Predicting Lead Times, Throughput and Variability

Digital Supply Chain
Education Series
Predicting Lead Times,
Throughput and Variability
WE
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FUTURE
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Dennis Groseclose
CEO, TransVoyant | VOLUME 9

©2019 TransVoyant | We Predict the Future
Digital Supply Chain
Education Series
Predicting Lead Times,
Throughput and Variability

What disruptions pose the greatest risk to today’s supply chains? Is it black swan disruptions, supplier disruptions,
manufacturing disruptions, price ﬂuctuations, competitor actions, logistics provider disruptions, I/T disruptions?
While all these single events can be devastating to a company’s operations, I contend that unanticipated variability causes
more pain over longer periods of time.
What do I mean by variability? Variability comes in many forms. Some of the most common forms of supply chain
variability include supply variability, manufacturing throughput variability, transportation lead time variability,
demand variability, and quality variability. Why are these things so damaging to a company?
While the one-time impact of a black swan event can be devastating to an organization in the near-term, supply
chain variability, in its myriad forms, continuously chips away at an organization’s operational eﬃciency, trading
partner trust, customer satisfaction and shareholder value like ocean waves erode a beach. It’s a constant pressure.
Predicting Lead Times, Throughput and Variability

Let’s consider transportation lead time variability, as one example. Let’s say the transportation lead time on an inbound
ocean lane is highly variable. On one occasion, it takes 25 days from port to port, the next time it takes 35 days, the next
time it takes 20 days. The average lead time for that lane across these three trips is 26.7 days. Unfortunately, the standard
deviation is terrible at 6.2 days.
How is a company supposed to plan its downstream operations with this kind of variability? The shipment could arrive as
early as 20 days or take as long as 35 days. If the company’s manufacturing operations are reliant on the inbound parts
arriving on time, it must either always plan for a 35-day transportation lead time, or hold enough buﬀer inventory of the
inbound parts to oﬀset the variability.

We all know how this works. It’s supply chain 101. The more buffer stocks a company holds to offset variability, the bigger
the impact to cost of goods sold, cash flow and cost of capital. If a company doesn’t hold enough buffer stock, it will see a
deterioration in revenue through eroding customer service levels. It will also see inefficient use of capacity across
manufacturing, warehousing, store and delivery operations.
Now consider this situation not just for this single transportation lane, but across all transportation lanes inbound to all
global manufacturing facilities or distribution centers. On that grand scale, holding either too much or too little buffer
inventory adversely impacts financial performance.

So, how can the company improve this situation? One solution is to accurately predict and avoid the disruptions that cause
the transportation lead time variability in the first place. If, for example, the reason certain shipments on an ocean lane take
35 days instead of 20 days is because an ocean carrier makes unscheduled port stops en route, the company can
significantly reduce its lead time variability by learning and then predicting which carriers are more prone to make those
unscheduled port stops, and select carriers who don’t. Digital supply chain solutions do just that.
Digital supply chain solutions watch, model and learn the behavior of all ocean carriers on all lanes under varying
circumstances. Some carriers are more prone to make unscheduled port stops when spot rates are higher. They do this to
boost revenue on a voyage. Because digital supply chain solutions have been watching these kinds of behaviors for years,
they know which carriers are more likely to make unscheduled port stops, on which lanes, when spot rates reach a certain
level. When spot rates are high, the digital supply chain solution predicts a 35-day lead time for the opportunistic carriers,
and a 20-day lead time for the carriers known to go direct regardless of spot rate fluctuations.

The implications are significant. By consistently, and dynamically selecting and booking the carriers who are predicted to perform
better on certain lanes based on behavior, a company can reduce lead time variability, and obviate the need to carry so much
buffer inventory while maintaining the same customer service levels. If the company were confident an ocean shipment on a lane
had an average transit time of 22 days with a standard deviation of two days versus a transit time of 35 days with standard
deviation of six days, picking the higher performing carrier not only shortens cycle times to improve order-to-cash, but also
reduces variability and its downstream impacts.
It’s this continuous understanding of behavior and the dynamic recalculation of lead time variability by lane and node that enables
digital supply chain solutions to inform inventory planning systems. The function of an inventory planning system is to factor
predicted supply and demand, and related measures of variability, to right-size inventory across an organization’s supply chain.
There are plenty of capable inventory planning systems that calculate inventory levels by node today. The problem is, in almost all
cases, these systems are being fed with bad information.

Most companies perform network optimization studies once or twice a year. During this exercise, they calculate average
lead times for transportation lanes, average throughputs for manufacturing facilities, manufacturing quality (loss), average
turnaround times at dock doors, cycle times at warehouses, etc. These “studies” rarely look at outside forces impacting
ﬂow averages and variability such as border crossing behavior, customs clearance behavior, port turnaround behavior, etc.
Being averages calculated over long periods of time, these lead times are inaccurate and far too static to be of real value.
They don’t account for dynamic events or changes in behavior and they lack insight into world behavior impacting those
limited nodes enterprises control. Lead times on ocean lanes vary greatly by the amount of congestion at the destination
port, turnaround times, container dwell times, the severity of the weather en route, and the propensity for carriers to make
unscheduled port stops. Border crossing times vary based upon seasonal and external factors such as weather. Lacking an
understanding of how these external events impact behavior (a.k.a., lead times and variability), a company has no other
option than to use average lead time approximations established once or twice a year.

Digital supply chain solutions continuously re-calculate predicted lead times, throughput and variability at a very granular
level – by lane, carrier, port, airport, route, supplier, manufacturing facility, warehouse, border crossing etc. By feeding
inventory optimization solutions much more dynamic and accurate lead time and variability information, inventories can
be right-sized on a more dynamic and accurate basis. Instead of calculating inventory levels network-wide using the same
lead times and variability numbers established once or twice a year, organizations are now using digital supply chain
solutions to right-size their global inventories at a nodal level on a weekly or monthly basis.
The inventory savings of doing so are signiﬁcant, not to mention the improvements in customer service by fulﬁlling
orders on-time and in-full more consistently, or the improvements in asset utilization by reducing manufacturing or
warehouse downtime.

And remember, the use case described above (transportation lead time variability) is but one of many along the supply
chain variability spectrum. These same principles apply to all other forms of variability. By continuously watching,
modeling and learning supplier throughput and product quality, a company’s own manufacturing throughput and
variability, warehouse throughput and variability and demand variability, digital supply chain solutions help organizations
to both avoid the root causes of variability and to better plan downstream operations for the elements of variability that
cannot be squeezed out.
This means reduced costs, increased revenues and service levels, and improved capital eﬃciency – all hallmarks of digital
supply chain solutions.

To learn more visit w w w.transvoyant.com

Predicting Lead Times, Throughput and Variability

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