do not need to be counted exactly, approximate verification will suffice. These SKUs help align your Shrink and Financial Accuracy metrics. Careful research is required! The XX Count type audits phantom inventory (inventory in your system value but long gone from the sales floor) and the TI type continually audits your top value items (QOH x Cost). Your other Count Types and weekly Shooting Outs round out the process of identifying impactful dollar inaccuracies.  In summary, utilizing Mango's Count Sheets helps your store measure, improve and contain Inventory Dollar Accuracy.

Where is Dollar Accuracy Measured

Now let's put it all together by looking at our Charts.pdf, starting with the Inventory Accuracy Scorecard on page 3.   Above we see our example store working on Operational Level 2 with a Dollar Accuracy of 76% (goal is 95% or better). The store has good Level 0 Completion (97%) and fantastic Level 1 IRA at 85% but are getting seriously caught up with Dollar Accuracy! Off to the right of our Scorecard we have a pretty good indication of the root issue: 80% Boundary Accuracy. The red rubber stamp BOUNDARY ALERT is an obvious indicator and will display if Boundary Accuracy falls below 97%. Let's go ahead and drill down into our Boundary issues. A quick way to drill down to see the makeup of Boundary problems is to go to the very last page of our Charts.pdf. The lower left portion of the Inventory Value Trend page helps stores see their system reported ending inventory (using default RIV options) at both Replacement Cost and Average Cost and how Boundary Issues are affecting the reported value.   Above we see our Boundary worksheet showing (Avg. Cost): Cost & QOH Errors (Possible Errors) of $19,397 Store Supply of $23, 316 and Negative Value of $157,070 Combined, these errors account for 20% of the store's reported inventory value and cast serious doubt on its accuracy! We can now drill into each boundary error by looking at this example store's Boundary Exception reports, pages 1-3 of Exceptions.pdf (Action email)    Page 1 - Cost/QOH Errors - $19,397      Page 2 - Store Supply - $23,316      Page 3 - Persistent Negative Value - $157,070   Now that we see where our Boundary Accuracy metric is coming from let's return to our Inventory Accuracy Scorecard to look at the 2nd component of Dollar Accuracy: Net Dollar Variance.    Net Dollar Variance, Mango's 2nd component in computing Dollar Accuracy is derived from the Net Dollar Variance row from the Count Sheet Results section of your Scorecard. The Random Count (bullet #1 above) and Other Count Types (bullet #2) Net Dollar Variance results are used:  Random Counts (red bullet #1) - here we see 164 SKUs were counted randomly over the past three months totaling $2,432 in total dollars counted. The resultant -$29 Net Dollar Variance equates to 99% ($29 - $2,432) / $2,432. Dollar Accuracy within the Random Count section indicate Dollar Accuracy for the typical SKU in our store. Our example store at 99%, has a high degree of dollar accuracy and would expect very little dollar change when counting most of its SKUs. Other Count Types (red bullet #2) - The Other Count Types column shows Count Sheet results from the store's high-risk SKU population (high probability of inaccuracy and/or high dollar variance impact) like Top Inventory (TI - high-dollar inventory) and double-x (XX - phantom inventory) Count Types. The example store counted 810 SKUs worth $90,264 and and found -$5,315 of net dollar variances equating to 94% ($90,264 - $5,135) / $90,264. It's expected to see a lower Net Dollar Variance (and IRA) in the Other Count Types column because of the focus on high-risk inventory. The store's true store accuracy is likely somewhere between the Random and Other metric Blending* - Mango will average the Net Dollar Variance from the Random Count Column and The Other Count column in a blended fashion, weighting the highest figure two times. This blending behavior makes a more accurate and stable metric (you can read more about blending below if you are really interested). Level 2 Dollar Accuracy - Putting it all together Mango blends* the Net Dollar Variance (99% Random Count, 94% Other Types) and then subtracts the blended figure from Boundary Accuracy (80%) to produce the Level 2 Dollar Accuracy metric. This means that Dollar Accuracy can never be higher than Boundary Accuracy. That is, Boundary Accuracy acts as a ceiling from which Net Dollar variance is subtracted. This is why fixing Boundary issues is the first step to improving Level 2 Dollar Accuracy!     *Blending Net Dollar Accuracy (for auditors and accuracy scientists)  Blending the Net Dollar Variance from random population SKUs (RC Count Type) and high-risk population SKUs (Other Types) helps stabilize the reported Dollar Accuracy figure. Blending is simply weighting the highest dollar variance twice in an average calculation. This can help when the two metrics are uneven. Unlucky Sample - When a very accurate Other Type store (e.g. 99%) has an unlucky random sample (e.g. 92%). Blend = 97% Newer store on Mango - When a very accurate randomly sampled store (e.g. 99%) is newer to Mango and has a lot of phantom inventory (e.g. 89%) = 95% Typical scenario - where random sampled population is a few points higher (98%) than high-risk population (95%) = 97%. Here there is marginal difference between the blended average and a regular average.

Summary - Dollar Accuracy

If your head hurts after reading all of this then don't worry, you are in good company! Dollar Accuracy is one of those things in life where the benefits can be enjoyed without knowing the how and wy of every detail. That is, you don't have to understand exactly how Coca-Cola is made to enjoy a glass. When it comes to Dollar Accuracy there are a few things to remember: Dollar Accuracy is important to be maintained for your owners because it's a requirement for financial reporting (store valuation, gross profit, shrink, taxes). Dollar Accuracy is made up of Boundary Accuracy and Count Sheet Net Dollar Variance. Boundary Accuracy is fixed outside of the Count Sheet process, using your Exceptions.pdf. Net Dollar Variance is measured as your store processes its Count Sheets. Your next step is to start fixing Boundary issues and then monitor your Dollar Accuracy as it improves!

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