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Extended Summary

Chapter 6. Printing Homes

Unit Cost Estimation for Additive Manufacturing in Construction

Authors: Alexander N. Walzer, Mariia Kozlova, and Julian Scott Yeomans
Source: Kozlova, M., & Yeomans, J. S. (Eds.). (2024). Sensitivity Analysis for Business, Technology, and Policymaking: Made Easy with Simulation Decomposition (SimDec). Taylor & Francis. https://doi.org/10.4324/9781003453789
License: CC BY-NC-ND 4.0

📖 Read full Chapter 6: Ch6.pdf

Can 3D printing reduce housing costs?

This chapter explores whether additive manufacturing (AM) — specifically 3D printing with cement — can bring cost efficiency to construction.
The focus is on estimating unit costs for concrete printing and investigating how batch size and technological uncertainty influence those costs.

Rather than relying on fixed-point assumptions, the authors develop and analyze a probabilistic cost model using Monte Carlo simulation and Simulation Decomposition (SimDec).

A data-driven model for estimating AM unit cost

The chapter presents a realistic cost model for 3D-printed construction, dividing costs into:

  • Direct costs (e.g. pre-production, printing, post-processing)
  • Indirect costs (e.g. equipment, labor, overhead)

The model calculates unit cost (UC) as:

$UC = Direct Cost per Unit + (Indirect Cost per Hour × Production Time) / Batch Size$

In the base case:

  • A single printed unit costs approx. €4,105
  • Producing 1,000 units reduces it to approx. €2,802

That’s a ~32% cost reduction purely from scaling production.

Going beyond averages with SimDec

To move beyond static assumptions, the authors ran a Monte Carlo simulation by varying:

  • Material and labor costs
  • Deposition and printing speeds
  • Time and setup delays
  • Batch size: 1 vs. 1,000 units

They then used SimDec to break down the simulated unit cost outcomes and reveal:

  • Which factors drive most of the cost variability
  • How combinations of inputs produce different cost scenarios
  • Where future technology improvements would have the greatest effect

What SimDec revealed

  • Indirect and direct costs were the biggest drivers of unit cost.
  • Deposition rate and operation labor were the next most influential.
  • Larger batch sizes lowered both cost and uncertainty.
  • Some input combinations were rare or impossible, due to real-world correlations (e.g. fast print speed + high labor cost).

One scenario assumed future tech improvements:

  • Lower material cost
  • Faster deposition
  • Less manual intervention
    → Result: unit cost dropped to ~€1,500, making printed homes significantly more affordable.

Why it matters

This analysis shows how sensitivity decomposition helps:

  • Understand cost structure in early-stage construction technologies
  • Prioritize R&D areas (e.g. deposition speed vs. labor input)
  • Visualize how batch size affects economies of scale
  • Move from cost estimates to cost-informed decision-making

Try it yourself

Attribution

Based on Chapter 6 of Sensitivity Analysis for Business, Technology, and Policymaking
© Alexander N. Walzer, Mariia Kozlova, and Julian Scott Yeomans, 2024 — CC BY-NC-ND 4.0.
This summary is an independent derivative work created for educational and indexing purposes, not affiliated with the original publisher.