On-Demand Apparel Manufacturing/ Dealing with waste and overproduction in the Apparel Industry.

Dharmen Hariprashad #dh2733

  1. Sustainability problem: Waste/ Apparel industry overproduction.

  1. Technology Summary: On-Demand manufacturing is a production model when goods are only made when or as they are required. It is also referred to as made-to-order in the fashion industry. In the wake of COVID and the adoption of digitization by consumers and new software innovations (3D Scanning), it has allowed brands to experiment and initiate new ODM processes. Also with an emphasis on sustainability and reducing carbon emissions, ODM is one way to deal with the overproduction problem in the apparel industry.
  • On-demand manufacturing allows companies to produce only what consumers order with a faster response time. It eliminates unnecessary production and harmful waste—this can be profitable for the brand and at the same time good for the environment. 
  • OnDemand Manufacturing is currently only used by smaller brands and new startups; however, new innovative software designs may allow larger companies to scale the process. 
  • Applying digitization to fashion will allow brands to redesign their manufacturing process. Manufacturers will be able to move away from a model where they estimate how much goods to produce and only make what is needed. 


  • Retailers
  • Consumers
  • Software Developers
  • Fabric Manufacturers
  • Brands
  • Trade Organizations.


  • Create a seamless process with the use of software for the OnDemand manufacturing process.
  • Educate consumers on the process of OnDemand manufacturing and its benefits to the environment. 
  • Create an OnDemand supply chain for retailers (partners) and direct-to-consumer channels.



Circular Content Management System (CCMS): Track and Trace Tool for Supply Chains


Technology and Background

Circular Content Management System (CCMS) is a cloud-based platform developed by Improvement IT with Netherlands-based clothing manufacturer Dutch aWEARness. All garments produced by Dutch aWEARness are 100% recyclable and are leased to the consumer who returns it after use so the materials can be reprocessed back into raw materials. Though a relatively small company, Dutch aWEARness aims to promote the circular economy and collaborate with other designers, retailers, manufacturers and others involved in the textile industry. Dutch aWEARness has used a track and trace barcode system for all of its products and materials and is now making the platform, CCMS, available for others in an effort to increase transparency and facilitate the circular economy.

How CCMS works:


  • Data input  (i.e. suppliers, processes, energy, raw materials, transportation modes, etc.)
  • Batch code is created for every step of the chain and linked together
  • QR code generated for final product
  • Scan shows all materials, partners, environmental impact and a Google map of the product’s route

Sustainability Problem

Textile waste takes up approximately 5% of U.S. landfill space according to EPA estimates and only 15% of textiles are currently recycled in the U.S. By promoting the circular economy, the CCMS enables greater accountability and transparency regarding raw materials and waste in the textile/apparel industries. The tool not only facilities better supply chain practices and facilitates the circular model for manufacturers but enables consumers to have a better understanding of the sourcing and environmental impact of the product. The tool in of itself does not solve the problem but facilitates the transition to a circular model that will reduce waste, encourage re-use and recycling of materials, and lessen the environmental impact of textile production.


  • Apparel industry
  • Textile manufacturers
  • Clothing retailers
  • Suppliers
  • Consumers
  • Governments (Dutch aWEARness has partnered with the European Commission’s Eco-Innovation program)
  • NGOs and activists
  • Software developer, Improvement IT

Technology Implementation

The platform is leased using a subscription-based model and training and maintenance is provided. Potential challenges are scaleability and cost. The data tracked is based on input provided by the user so accuracy of data input would be crucial to the tool’s performance.