What if you had access to a way of allowing you to easily and factually accelerate your logistics lead times from goods receipts, to making your delivery service level more reliable, to limiting overheating of fragile products or theft risks, or even shortening transport routes?

e-VSM (Value Stream Mapping) helps you answer operational questions about your logistics flows and optimise them.

Mastering your logistics flows highly contributes to your operational performance and customer loyalty, in terms of service, costs, stocks, but also environmental footprint. As a result, keeping them efficient and under control is key.

When your service levels deteriorate or when you decide to try alternative routes, analysing your current performance and knowing what to do can be challenging. For this you need precise, reliable, and representative data that gives you enough visibility and objectivity to make the right decisions.

However, getting detailed data about complex logistics flows, such as multimodal transport and cross-dock operations, is not always easy. It involves various internal stakeholders and subcontractors, which often results in incomplete and outdated data.

Most logistics service providers manage data in their own information systems such as ERP, WMS and TMS tracking platforms, however data can be scattered, formatted differently, complex to link together and analyse. For example, proofs of delivery are usually hard to collect, making the real delivery service level impossible to calculate.

Players are not always willing to collaborate and communicate details, or in the case of subcontracting, do not even have access to those details themselves. In some cases, it might be better to conceal the ongoing diagnostic from the parties involved in the flow to avoid a temporary behaviour change such as theft.

Finally, there may simply be no satisfying data in existing systems, since they mainly record status or specific events, for example, shipments and receipts. Values such as temperatures, handling conditions, or exact road route are therefore not captured.

Another method to observe a delivery process is to go directly on the field but it tends to be costly, limited and reflects only the reality of the day and place of observation.

e-VSM is an innovative approach that gives you the opportunity to have real-time end-to-end visibility on all your flows and identify optimisation levers.

It relies on IoT technology and on the ‘Zero G’ (or 0G) network developed by Sigfox, who specialise in flow monitoring and IoT, to automatically track, from end-to-end and almost in real time, all your flows to optimise.

A representative sample of deliveries can be carefully tracked by appropriate trackers collecting data throughout the flow such as location, temperature, and movements. In order to integrate and manage this new data as well as ‘traditional’ data like shipping and delivery addresses, we developed an application with a processing engine that automatically converts this raw data into meaningful supply chain indicators.

Examples of these indicators include time, dispersion, service rate, total distance, number of stops, CO2, temperature thresholds, shocks and percentage reliability of milestones. These indicators are then visualised in an interactive BI (Business Intelligence) dashboard combining the overview and details. It allows us to factually describe flows to identify possible sources of underperformance and to build an action plan with the flows’ stakeholders.

We use our toolbox to quickly integrate and analyse data

 

Thanks to this method, Guerlain now have a better understanding of its supply flows. They tracked 100 batches from end-to-end, across several sites and warehouse platforms throughout France and Spain. This made it possible to challenge several components of how the flows were organised: synchronisation of production and logistics; synchronisation of flows on platforms; transportation means (dedicated or grouped, delivery order); flows run in parallel. In total, a potential lead time reduction of 50 days (-50%) was identified, of which 5 days came from transportation only.

La Redoute also uses this technology and approach to audit its flows in France, for instance to detect unusual delays when returning uncollected packages.

Other companies use a similar methodology to understand and optimise their intercontinental flows. Michelin tracks thousands of shipments of containers per year and reduced the delivery time on different routes by several days thanks to Safecube – a joint venture start-up between Argon & Co , Michelin, and Sigfox, specialized in flow monitoring and IoT. This method could be used to analyse temperature, either for focusing on this issue or in addition to lead times.

Benefits go beyond the scope of shipments or tracked flows. Simply visualising a part of your flows can contribute to streamlining the chain, empowering stakeholders, and discouraging deviant behaviours. Potential thieves are less tempted to steal if ‘package traps’ are introduced.

This type of temporary tracking can be a first step to operational tracking. Besides gathering data for a given flow, this method can be implemented to trial new tracking concepts and validate its value. It can then be turned permanent on some key flows.

This pragmatic method is a clear improvement in comparison to existing methods thanks to:

  • its short implementation time (the only requirement is to order trackers beforehand)
  • its factual, representative, and field-based data
  • its automatic and end-to-end data capture (the only thing to do is to inject trackers and get them back)
  • its ability to be deployed on small or large scales, depending on the company needs
  • its adaptability, which addresses several logistics issues (sometimes even at the same time)

 

If you have any question, do not hesitate to contact us. e-VSM can help you shed light on, and optimise your logistics flows.

Authors

Bejnamin Grise

Senior Manager

benjamin.grise@argonandco.com

Clovis Ravion

Manager Digital Factory

clovis.ravion@argonandco.com

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