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A total cost model of 3D printing

The diffusion of 3D Printing (3DP) is creating an environment in which manufacturing innovation is flourishing. While the technical feasibility of redistributed manufacturing through 3DP has been demonstrated across many industry sectors, the economic foundations of such redistribution are not fully understood. This project sets out to develop a total cost model for 3DP manufacturing operations, as a fundamental precursor to defining viable business cases for redistributed, as well as novel, manufacturing applications (Holmstrom et al, 2014).

To date costing approaches of 3DP have largely focused on investments (ie capital expenditure) and consumables (ie materials). Analyses of these 'well-structured costs' (eg Son, 1991) have observed that fully utilizing the available machine capacity forms a prerequisite for efficient operations (Baumers et al, 2013). This principle is shared with traditional manufacturing (Pinedo, 2012), which exhibits significant economies of scale, and as a result, global supply chains (Christensen, 2001).

This stands in contrast to 3DP where the underlying reason for the different requirements towards full utilisation is that 3DP is inherently parallel (Ruffo and Hague, 2007). Moreover, existing approaches to cost estimation for 3DP have largely ignored hidden or so-called 'ill-structured' costs relating to build failure and ancillary manual processes, such as part finishing and support removal, and, perhaps most importantly, cost relating to or unintended product variation.

This omission in current cost models has come at the expense of industrial applicability, also leading to a lack of realistic decision tools for the support of 3DP technology adoption, which are an essential prerequisite for adoption and successful diffusion. In this project we therefore propose to conduct a series of experiments to establish the empirical parameters needed to develop a realistic and comprehensive costing model of 3DP, which in turn is fundamental to any business case based on 3DP technologies.

Objectives

The proposed feasibility study is built around an array of research hypotheses assessing the economic, managerial and technical feasibility envelope of available 3DP systems, with a bias towards platforms delivering the material properties and reliability required for true manufacturing (as distinct from prototyping etc) applications.

The four main research hypotheses are:

  • there is no clear relationship between the quantity of a product demanded (batch size) and the average unit cost of 3DP;
  • cost related to manual process intervention, unintended product variation and risk of build failure can be modelled as dependent on part features/geometry or on other product characteristics;
  • the network effects found in 3DP outweigh the costs of operating a platform-type re-distributed manufacturing configuration based on 3DP;
  • from the manufacturing cost perspective, 3DP is an enabling technology for re-distributed manufacturing. 

Research outputs

This proposal aims to reconcile two clusters of research questions that are of particular interest for the diffusion of 3DP in redistributed manufacturing settings: Firstly, it is necessary to establish an understanding of the total costs of 3DP as a parallel digitally integrated manufacturing technology. Improved cost models will describe configurations minimising overall monetary cost, energy consumption, and in many cases also waste caused by unintended variation. In particular, this research will empirically quantify the relationship between production quantity and unit cost (ie whether there are economies of scale in 3DP).

Secondly, unlike conventional manufacturing, the parallel and digital design-driven nature of 3DP also gives rise to network effects in 3DP. Network externalities can improve the value, or benefit, of an individual process as the installed base of such platforms increases.

These clusters are vital for understanding both the viability of platform-type 3DP operations as well as redistributed manufacturing settings.

Duration

May 2015 - December 2015