So I've been tracking my inventory record accuracy (for the end of month counts and cycle counts) and my suppliers delivery accuracy % with the method you outlined. I was thinking about another one and wondered about your feedback.
I think it would be worthwhile to track "Out of Stocks". For example, I'm out of [Product X] until Friday because I didn't keep enough inventory and my supplier short shipped me. I know I'm out of stock because our Shipping Manager sends a report anytime it happens - but nothing is ever tracked.
Wouldn't it be worthwhile to track, by vendor, the # of times I ran out of stock on a sku every month and possible how long it lasted? If so, what is a common method to track it?
Let me know if you have any ideas. It just got my brain stirring and I think it would fall nicely as a "Customer Service KPI" on my KPI Board.
Here was my initial response:
I have often used % Out-of-Stock as a KPI. Often, you can use this to communicate good news, as well as to identify area(s) that need improvement. (Sales guys are always complaining, "we ain't got nuttin' to sell." Well, if you whack them with a preponderance of evidence over time, they'll stop complaining and start selling.)
I have usually taken the statistic one step further. I categorize the sku's A,B, and C using Pareto analysis. I also filter out any items that are in out-of-season or discontinued that might pollute the findings.
It is usually easy to run a stock status report daily, and pull it into Excel - maybe using vlookup.
Others might criticize this effort, saying that "service level" measurements or other indicators are better...fair comment, I guess, but out-of-stock stats are easy for others to understand. And it is a simple, easy to compute tool.
I like the process of having your shipping manager flag stock-outs when they happen. I am not familiar enough with your processes to properly comment, but when I worked in wholesale, we would run any customer orders through a predisposition file before the orders hit the warehouse floor - if the computer system identified an item as "out-of-stock" the system would pull that line item order aside and hold it in the order bank. The order would never be released to the warehouse until the goods became available. So, the shipping manager would only be identifying errors in inventory record accuracy (i.e. the system thinks you have 10 on hand, but the order pickers could not find the product.) If this is the case with you, then you would need to run the stock status report to capture all of the stock-outs.
I recommend that you publish the stats over time. Publish the stats daily, and you can even graph the results. Once you gather a few months of data, you can reveal some really cool trends.
It is important to go beyond just reporting the stats - I know that you know this - to identify root causes. You could do a little brainstorming with a fishbone diagram to identify what the root causes might be - then tarcj the actual causes. You might be able to show that 90% of your stock-outs are caused by vendor compliance problems, and another 5% are caused by forecast error...bla bla bla...
Hope this helps. Any more questions, give me a shout.
To which my colleague responded:
Looks good. Just to keep it simple (I can look into sorting by ABC etc once I get it all figured out) how do you measure % Out of Stock? For example. Let's say in October I only had 2 things run out of stock. [Product X] and [Product Y]. Out of the 40 items I order from that supplier, those 2 items were out of stock for 2 days once during the month.
How do I measure that? Is that the right example?
To which I responded:
My view is that your total potential stock outs for a month is 40 items x (31 days minus 5 Saturdays minus 5 Sundays = 21 "working days") = 840. In other words, there are 840 potential working item-day combination where you could suffer a stock out in that month.
Your daily % in stock for October 1 is (40-2)/40 = 95%
You were stocked out for 2 days, so:
Your daily % in stock for October 2 is (40-2)/40 = 95%
Your month-to-date % in stock as at October 2 = (38+38)/(40+40) = 95%
Assuming that you were back in stock on October 3 (obviously, your daily % in stock = 100%),
Your month-to-date % in stock as at October 3 = (38+38+40)/(40+40+40) = 96.7%
Your month-to-date % in stock as at October 4 = (38+38+40+40)/(40+40+40+40) = 97.5%
and so on. At the end of the month,
Your month-to-date % in stock as at October 31 = (838)/(840) = 99.8%
As I mentioned, someone with a PhD in Statistics will poke holes in this approach as it fails to account for other issues such as sales missed, ability to recoup lost sales, service levels, and so forth. If, for example, you were running a very important and time-sensitive promotion on October 1st and you stocked out at that time, you should punch yourself in the face for making the mistake. The approach that I have shown is simple, but I think it is effective. Using this method, you can approach your boss with hand-on-heart and say, "Yes, our % in stock in October was not perfect, but it was pretty damn good - "world class," in fact - and when a mistake was made we identified it, found out what caused it, and recovered fast."
One last hint - I like using "% in stock" rather than "% out-of-stock" as it tends to impart a more positive perspective on things.
And I was pleased when my colleague responded:
Ok - let me work on this for a bit and stew over it. I think I'll get back to you but it makes PERFECT sense. BTW - our shipping supervisor does this report based on what his people tell him. "Were out of this!". That's how I'll know. We can often get something down from our warehouse to fix it. So I need to setup something in Excel and track it daily by the sounds of it because I can't assume I'll be out of stock until the vendor arrives.
Making KPI's too technical and running your business solely off "the numbers" is a poor mistake to make. "Profit is the inevitable conclusion of work well done", as Mr. Ford said - not metrics well done!
I love this one. It's easy to understand and easy to improve on. I'll get back to you in a bit after I try getting it setup!
Our conversation later led to the topic of computing inventory turnover. More on that later.