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Assessment the effect of rapid prototyping implementation on supply chain sustainability: a system dynamics approach

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Abstract

The present study aimed to assess the effect of implementing Rapid Prototyping (RP) in the product development phase on the sustainability of a conventional supply chain. The sustainability indicators of RP utilization were identified through a critical literature review and consulting two experienced RP practitioners to determine the key variables regarding the potential impact of RP on the supply chain components, with an emphasis on sustainability pillars. A generic system dynamics modeling was provided to simulate the RP-adapted supply chain and measure its sustainability performance. The simulation results indicated that RP utilization in the design phase could decrease the number of the assembly parts and material consumption in conventional manufacturing, while indirectly affecting the reduction of waste generation, logistics, CO2 emissions, processes, and the total costs which are related to environmental and economic aspects of the sustainable supply chain. Findings indicated that significant increase in operational skills and knowledge as the main indicators of the social dimension could remarkably reduce the failure rates and increase the quality of the products. This indicator plays a pivotal role in operational success and could be enhanced through training programs. Social sustainability indirectly affects environmental and economic sustainability. This was the first model-based research to examine the potential effects of RP on the sustainability of a conventional manufacturing. The proposed generic model encompassed the variables that could be applicable in every scenario to help decision-makers change values or add more variables within specific industry settings and choose the applicable ones, which in turn, accelerating the RP adoption in supply chains and providing insights for operational decisions regarding product design stage.

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  1. Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Islamic Republic of Iran

    Nazanin Hosseini Arian, Alireza Pooya & Fariborz Rahimnia

  2. Department of Management, Faculty of Economics and Management, Tarbiat Modares University, Tehran, Islamic Republic of Iran

    Ali Sibevei

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  1. Nazanin Hosseini Arian
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Appendices

Appendix 1 Integrated causal loop diagram

Fig. 22 Integrated causal loop diagram.

Appendix 2 Equations

Acceptable skill level = 100%

Average tooling cost = complexity* Price increase per complexity

(Based on the model purpose, it is assumed that increase in geometric complexity will increase tooling costs)

Average order lead time = 1 week

(It is assumed that average order supplier lead time is one week)

Total fuel consumption = (fuel consumption per km for material supplying*total km for material purchasing transportation) + (total km for waste disposal*fuel consumption per km for waste disposal)

Quality = INTEG (increase,0)

Increase through iterations = time to increase look up (RP prototypes)

time to increase look up([(0,0)-(120,10)],(0,0.1),(5,0.3),(8.80734,0.701755),(13.7615,1.35965),(19.3761,1.57895),(29,1.66667),(30,1.5),(35,1.4),(40,0.9),(45.107,0.745614),(48,0.5),(50,0.2),(120,0))

(Increase in skill of operators through iterations is based on the time and number of prototypes they make)

Discover = RP failed prototypes /time to detect

Increase through training = (training requirement*c)/personnel*duration

RP Prototypes = INTEG (iteration rate + second phase-rejection rate)

(This is the sock variable which is sum of the input rates and output rates)

Transportation cost = (total km for waste disposal + total km for material purchasing transportation)*average extra cost for transportation per Km + total fuel cost

Frequency of transportation for material purchasing = number of material supplier*(total material consumption in production/truck capacity)

Fuel consumption per km for material supplying = 48L/100KM

Fuel consumption per km for waste disposal = 32L/100kM

Iteration rate = iteration for first project + iteration for second project + iteration for third project.

Skill gap = acceptable skill level-operator skill

Average material consumption per assemble part = 0.3 kg

Number of material supplier = DELAY1 (1/5*total of assemble parts, 52)

Supplier lead time = average order lead time*number of material supplier

Design = IF THEN ELSE (New designs released > 0, discover-iteration rate, 0)

Frequency of transportation for waste disposal = waste generation in material processing during production/truck capacity 2

Total distance (km) for waste disposal = frequency of transportation for waste disposal*distance to the disposal site

Total material consumption in production = DELAY1 (total of assemble parts*production number*average material consumption per assemble part, 53)

Decrease rate = reduction per project in first year + DELAY1 (reduction per other project, 53)

Total repair cost = total of assemble parts*production number*average repair cost per unit

Total tool cost = total of assemble parts*average tooling cost per unit

Total waste in material processing during production = total material consumption in production*waste

Training cost = training requirement*cost per training duration*personnel

Training requirement = skill gap/adj time

Material purchasing cost = average cost per one kg material*total material consumption in production

Operator skill = INTEG (increase through iterations + increase through training,

Total cost = redesign cost + carbon penalty cost + material purchasing cost + total tool cost + training cost + transportation cost + inventory cost

Total fuel cost = total fuel consumption*fuel cost per liter

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Arian, N.H., Pooya, A., Rahimnia, F. et al. Assessment the effect of rapid prototyping implementation on supply chain sustainability: a system dynamics approach. Oper Manag Res 14, 467–493 (2021). https://doi.org/10.1007/s12063-021-00228-6

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