Optimising supply chains in shipping

Losing tens of millions of dollars every year in scheduling penalties, an industrial transport company, The Noble Group, turns to operational research to optimise their supply chain. By using reactive deterministic algorithms they were able to achieve savings of approximately $1.3m per month.

Large container ship out at sea piled with shipping containers.

The Problem

The Noble Group specialises in transporting industrial and energy products by moving physical commodities from producer to consumer and managing the market, credit, and operational risk associated with that.

One of Noble’s most profitable activities is the transportation of coal and the journey from mine to port is almost invariably made by barge. Noble owns its own fleet of barges, has long-term contracts for the lease of others and must occasionally hire more on a spot basis.

Noble used a manual scheduling procedure that logistics managers had to perform many times a day. It lacked flexibility and they frequently had to resort to using rough calculations and rules of thumb.

Trying to make calculations in the absence of a proper decision-support system can result in wild inaccuracies and the ultimate outcome was inefficiency. The group was accustomed to losing tens of millions of dollars every year in supplementary charges incurred by hiring additional barges and failing to meet schedules.

Noble desperately needed a procedure that would enable it to strike an economically satisfactory balance between hiring barges on a leased or spot basis and avoiding the financial penalties from late deliveries.

The Solution

It was essential to equip managers with the ability to access with confidence three key considerations:

  • How many owned, leased and spot barges to allocate to each vessel
  • When to dispatch each barge
  • Whether to hire a floating crane to speed up barges’ unloading.

The solution, known as The Barge Rotation System, was devised to integrate large quantities of information in a way that would assist logistics managers rather than confound them.

The first step was to arrange all the relevant data in a single spreadsheet model. By separating factors on the basis of update frequency it allowed users to complete data entry in a matter of minutes, rather than the many hours it had taken previously. The simple Excel based graphical user interface ensured that it could run without specialist software.

A reactive deterministic model was used in making algorithms that would incorporate the uncertainties; vessel arrival dates, loading times, and supplier availability for example.

The first algorithm ensured that the quantity of barges and the number of voyages allocated would not exceed the maximum number of available barges. The second algorithm creates a feasible schedule and a final algorithm then decides whether substituting owned barges with leased or spot barges would result in a lower cost.

The Value

Within 18 months the application of state-of-the-art OR was saving Noble approximately $1.3m a month. The system also yielded some surprising qualitative findings such as that batch hiring can represent an optimal course of action during busy periods.

“The implemented system has improved the financial performance of my division” confirms Noble executive director Tim Gazzard. “It has also helped planners to improve their understanding of operations and other departments to appreciate the complexity of logistics and understand better how their decisions influence the logistics operations.”

Many large maritime businesses continue to make complex operational decisions manually by using intuition and limited data and this needn’t be the case. Advances in optimisation mean we can now address a much broader class of challenges in the maritime industry and provide a range of tangible benefits.

Full article available in Impact Magazine, Autumn 2016. ‘Shipping Forecast’