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Newton.Vaureal Consulting - Logistics and Supply Chain Management - Paris
+33 1 40 17 04 03



While the Supply Chain is affected by operational hazards – in particular its logistics component – today it is also exposed in more subtle and insidious ways to acts of malice and/or major risks that have far more serious consequences.

There are words that reveal epochs. This is the case with risk: risks to customer service, risks of loss of products, risks for people, risks to image etc. etc..

Risk management in Supply Chain involves three key words: anticipation, training and observation. We must anticipate the unthinkable, prepare for it and be watchful – become, in fact, just like an alert sentry.


The construction of a BCP (Business Continuity Plan) is a good guide to mobilizing energies and interests related to unforeseeable risks – ranging from the unexpected need to partially or completely shutdown a warehouse to that of inability to use a specific mode of transport.

Our feedback suggests that BCPs encounter three main difficulties:

  • Expression of objectives in defining recovery time to that of a normal situation during a transition period.
  • Involvement of logistics providers in the process – since much of the success of the process depends on it.

Involvement of senior management in these topics.

Prepare and train teams

The weakness of the majority of BCPs is that they are not “enacted” regularly.

If a plan has been well prepared, the only means of effective appropriation lies in training. A Supply Chain is in action daily, so we assume that this action is worth training for. But what about the unexpected? Training and preparation are necessary to confront it voluntarily.

To decide is to resolve.

Decisiveness in action is prepared for by practice.

Stay alert and observe

This is where Big Data and Analytics tools demonstrate their value. New generations of tools, such as IBM’s Watson or GPS from Business Investigation, focus on information flows that are endogenous (internal) or exogenous (external) to a company and have the capacity (after a period of self-parameterization [machine-learning]) to identify those weak indicators that are often associated with increased risk.