The Evolution Of Supply Chains – Part 3: Out Of The Silo, Into The Hive

In our previous two blogs, we learned that the problem with Supply Chain Management (SCM) lies in the word ‘chain’. It forces us to think in sequences where ‘B’ can’t begin until ‘A’ is completed.

Don’t get me wrong, there are many physical operations where that is true. But a large part of SCM requires us to assess and evaluate multiple options over many variables, often reaching temporary or contingent results until further data is available.

This is beginning to put serious constraints on supply and logistics practice. Because our business relationships are going to be increasingly complex and fluid – certainly not ‘chains’.

Responding to change before it happens

Conventional SCM tries to build in certainty through long-term contracts and commitments but risks missing out on fleeting or emergent opportunities. Supply needs to reconfigure itself to respond to change as it happens, and often before the full picture is clear.

Further improvement in both economic and environmental performance can only come about through greater collaboration. Often this will be with complete strangers and may only be for a single event.  Coming together on the back haul for a particular truck journey, for example.

In these and other areas the aim is to optimise operations over a wide range of factors, for everyone involved. These might include total cost of ownership and total environmental impact.

But conventional ‘chain’ thinking almost inevitably creates ‘silo’ thinking.

Best outcomes

Instead we need to look simultaneously across all the possible options to find the ‘best’ outcome according to our criteria. Often with incomplete information, or in the knowledge that current conditions may change by the time the plan is executed.

Advances in computational science, and computer power, mean that these approaches are now feasible. TGMatrix’ Intelligent Freight Planner offers a small example, where thousands of possible matches of load and capacity can be compared simultaneously across criteria of cost and time and carbon emissions and so forth.

Other advances are on the way with machine learning and AI having much to offer. But for society to get full value, we need to stop thinking in terms of linear chains with risk supposedly nailed down by a long term contract at each link, and instead learn to embrace the more distributed risks and rewards of a constantly changing supply ‘hive’.

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