Production as a service: A centralized framework for small batch manufacturing

Distributed Use Models to Expand Small Batch Manufacturing Flexibility

“…where B C is a regression coefficient of C's percentage change in response to 1% change in revenue; for α = 0, we expect an additional cost increase of 10-50% due to greater transaction costs attributable to sharing (direct costs, except labor, will be higher);  for α = 1 (inhouse production) fixed assets investments increase proportionally to revenue growth with a leverage of 101-110%. Greater investment is assumed to be planned since the production capacity is depleted (see the inputs);  investment in working capital is growing in proportion to revenue growth;  for α = 1 depreciation will be expressed as A = B A R, (6) where B А is a regression coefficient of A's percentage change in response to 1% change in revenue;  for α = 0 depreciation growth rate is 0 (when sharing, investments in fixed assets are not expected);  investments in fixed assets are financed by the growth of debt or equity in proportion to the regression coefficient; for α = 1 a pair of dummies is employed: expansion of debt (β = 1, otherwise 0), expansion of equity (γ = 1, otherwise 0);  the cost of capital is assumed unchanged;  demand impulse (as revenue growth) is planned within 100.01% -111% interval (expert estimate);  α, β and γ are normally distributed random values, β and γ are in counterphase to each other; As a result, we obtain three possible combinations of dummies {α, β, γ} employed in our simulation model:  {1,0,1}inhouse capacity expansion through debt increase;  {1,1,0}inhouse capacity expansion through equity increase;  {0,0,0}use of sharing. Since this simulation model was built to test a theoretical hypothesis, and not a practical result, the validity was controlled by scaling the number of iterations: 2000, 5000, 15000, 100000 iterations, as well as by reproducing the model on other companies' inputs.…”

“…A special case of subcontracting is a "professional" outsourcer company whose core competency is to be well-prepared to variability of output, volatility of batches, complicatedness of logistics or to excessive customization of products. This concept fits into the Production as a Service (PaaS) framework that connects product developers who have small batch production needs, with existing manufacturing facilities that have underutilized resources [6]. According to Balta et al (2018), PaaS is a framework based on the service-oriented architecture design that breaks down the manufacturing of a product into several steps, or services, to incorporate various manufacturers with available capability in fulfilling the production request subject to economic effect maximization.…”

“…An important issue of technology access and preservation is also emphasized in the literature. On the one hand, small batch manufacturers that use PaaS face risks of intellectual property rights violation (e.g., see Balta et al [6]), especially if the outsourcer is in a foreign jurisdiction. On the other hand, Pardo and Rama argue that "subcontracting of production do not lower necessarily the technology needs.…”

Towards digital automation flexibility in large-scale timber construction: integrative robotic prefabrication and co-design of the BUGA Wood Pavilion

“…When researchers in the field of architectural technology describe its 'innovation resistance' and low levels of productivity gains, they tend to disregard the unique modalities of construction (Ishak and Newton 2016). It is evident that manufacturing industries have largely advanced to interconnected and sensor-armed (Monostori 2014), selfconfiguring (Friedrich et al 2015) and self-organizing (Balta et al 2017) production systems, while construction companies have made little fundamental advancements. But while manufacturing might be a reliable source of inspiration, it is probably unproductive to take it as a blueprint for construction efforts, as not only the two industries' fundamental economic principles but also cultural, social and political factors are of an entirely different nature.…”

Execution Plan Control in Dynamic Coalition of Robots with Smart Contracts and Blockchain

“…Manufacturing on demand, precision agriculture, and remote exploring (including aerospace scan of planets) can be considered as examples of the areas of possible use of robot coalitions that require the coordinated action of robots with different functionalities [2][3][4]. For example, in precision farming, there are robots capable of sowing, watering, fertilizing, exploring a territory, analyzing the composition of the soil that should work together to get maximal benefit.…”