Can Six-Sigma be applied to inventory?

Hi Guys,

It’s been a while since you’ve heard from the RigBasket team. As an early stage start-up we’ve had a “surge” in demo requests and have been with our customers. We’re finally able to take a step back and catch up on the blog. This will be quite a controversial post as the term ‘Six-Sigma’ raises a few eyebrows when mentioned. This particular blog post also highlights the core philosophy of RigBasket, using six-sigma to improve inventory flow.

Now I need to mention a very important point before beginning the discussion. When I use the term ‘Six-Sigma’, I’m referring to the methodology that applies to reducing process variability. When I mention ‘Lean’, I’m referring to the methodology applied to waste elimination in processes.

Inventory  & Balance Sheets

The Generally Accepted Accounting Principles, GAAP, have considered inventory to be an asset for the longest period of time, but is that truly the case? Hold on, we are not challenging the GAAP, but we wanted to merely suggest that current business activities may have outgrown previous classifications. Is inventory really something that helps businesses make more money and improve their cash flow? Perhaps if you consider demand for items to be somewhat inelastic i.e. not fluctuating with price or other market conditions, then inventory might be an asset.

However, that is rarely the case. Most times the demand for items fluctuates and not all items stored in inventory can be liquidated at will for cash. On the contrary oftentimes organizations tend to stock up on excess inventory and realize the problem at the end of the year when they have to scrap a lot of it to clear their books. So does inventory really qualify as an asset or is it more of a liability?

Based on the argument above, the latter seems like the most feasible option. Inventory build-up hinders cash flow and at the end of the day ‘Cash is King’ for all organizations, therefore any non-cash generating activity should be a liability and not an asset. If organizations followed the theoretical ‘Just in Time’ principles, there would be no inventory storage, however due to various reasons that is not feasible in manufacturing. However, what is feasible is making sure your inventory flow or velocity is maximized to avoid excess build-up causing low cash on hand.

Where does six-sigma fit in with all of this?

Six-Sigma is a methodology for reducing process variance using a bunch of statistical tools. Just like any manufacturing process, inventory should flow with minimal variance. What I mean by that is there shouldn’t be huge fluctuations in inventory levels for organizations. The inventory levels should always be under ‘control’.

Say you have a certain quantity of Part X in stock. On average, you always have 6 parts in stock with a standard deviation of 2. Considering your inventory follows a ‘normal’ distribution over time, 67% of the time you will stock between quantity 4 and 8 of this part. 99.9% of time you will have between 0 and Qty 12 of this part. Only 0.1% of the time will you have more than 12 pieces on hand since your inventory can never go below 0.

Now if you have all your manufacturing processes in control, you are level loading to make sure your production is well distributed across the year based on demand, you have a good understanding of your demand and your supplier metrics are in control, then your inventory will always be in control. However, if you haven’t gotten some of these processes in control, which is what is true for most organizations, you will have certain months when your inventory will “surge” up to say 16 and other months where you will run out.

In order to compensate for this lack of control, organizations tend to have a huge inventory ‘buffer’, just to make sure they don’t run out. However, come the end of the year they realize that due to overstocking, they now have to scrap some parts to attain financial targets. In the case of goods that expire like chemicals, you might have to scrap multiple times per year leading to massive waste.

Conclusion

The message I am trying to convey here is that if your inventory variance is under statistical ‘control’ and follows more of a ‘normal’ distribution, your operations are well in control. However, if you are having issues with your supplier quality or manufacturing processes or poor demand forecasting, the first area that will indicate the problem is inventory. If your inventory is in control, you can further optimize your processes to cut down the variations even further and reduce the waste generated.

At RigBasket, our solution is designed to help organizations detect operational and supply chain issues quicker without having to spend excessive time on gathering and analyzing data to come up with improvement projects. Schedule a demo today to see how we can help you save millions,  and how our tools will effectively “pay for themselves”.

As always we would love to get your feedback and thoughts. It is our goal to make sure we provide valuable information to all our readers and help them improve their operations for a better, less wasteful world.

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