Lateral transshipment and stock redistribution for a sustainable fashion

Circular economy is a concept according to which waste can be designed out of an economy through continuous circulation of products and materials. The implementation of circular economy thinking to reduce environmental impacts and resource use has led to the development of innovative business models.

“The promotion of sustainable fashion within a circular economy is a vital contemporary logic.”

Kirsi Niinimäki

After looking into product lifecycle to assess the Global Warming Potential (GWP) for textilesJarkko Levänen concluded that Reduction is the solution with the highest GWP benefits, and that many circular business models “come with a high rebound risk, and existing practices carry similar, yet smaller risks”.

” The lowest global warming impacts are achieved in the Reduce scenario.”

Jarkko Levänen

Innovative business models driving the use of lateral transhipment and stock redistribution are fundamental to the Reduce scenario. Lateral transshipments within an inventory system are stock movements between locations. These transshipments proactively redistribute stock.

“The increase in information sharing coupled with a lean supply chains produces more opportunities for lateral transshipments.”

Colin Paterson

Optimal control of lateral transhipments (occur at the same level within the supply chain) has been researched in many different settings – using lateral transhipments can save up to 50% of annual inventory related costs. Value can be enhanced further if also considered the economic and environmental cost of inventory returns.

“Businesses that sell clothing are most likely to be hit by the $115 billion returns predictions. Returned items have ended being binned showing how this isn’t only an issue for businesses and brands, but also for the environment.”


Stock redistribution can be done at the beginning of the period, at a specified point in time during the period, or determined dynamically according to the observed demand – dynamic policies can outperform static approaches by more than 30%.

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