Lead time variability is a problem in any supply chain / manufacturing environment. Just-in-time supplies don’t always work out that way: you order too soon or too much and now you’re carrying inventory on your supplies, possibly risking obsolescence. You order too late and even a small shipping delay can put production into a full stop. At the same time, demand variability can create disruptions for your production cycles too.
The ideal is to be able to order your supplies just in time for production needs, to meet the varying demands of customers. Variability exists in all aspects of a supply chain: customer demand, supply, production, and shipping, to name a few. Lead times can change at the drop of a hat, so the goal is to limit that variability.
But how?
Causes of lead time variability
There are many reasons that your lead time can get skewed:
- Transportation delays and scheduling issues
- Customs delays (for international shipments)
- Issues surrounding labor - strikes, etc
- Weather
- Mistakes: in data entry / processing or in routing
- Security
- Stock out at the supplier level
The problem is that forecasting based on past needs isn't always accurate and can't factor in any of the above causes of variability. You need to get to a stage of predicting your needs, as well as possible disruptions to the supply chain.
Closing the gap on lead time variability
Predicting customer demand isn't always possible so lead time variability can be even further skewed, beyond the causes mentioned above, but there are a few ways that you can close that gap a little more, resulting in better customer service response:
- Ability to view the supply chain, from end to end, in real-time ‐ Technology is the key to having this full view of the supply chain, from demand planning through S&OP, production and shipping, with all the concomitant data that goes with this view. Being able to track trends and visualize issues that are occurring, allows you to respond more quickly and find solutions.
- Narrowing down the supply variability issues to better respond to demand ‐ Using data to be able to examine supply trends coming into your production environment makes it easier for you to respond to any and all changes, as they occur. For example, if your supplier for a specific raw material is trending towards delays over the last several shipments, you will be able to see that shift long before it becomes problematic, and adjust your ordering accordingly.
The goal is to maximize supply performance while minimizing any of the causes of lead time variability, both in demand and supply. With the availability of data to allow more predictive analytics to work in your favor, you can develop better processes and start limiting the impact of lead time variability on your production bottom line.
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