The Opinion Page
News and comments about the issues facing today's SCM and Inventory Management professionals.
In a report released in August, 2011, research firm The Aberdeen Group of Boston, MA found that a formal process of Sales and Operations Planning was a key enabler in companies who were able to achieve best-in-class results in customer service levels, average cash conversion cycles, forecast accuracy at product family levels, and in gross margin rates realized.
Author Nari Viswanathan, Vice President / Principal Analyst, Supply Chain Management at Aberdeen discussed benchmarking survey findings that North American business face three predominant pressures: to reduce supply chain operating costs, to improve the management of increasing demand volatility, and to improve top line revenue. Firms surveyed also reported pressure from customer mandates for faster, more accurate and more unique fulfilment as well as a need for tighter integration between planning and execution. Managing demand forecasts within an S&OP framework, and integrating the financial planning and budgeting processes within the S&OP process were seen to address these pressures.
Top performers – those earning best-in-class status – had an average cash conversion cycle of 46.4 days, a perfect order rate of 92.8%, experienced 75.3% forecast accuracy at a product family level, and enjoyed a gross profit margin rate of 43.5%.
Put simply, Sales and Operations Planning (S&OP) is a business process that facilitates the balancing of supply and demand. Occurring over a monthly cycle, it is cross-functional, involving the participation of staff from various business units within a company, including Sales, Marketing, Production, Distribution, Finance, and Product Development. S&OP is meant to bring together all the plans for the business into one integrated set of plans.
S&OP was first developed by Richard C. Ling in his pioneering work titled Orchestrating Success: Improve Control of the Business with Sales and Operations Planning (1988). Ling devised a process that would improve upon its predecessor, known as “Production Planning.” Production planning is a process that demands that Sales and Marketing devise a forecast and hand it off to Operations, who execute the plan through functions like Master Production Scheduling.
S&OP, on the other hand, requires that the barriers between Operations, Sales, and Finance families be removed – that a single comprehensive plan is developed in a collaborative manner. Forecasts are articulated at aggregate (product family) levels, rather than at item levels of detail. Differences that frequently arise between various functional areas within the business are reconciled before the plan is set to execution. In other words, in a “Production Planning” environment, sales plans and operations plans are sequential; in the S&OP case, sales planning and operations planning occur jointly.
While S&OP was initially targeted to improving business processes in manufacturing firms, the principles apply equally to any firm engaged in forecasting future needs: wholesalers, retailers, and service industries can all benefit from what S&OP has to offer.
Thomas F. Wallace of Cincinnati, Ohio has worked feverishly to promote the benefits of S&OP since 1999. His book, Sales & Operations Planning: The How-To Handbook provides a concise and practical summary of the process.
Wallace explains that the monthly S&OP process occurs essentially in five stages, with Steps 2, 3, and 4 being characterized as the “heavy lifting” activities:
I have been corresponding recently with a colleague who is striving to improve inventory management practices in his business. We had been discussing the importance of inventory record accuracy, which led us to two separate but related issues: stocks-outs and inventory turnover. Here was my friend's question:
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.
John Skelton is the Principal Consultant and founder of Strategic Inventory Management.