# Impacts of Additive Manufacturing on Supply Chain Flow: A Simulation Approach in Healthcare Industry

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## Abstract

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## 1. Introduction

- No need for tooling
- Feasibility of producing small production batches economically
- Possibility for quickly change design
- Product optimization for function
- More economical custom product manufacturing plus the capability to produce complex geometries
- Potential for simpler supply chains with shorter lead times and lower inventories

## 2. Literature Review

#### 2.1. Review on Supply Chain Studies with Additive Manufacturing

#### 2.2. Review on Supply Chain Studies with Simulation Approach

#### 2.3. Contributions of the Study

## 3. Supply Chain Networks of Orthopedic Insoles

- Due to higher ‘first time right’ production with 3D printing, total lead-time decreases.
- There is less room for human induced error in the supply chain with 3D printing.
- Supply chain with 3D printing ensures that a single moment of contact between customer and orthopedist should be enough for a correct fitting.
- Lower skilled employees can operate 3D printers rather than traditional machines.
- If orthopedists have a standard 3D scan of the client’s foot, they can outsource the production anywhere they want.
- Direct shipping becomes an option in the supply chain with 3D printing.
- With 3D printing, manufacturers can reduce their dependency on different suppliers.
- Due to producing a unibody insole with 3D printing, they can become thinner but stronger.
- Customization (e.g., color, carved) can move beyond the level of customization offered by traditional methods.

## 4. Simulation Models for the Case Study

- The yield rate is different for the CNC machine and the 3D printer.
- The quality of finished parts from the CNC machine and the 3D printer are the same.
- The time consumption of a 3D printer made product is determined by the volume of the product.
- Raw materials are infinite.

#### 4.1. Simulation Model of TSCN

#### 4.2. Simulation Model of 3DPSCN

## 5. Comparative Results

#### 5.1. Scenario Analyses

#### 5.1.1. Sensitivity to Changes in the Number of Machines (Scenario A)

#### 5.1.2. Sensitivity to Changes in the Acceptance Rate of First Testing (Scenario B)

#### 5.1.3. Sensitivity to Changes in the Number of Doctors (Scenario C)

#### 5.1.4. Sensitivity to Changes in the Distribution of Customer Arrivals (Scenario D)

#### 5.1.5. Sensitivity to Changes in the Breakdown Situations (Scenario E)

## 6. Conclusions

- To avoid carrying out further simulation experiments, simulation optimization techniques such as meta-models or variance analysis techniques can be applied.
- To reflect larger networks in real life, the number of facilities can be increased horizontally and vertically.
- To see the environmental effects, more performance indicators which represent assessment values concerning polluting factors, water or electricity uses related with production and transportation can be added.
- To see the economic effects, cost information of shipping, manufacturing and other activities can be added.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Traditional versus 3D printing supply chain [2].

Definition | Parameter Value |
---|---|

Arrivals of customers to the orthopedist to give an order and also receive the insole | TRIA (50, 60, 70) min |

Taking a plaster cast from the customer’s foot (per foot) | TRIA (5, 6, 7) min |

Transportation time from orthopedist to manufacturer#1 (vice versa) | UNIF (7.5, 8.5) h |

Transportation time from orthopedist to manufacturer#2 (vice versa) | UNIF (5.5, 6.5) h |

Transportation time from cork supplier#1 to manufacturer#1 | UNIF (1.5, 2.0) h |

Transportation time from cork supplier#2 to manufacturer#1 | UNIF (3.5, 3.9) h |

Transportation time from cork supplier#1 to manufacturer#2 | UNIF (3.2, 3.8) h |

Transportation time from cork supplier#2 to manufacturer#2 | UNIF (4.5, 4.9) h |

Pre-processing operation time of cork | TRIA (45, 50, 55) min |

Rate of waste in pre-processing of cork | UNIF (0.025, 0.030) |

Waiting time of cork, plastic and leather for assembly operation | UNIF (10, 15) min |

Transportation time from plastic supplier to manufacturer#1 | UNIF (3.9, 4.1) h |

Transportation time from plastic supplier to manufacturer#2 | UNIF (4.6, 5.0) h |

Pre-processing operation time of plastic | TRIA (25, 30, 35) min |

Rate of waste in pre-processing of plastic | UNIF (0.025, 0.030) |

Transportation time from leather supplier#1 to manufacturer#1 | UNIF (1.1, 1.5) h |

Transportation time from leather supplier#2 to manufacturer#1 | UNIF (0.35, 0.55) h |

Transportation time from leather supplier#3 to manufacturer#1 | UNIF (7.0, 7.8) h |

Transportation time from leather supplier#1 to manufacturer#2 | UNIF (2.8, 3.2) h |

Transportation time from leather supplier#2 to manufacturer#2 | UNIF (2.1, 2.5) h |

Transportation time from leather supplier#3 to manufacturer#2 | UNIF (5.4, 5.8) h |

Required time to assemble of plastic, cork and leather (per foot) | TRIA (20, 30, 40) min |

Rate of waste in assembling of plastic, cork and leather | UNIF (0.03, 0.04) |

Required time to test the insole (for a pair) | TRIA (7, 8, 9) min |

Required time to adjust the tested insole (per foot) | TRIA (20, 30, 40) min |

Definition | Parameter Value |
---|---|

Arrivals of customers to the orthopedist to give an order and also receive the insole | TRIA (50, 60, 70) min |

Scanning of customer’s foot in orthopedist (per foot) | UNIF (3, 4) min |

Required time to send the scanning files from orthopedist to manufacturer | UNIF (1, 2) min |

Transportation time from filament supplier#1 to manufacturer | UNIF (8.5, 8.9) h |

Transportation time from filament supplier#2 to manufacturer | UNIF (8.3, 8.7) h |

Printing the insole in 3D printer (per foot) | TRIA (55, 60, 65) min |

Transportation time from manufacturer to orthopedist | UNIF (10, 12) min |

Required time to test the insole (for a pair) | TRIA (7, 8, 9) min |

Performance Indicator (PI) | Entity | Explanation |
---|---|---|

PI.1 | Day | Lead time of customers who get insole on the first try in TSCN |

PI.2 | Day | Lead time of customers who get insole on the second try in TSCN |

PI.3 | People | Number of customers who get insole on the first try in TSCN |

PI.4 | People | Number of customers who get insole on the second try in TSCN |

PI.5 | Unit | Amount of cork waste in TSCN |

PI.6 | Unit | Amount of plastic waste in TSCN |

PI.7 | Unit | Amount of waste occurred in manufacturing in TSCN |

PI.8 | Day | Total manufacturing and pre-processing time in TSCN |

PI.9 | Day | Lead time of customers who get insole in 3DPSCN |

PI.10 | People | Number of customers who get insole in 3DPSCN |

PI.11 | Day | Total manufacturing time in 3DPSCN |

PI | Average | Standard Deviation | Half-Width | Minimum | Maximum |
---|---|---|---|---|---|

PI.1 | 20.32 | 1.56 | 1.17 | 17.63 | 22.81 |

PI.2 | 27.03 | 1.88 | 1.12 | 23.18 | 30.14 |

PI.3 | 560 | 17.18 | 12.29 | 533 | 596 |

PI.4 | 1164 | 28.02 | 20.04 | 1128 | 1219 |

PI.5 | 57.00 | 3.12 | 2.24 | 51.61 | 61.08 |

PI.6 | 56.00 | 3.10 | 2.23 | 51.97 | 60.61 |

PI.7 | 65.00 | 5.59 | 4.22 | 55.53 | 75.56 |

PI.8 | 20.72 | 1.59 | 1.14 | 17.10 | 22.77 |

PI.9 | 2.55 | 0.14 | 0.10 | 2.39 | 2.76 |

PI.10 | 2308 | 1.68 | 1.21 | 2306 | 2311 |

PI.11 | 2.36 | 0.14 | 0.10 | 2.19 | 2.56 |

PI | Average | Minimum | Maximum |
---|---|---|---|

PI.1 | 2.44 | 2.02 | 5.34 |

PI.2 | 5.32 | 4.01 | 9.26 |

PI.3 | 692 | 675 | 719 |

PI.4 | 1595 | 1566 | 1620 |

PI.5 | 62.00 | 59.00 | 67.00 |

PI.6 | 64.00 | 58.00 | 70.00 |

PI.7 | 81.00 | 72.00 | 90.00 |

PI.8 | 2.01 | 1.89 | 3.10 |

PI.9 | 2.19 | 2.12 | 2.29 |

PI.10 | 2315 | 2310 | 2322 |

PI.11 | 2.00 | 1.99 | 2.01 |

PI | Average | Standard Deviation | Half-Width | Minimum | Maximum |
---|---|---|---|---|---|

PI.1 | 11.66 | 0.87 | 0.91 | 1.11 | 59.57 |

PI.2 | 14.53 | 1.05 | 0.58 | 2.19 | 80.56 |

PI.3 | 565 | 17.23 | 18 | 540 | 611 |

PI.4 | 1186 | 28.09 | 19 | 1157 | 1205 |

PI.5 | 57.40 | 3.15 | 2.17 | 52.38 | 61.35 |

PI.6 | 56.71 | 3.08 | 2.19 | 52.75 | 61.41 |

PI.7 | 66.00 | 5.65 | 4.29 | 59.28 | 74.97 |

PI.8 | 11.25 | 0.68 | 0.58 | 1.04 | 59.93 |

PI.9 | 1.49 | 0.08 | 0.09 | 1.15 | 2.31 |

PI.10 | 2307 | 1.64 | 2 | 2303 | 2311 |

PI.11 | 1.39 | 0.06 | 0.09 | 1.08 | 2.16 |

PI | Average | Standard Deviation | Half-Width | Minimum | Maximum |
---|---|---|---|---|---|

PI.1 | 12.19 | 0.95 | 0.55 | 1.11 | 61.66 |

PI.2 | 15.10 | 1.12 | 0.46 | 2.18 | 82.72 |

PI.3 | 550 | 16.21 | 15.72 | 523 | 588 |

PI.4 | 1153 | 27.16 | 15 | 1125 | 1187 |

PI.5 | 57.10 | 3.11 | 2.62 | 51.03 | 62.46 |

PI.6 | 57.25 | 3.44 | 1.69 | 53.57 | 60.46 |

PI.7 | 63.42 | 4.95 | 4.15 | 56.05 | 71.35 |

PI.8 | 11.94 | 0.83 | 0.42 | 1.04 | 61.58 |

PI.9 | 1.44 | 0.07 | 0.11 | 1.15 | 2.85 |

PI.10 | 2300 | 1.63 | 2.43 | 2295 | 2306 |

PI.11 | 1.34 | 0.06 | 0.11 | 1.08 | 2.70 |

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## Share and Cite

**MDPI and ACS Style**

Özceylan, E.; Çetinkaya, C.; Demirel, N.; Sabırlıoğlu, O.
Impacts of Additive Manufacturing on Supply Chain Flow: A Simulation Approach in Healthcare Industry. *Logistics* **2018**, *2*, 1.
https://doi.org/10.3390/logistics2010001

**AMA Style**

Özceylan E, Çetinkaya C, Demirel N, Sabırlıoğlu O.
Impacts of Additive Manufacturing on Supply Chain Flow: A Simulation Approach in Healthcare Industry. *Logistics*. 2018; 2(1):1.
https://doi.org/10.3390/logistics2010001

**Chicago/Turabian Style**

Özceylan, Eren, Cihan Çetinkaya, Neslihan Demirel, and Ozan Sabırlıoğlu.
2018. "Impacts of Additive Manufacturing on Supply Chain Flow: A Simulation Approach in Healthcare Industry" *Logistics* 2, no. 1: 1.
https://doi.org/10.3390/logistics2010001