Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1237055

Manufacturing Cost Levelization Model – A User’s Guide

Morrow, William R.·Shehabi, Arman·Smith, Sarah Josephine
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Manufacturing Cost Levelization Model is a cost-performance techno-economic model that estimates total large-scale manufacturing costs for necessary to produce a given product. It is designed to provide production cost estimates for technology researchers to help guide technology research and development towards an eventual cost-effective product. The model presented in this user’s guide is generic and can be tailored to the manufacturing of any product, including the generation of electricity (as a product). This flexibility, however, requires the user to develop the processes and process efficiencies that represents a full-scale manufacturing facility. The generic model is comprised of several modules that estimate variable costs (material, labor, and operating), fixed costs (capital & maintenance), financing structures (debt and equity financing), and tax implications (taxable income after equipment and building depreciation, debt interest payments, and expenses) of a notional manufacturing plant. A cash-flow method is used to estimate a selling price necessary for the manufacturing plant to recover its total cost of production. A levelized unit sales price ($ per unit of product) is determined by dividing the net-present value of the manufacturing plant’s expenses ($) by the net present value of its product output. A user defined production schedule drives the cash-flow method that determines the levelized unit price. In addition, an analyst can increase the levelized unit price to include a gross profit margin to estimate a product sales price. This model allows an analyst to understand the effect that any input variables could have on the cost of manufacturing a product. In addition, the tool is able to perform sensitivity analysis, which can be used to identify the key variables and assumptions that have the greatest influence on the levelized costs. This component is intended to help technology researchers focus their research attention on tasks that offer the greatest opportunities for cost reduction early in the research and development stages of technology invention.

Explore related subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Morrow, William R., Shehabi, Arman, Smith, Sarah Josephine. 2015-08-01. Manufacturing Cost Levelization Model – A User’s Guide. https://doi.org/10.2172/1237055

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Local Air Quality Impacts of a Peak-Shaving Diesel Generator Unit in Waynesville, North Carolina

The Waynesville Electric Department in Waynesville, North Carolina, operates a 2,000-kW diesel generator with a 4% capacity factor, emitting less than 1 ton annually of PM2.5, VOCs, carbon monoxide (CO), and sulfur dioxide (SO2), and approximately 1.8 tons of NO. The National Laboratory of the Rockies (NLR) conducted high-resolution, near-source air-quality modeling to evaluate the generator's impacts. Results indicate minimal effects on ambient pollutant concentrations: annual PM2.5 increases are below 0.0004 microgrm/m3 for local census tracts, while CO and SO2 levels increase by less than 0.00004 ppm and 0.0002 ppb, respectively. Estimated annual premature mortality attributable to PM2.5 exposure is less than 0.001 cases across Haywood County, with an associated economic impact of approximately $12,000. This report complements a separate technoeconomic analysis to inform Waynesville's investment strategies for demand reduction.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Energy Technology Innovation Partnership Project (Bilingual)

The U.S. Department of Energy's (DOE) Energy Technology Innovation Partnership Project (ETIPP) helps coastal, remote, and island communities increase the affordability, reliability, and security of their energy systems through energy planning and deep-dive technical assistance. This is a English and Spanish translation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗