The Pay Per Part Model
A typical manufacturing service model often looks like this: a manufacturer purchases a piece of high-tech equipment from a supplier. They then need to have enough demand to keep that machine running at a capacity that delivers adequate return (as well as managing skilled staffing requirements, maintenance costs, upgrades, etc.). The constraints to all stakeholders in the design, procurement, and design cycle are evident.
The pay-per-part manufacturing model enables a specialist manufacturer to use a full-service machine without buying or leasing any equipment. Instead, they pay a previously agreed price for each produced part. For a laser cutter, this would be the price for each cut sheet metal part. In other words, they only pay for what they need. This allows the manufacturer (i.e., the company using the machine) to make their production processes more flexible and react faster to market changes.
Agility for all stakeholders
Typically manufacturing service companies design their business model around long lead times, continual capacity, and high volumes necessary to justify the investment in specialized equipment. The pay-per-parts model presents the opportunity for an entirely new business model that includes smaller batch sizes, shorter lead times, and low-cost product development cycles designed with the security of a set price.
Where previously a service such as a metal sheet cutting facility would require a minimum order volume to ensure relevant costs are met, a pay-per part model allows facilities to offer short runs for prototyping and development by drastically reducing the processing cost. This creates space within the manufacturer’s own working cycle for innovation, testing, and experimentation and enables innovators to iterate designs earlier in the design process. This combination contributes to better end products coming to market more quickly.
Laser-cutting leads the way
The first iteration of the pay-per-parts model is being rolled out in a collaborative project in Germany. Munich Re is covering insurance, IoT service provider relayr, provides the data analysis for the financing model while manufacturing specialist TRUMPF supplies customers with the required production components, namely the machines for their factory lines and the corresponding software and services for manufacturing sheet metal parts. Klöckner & Co., one of the world's largest producer-independent steel distributors, will be a business model development partner and eventually supply the raw material. Initially, the partnership will commence as a project under an agreed-upon learning phase.
The model will see manufacturing services providers gain access to the latest automated laser cutting technologies without the need for upfront investment; in addition, they are given the tools and insight to adjust production volume to demand. The planned performance guarantee offered by Munich Re, means businesses are insured against the financial impact of potential production downtime.
Airbnb began on the premise that you could rent spare rooms in your house to host travelers looking for accommodation. Utilizing under-used resources has been adapted to everything from the extra seats in your car as you drive to work (the initial offering of Uber) to the excess space in your garage (such as the app Neighbor). Now this concept of reorganizing resources and demand is coming to manufacturing.
The pay-per-part business model frees up high-tech manufacturing services equipment from the bonds of long lead times and high volumes, opening the possibility for a new era of customer procurement based on data-driven resource management.
Just like a host on Airbnb communicates their availability through the company’s website, manufacturers can now log their manufacturing capacity via a platform such as German-based Kreatize. The platform allows companies that offer their machine on a pay-per-parts model to mitigate their risk. In case the machine user cannot fully utilize its capacity, it can get additional orders from a platform like Kreatize. Manufacturing service providers feed their capacity status to the platform and, in turn, are offered jobs that match their capacity and expertise.
On the other side, customers can order parts through the platform and connect to the right manufacturer who has the equipment and availability necessary for the job. By accessing hundreds (potentially thousands) of suppliers, customers can expect parts to be delivered faster, as the platform does the hard work of matching capacity and expertise via one customer request. The costs for both parties are clear, determined upfront, and managed through a single source. This model covers traditional fabrication processes (turning, milling, waterjet cutting, laser cutting) and additive manufacturing processes (FDM, MJM, SLS & SLM).
Manufacturers are relieved of sourcing, managing, and retaining customers. Conversely, customers (i.e., engineers, designers) access an intuitive platform that makes ordering parts super simple, significantly reducing the procurement burden. The days of Request-For-Quote processes are over.
A leap towards Industrie 4.0
Essential to the pay-per part and resource sharing model is a robust IoT infrastructure. Data is now at the core of manufacturing as the industry edges towards fully realized Industrie 4.0. The widespread deployment of sensors and smart devices throughout production facilities means massive amounts of data is available to enable advanced algorithms to be applied for decision-making.
Data analytics for industrial processes traditionally relied on conventional statistical modeling approaches. Companies in manufacturing industries have now successfully integrated engineering, science, and statistical modeling tools to develop large-scale process automation platforms. These systems are often known as ‘AdvancedProcess-Control’ (APC) systems. They enable companies to optimize the efficiencies of their machines and processes. For example, an inspection tool in a sheet metal cutting facility might measure cut quality at the source and store the data in associated databases. The APC system, analyzing these output parameters, decides whether the laser settings (the input parameters) need to be altered to adjust for particular conditions.
As more industrial IoT devices and metrics become embedded within automated processes, different types of structured and unstructured data, including sensor data, images, videos, audios, and log files are being collected. More advanced data processing and analysis approaches will allow manufacturers to continue to leverage their data to inform decision-making and identify new or un(der)-used opportunities to improve productivity and efficiency. The growth of the pay-per-part and resource sharing model to work is pegged to broader developments in the quality of the gathering, analyzing, and applying data across the process.
Data-driven manufacturing efficiency, resource sharing, and innovative economic models are successes within themselves. However, the potential for a more sustainable and waste resistant manufacturing industry is essential for growth and innovation in the digitally advanced but resource-constrained conditions of the present. Lessons learned from the initial pay-per-parts pilot will be valuable across the sector and drive the program’s expansion into other manufacturing processes.