We are back with another edition of our six-sigma blog series. This time I will cover the benefits of using six-sigma to optimize logistics in order to improve your organization’s cost lines.
Before I begin my discussion I would like to mention that anytime I talk about ‘Lean’ , I’m referring to the methodology of waste elimination in processes. Similarly, when I mention the word ‘Six-Sigma’ I will be referring to the methodology of variation reduction in processes.
Why optimize Logistics?
According to a report published by the American Trucking Association in October 2015:
In 2014, the trucking industry was short 38,000 drivers. The shortage is expected to reach nearly 48,000 by the end of 2015. If the current trend holds, the shortage may balloon to almost 175,000 by 2024.
Over the next decade, the trucking industry will need to hire a total 890,000 new drivers, or an average of 89,000 per year. Replacing retiring truck drivers will be by far the largest factor, accounting for nearly half of new driver hires (45%).
The full article can be found here.
America has a shortage of truck drivers and hence the demand far exceeds the supply. We are so short that you could have made six figures just by being a sand hauler during the frac boom. Not optimizing logistics can cause excessive amounts of down-time for organizations due to the limitation on the number of truck drivers available. Organizations need to optimize logistic paths as much as possible to ensure they’re maximizing their utilization of truck drivers in order to make sure they can add to their bottom line.
Let’s say you have a truck driver named Joe who works 60 hours a week to serve 3 organizations A, B & C. For the sake of simplicity, let’s say Joe divides his total time by 3 to serve each organization i.e. he will drive 20 hours for organization A, 20 for B & 20 for C.
Now let’s say organization A has controlled all of its internal processes so when Joe arrives to pick up parts from them, he picks up the parts, loads them up and drives off to the end-user. His utilization is more than 100%. 100% of course is not practically possible as the loading and un-loading process takes time as well as the paperwork etc. But let’s say organization A has also identified the optimal routes (more on this later) for Joe so he always gets parts to the end user on time with minimal delays.
Organization B on the other has not gotten all of its processes in control. Whenever Joe gets to organization B, he has to wait for a minimum of 3 hours before the parts are ready to load. Due to the delays, Joe also has to expedite delivery which organization B pays extra money for and the customers don’t always get the parts on time. Furthermore organization B hasn’t optimized the logistic routes so Joe often encounters traffic congestion or bad road conditions when driving. Organization B clearly isn’t utilizing Joe to his maximum potential.
Organization C is a mix of A and B. They don’t have their processes completely in control but have optimized their logistic routes very well so Joe’s utilization is close to 75%.
Let’s analyze the above scenario
Organization A is utilizing Joe to his maximum capability as he wastes minimal time waiting for parts when arrives at A. A has selected the routes with the lowest mean time to destination and the lowest variance. Furthermore, A has analyzed the logistics path in such a way that the time taken to get to each route is in ‘statistical control’, meaning 99.7% of the time Joe arrives within 3 standard deviations of the mean. The end-user can utilize this to plan out their processes better as Joe’s delivery times from A are predictable. They can stock up their inventory in a way that takes into account the worst case scenario of parts delivery.
Organization B on the other hand is a complete mess. They have no idea what ‘statistical control’ is and haven’t incorporated data from the past to plan out their logistic routes. Not only does Joe have to wait 3 hours to get the parts loaded up but since his logistic routes aren’t optimized, he spends excessive time driving which in a way further under utilizes him. Joe doesn’t care because he gets paid by the hour but organization B ends up wasting a lot of money due to poor management of logistics processes. Due to delayed shipments, they also will lose their customer base as their customers will prefer suppliers like organization A that are more predictable.
If organization B uses six-sigma concepts to use the logistic routes that are in ‘statistical control’ and fit a ‘normal’ distribution, they could save a lot of money as the faster Joe delivers their products, the more products they can ship out resulting in a positive impact on the bottom line.
Organization C utilizes six-sigma concepts to optimize their logistics routes but they are still not as good as organization A. They need to systematic change to reduce the means of their driving times while keeping the variance as low as possible.
What can we learn from this?
The biggest take-away from the above article is that six-sigma can optimize logistics by showing you which logistic routes are in ‘statistical control’ and predictable. This makes the planning phase much easier and your customers can be satisfied since they can predict when they will have the parts on hand. You can then work on reducing the mean and variance even further by using continuous improvement tools like the DMAIC methodology to figure out the root cause of the high mean and systematically reduce it.
Keeping all other factors constant, ‘Out of control’ delivery times can cause customers to overstock on their inventory which ends up affecting their bottom lines as at the end of the year they have to scrap a bunch of parts to clear their accounting books. Thus optimizing logistics not only saves you time and money but also helps your customers plan better.
RigBasket’s goal is to make sure we provide you with the most useful information to help improve your processes. Schedule a demo to see how we can improve your logistics operations.