scholarly journals Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kyle C. McDermott ◽  
Ryan D. Winz ◽  
Thom J. Hodgson ◽  
Michael G. Kay ◽  
Russell E. King ◽  
...  

PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

2021 ◽  
Author(s):  
Kyle C McDermott ◽  
Ryan D Winz ◽  
Thom J Hodgson ◽  
Michael G Kay ◽  
Russell E King ◽  
...  

Purpose - Investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/Methodology/Approach - This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings - This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance. Research limitations/implications - This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity, and post-processing requirements. Originality/value - This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.


2019 ◽  
Vol 25 (3) ◽  
pp. 473-487 ◽  
Author(s):  
Yuan Zhang ◽  
Stefan Jedeck ◽  
Li Yang ◽  
Lihui Bai

PurposeDespite the widespread expectation that additive manufacturing (AM) will become a disruptive technology to transform the spare parts supply chain, very limited research has been devoted to the quantitative modeling and analysis on how AM could fulfill the on-demand spare parts supply. On the other hand, the choice of using AM as a spare parts supply strategy over traditional inventory is a rising decision faced by manufacturers and requires quantitative analysis for their AM-or-stock decisions. The purpose of this paper is to develop a quantitative performance model for a generic powder bed fusion AM system in a spare parts supply chain, thus providing insights into this less-explored area in the literature.Design/methodology/approachIn this study, analysis based on a discrete event simulation was carried out for the use of AM in replacement of traditional warehouse inventory for an on-demand spare parts supply system. Generic powder bed fusion AM system was used in the model, and the same modeling approach could be applied to other types of AM processes. Using this model, the impact of both spare parts demand characteristics (e.g. part size attributes, demand rates) and the AM operations characteristics (e.g. machine size and postpone strategy) on the performance of using AM to supply spare parts was studied.FindingsThe simulation results show that in many cases the AM operation is not as cost competitive compared to the traditional warehouse-based spare parts supply operation, and that the spare parts size characteristics could significantly affect the overall performance of the AM operations. For some scenarios of the arrival process of spare parts demand, the use of the batched AM production could potentially result in significant delay in parts delivery, which necessitates further investigations of production optimization strategies.Originality/valueThe findings demonstrate that the proposed simulation tool can not only provide insights on the performance characteristics of using AM in the spare parts supply chain, especially in comparison to the traditional warehousing system, but also can be used toward decision making for both the AM manufacturers and the spare parts service providers.


2018 ◽  
Vol 24 (7) ◽  
pp. 1178-1192 ◽  
Author(s):  
Siavash H. Khajavi ◽  
Jan Holmström ◽  
Jouni Partanen

PurposeInnovative startups have begun a trend using laser sintering (LS) technology patents expiration, namely, by introducing LS additive manufacturing (AM) machines that can overcome utilization barriers, such as the costliness of machines and productivity limitation. The recent rise of this trend has led the authors to investigate this new class of machines in novel settings, including hub configuration. There are various supply chain configurations to supply spare parts in industrial operations. This paper aims to explore the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it.Design/methodology/approachThis study quantitatively examines the feasibility of different AM-enabled spare parts supply chain configurations. Using cost data extracted from a case study, three scenarios per AM machine technology are modeled and compared.FindingsResults suggest that hub production configuration depending on the utilized AM machines can provide economic efficiency and effectiveness to reduce equipment downtime. While previous studies have suggested the need for AM machines with efficiency for single part production for a distributed supply chain, the findings in this research illustrate the positive relationship between multi-part production capability and the feasibility of a hub manufacturing configuration establishment.Originality/valueThis study explores the promise of a production configuration that combines the benefits of centralized production with the flexibility of local manufacturing without the huge costs related to it. Although the existing body of knowledge contains research on production decentralization, research on various levels of decentralization is lacking. Using a real-world case study, this study aims to compare the feasibility of different levels of decentralization for AM-enabled spare parts supply chains.


Author(s):  
Atanu Chaudhuri ◽  
Dennis Massarola

This chapter aims to investigate the potential economic and environmental sustainability outcomes of additive manufacturing (AM) for spare parts logistics. System dynamic simulation was conducted to analyze the sustainability of producing a spare part used in a railways subsystem using a particular additive manufacturing (AM) technology (i.e., selective laser sintering [SLS]) compared to producing it using injection molding. The results of the simulation showed that using SLS for the chosen part is superior to the conventional one in terms of total variable costs as well as for carbon footprint. Compared to the conventional supply chain, for the AM supply chain, the costs of the supplier reduces by 46%, that of the railways company reduces by 71%, while the overall supply chain costs reduce by 61.9%. The carbon emissions in the AM supply chain marginally reduces by 2.89% compared to the conventional supply chain.


2016 ◽  
Vol 27 (7) ◽  
pp. 915-931 ◽  
Author(s):  
Nils Knofius ◽  
Matthieu C. van der Heijden ◽  
W.H.M. Zijm

Purpose For more than ten years, the value of additive manufacturing (AM) for after-sales service logistics has been propagated. Today, however, only few applications are observed in practice. The purpose of this paper is to discuss possible reasons for this discrepancy and to develop a method to simplify the identification of economically valuable and technologically feasible business cases. Design/methodology/approach The approach is based on the analytic hierarchy process and relies on spare part information, that is easily retrievable from the company databases. This has two advantages: first, the approach can be customized toward specific company characteristics, and second, a very large number of spare parts may be assessed simultaneously. A field study is discussed in order to demonstrate and validate the approach in practice. Furthermore, sensitivity analyses are performed to evaluate the robustness of the method. Findings Results provide evidence that the method allows a valid prioritization of a large spare part assortment. Also, sensitivity analyses clarify the robustness of the approach and illustrate the flexibility of applying the method in practice. More than 1,000 positive business cases of AM for after-sales service logistics have been identified based on the method. Originality/value The developed method enables companies to rank spare parts according to their potential value when produced with AM. As a result, companies can evaluate the most promising spare parts first. This increases the effectiveness and efficiency of identifying business cases and thus may support the adoption of AM in after-sales service supply chains.


Author(s):  
Peter Tatham ◽  
Jennifer Loy ◽  
Umberto Peretti

Purpose – 3D printing (3DP), which is technically known as additive manufacturing, is being increasingly used for the development of bespoke products within a broad range of commercial contexts. The purpose of this paper is to investigate the potential for this technology to be used in support of the preparation and response to a natural disaster or complex emergency and as part of developmental activities, and to offer a number of key insights following a pilot trial based in the East African HQ of a major international non-governmental organisation. Design/methodology/approach – Using an illustrative example from the water, sanitation and hygiene (WASH) field this paper demonstrates, from both a theoretical and practical standpoint, how 3DP has the potential to improve the efficiency and effectiveness of humanitarian logistic (HL) operations. Findings – Based on the pilot trial, the paper confirms that the benefits of 3DP in bespoke commercial contexts – including the reduction of supply chain lead times, the use of logistic postponement techniques and the provision of customised solutions to meet unanticipated operational demands – are equally applicable in a humanitarian environment. It also identifies a number of key challenges that will need to be overcome in the operationalisation of 3DP in a development/disaster response context, and proposes a hub-and-spoke model – with the design and testing activities based in the hub supporting field-based production at the spokes – to mitigate these. Research limitations/implications – In addition to an extensive review of both the HL and additive manufacturing literature, the results of the pilot trial of 3DP in support of humanitarian operations, are reported. The paper recommends further detailed analysis of the underpinning cost model together with further field trials of the recommended organisational construct and testing of the most appropriate materials for a given artefact and environment. Practical implications – 3DP has the potential to improve the response to disasters and development operations through the swift production of items of equipment or replacement spare parts. With low capital and running costs, it offers a way of mitigating delays in the supply chain through on site fabrication to meet an identified requirement more swiftly and effectively than via the traditional re-supply route, and it allows for adaptive design practice as multiple iterations of a product are possible in order to optimise the design based on field testing. Social implications – The logistic challenges of responding in a disaster affected or development environment are well documented. Successful embodiment of 3DP as part of the humanitarian logistician’s portfolio of operational techniques has the potential to deliver more efficient and effective outcomes in support of the beneficiaries as well as a sense of empowerment in relation to problem solving. In addition, it has the longer term potential for the creation of a new industry (and, hence, income source) for those living in remote locations. Originality/value – The research demonstrates that, whilst 3DP is increasingly found in a commercial environment, its use has not previously been trialled in a humanitarian context. The research reported in this paper confirms the potential for 3DP to become a game-changer, especially in locations which are logistically difficulty to support.


2018 ◽  
Vol 29 (2) ◽  
pp. 372-397 ◽  
Author(s):  
Melanie Muir ◽  
Abubaker Haddud

Purpose The purpose of this paper is to approximate the impact that additive manufacturing (AM) will have on firm inventory performance (IP) and customer satisfaction (CS) when it is applied within the spare parts (SP) supply chain of manufacturing organisations. This research also explores the influence of customer sensitivity (CSy) to price and delivery lead time and supply risk (SR) within those approximations. Design/methodology/approach An online survey was used to collect the primary data for this research. Data were collected from 69 respondents working for organisations in two industrial segments within the UK manufacturing sector: “Industrial and Commercial Machinery and Computer Equipment” and “Measuring, Analysing and Controlling Instruments, Photographic, Medical and Optical Instruments”. The respondents worked for entities that were categorised in three groups: customers, suppliers, and entities that were both customers and suppliers. The groups that were self-identified as “customers” or “suppliers” answered 20 survey items each and the group that was identified as both “customers” and “suppliers” answered 40 survey items. Findings The results revealed that AM was considered a suitable vehicle for the fulfilment of SP demand. However, AM appeared to make no material difference to CS; the scenario used improved delivery time of SP but increased price. Also, AM was thought to improve IP through less reliance on buffer stock to manage SR and spikes in demand and less carrying of SP at risk of obsolescence. Research limitations/implications The respondents worked for entities within two manufacturing industry segments within the UK and the insights garnered may not be indicative of similar organisations competing in other manufacturing industry segments within the UK or in other countries. In addition, approximately 82 per cent of the surveyed respondents worked for small organisations with fewer than 100 employees and the results may differ for larger organisations. Further limitations were the relatively small sample size and lack of open-ended questions used in the survey. Larger sample size and the usage of open-ended survey questions may lead to more reliable and valuable responses and feedback. Practical implications The findings from this research are considered to be of interest to practitioners contemplating adoption of AM and to developers of AM wishing to increase market share due to the positive reaction of entities within the industrial and commercial machinery and computer equipment, and measuring, analysing and controlling instrumentation industrial segments. This research raises awareness to the possible risks and rewards – from a range of perspectives, of AM to practitioners considering its adoption in the spare parts supply chain (SPSC). Originality/value The paper takes a novel perspective on AM in SPSCs by illuminating the supplier and buyer perspective based on empirical data. This research provides new insights about the appreciation of the use of AM in SPSCs of mostly small sized manufacturing companies located in the UK. This paper also gives new insights about the willingness/conditions of manufacturing companies in the UK to adopt AM for the provision of SP. The originality of this research is twofold: it broached the applicability of AM in the supply chains of the two targeted industrial segments, and as far as the authors are aware, the influence of CSy (e.g. to price or lead time) and SR on SPSC players’ attitude to AM is yet to be considered. Finally, this research adopted a systems theory lens and considered system-wide impact of AM introduction.


2018 ◽  
Vol 29 (5) ◽  
pp. 846-865 ◽  
Author(s):  
Abhijeet Ghadge ◽  
Georgia Karantoni ◽  
Atanu Chaudhuri ◽  
Aravindan Srinivasan

Purpose The purpose of this paper is to assess the impact of additive manufacturing (AM) implementation on aircraft supply chain (SC) networks. Additive and conventional manufacturing spare part inventory control systems are studied and compared, revealing insights into SC performance. Design/methodology/approach A leading global commercial airline’s SC network data are used to model the research problem. A system dynamics simulation approach is followed, drawing out insights for managers. Findings A significant improvement in SC efficiency is observed through the implementation of AM, rendering it a worthwhile investment for global SCs. AM helps to balance inventory levels, and increase responsiveness while decreasing disruptions and carbon emissions in the supply networks. Practical implications The paper offers guidance on the adaption of AM in aircraft SCs and AM’s impact on spare part inventory systems. Originality/value The study provides robust evidence for making critical managerial decisions on SC re-design driven by a new and disruptive technology. Next-generation SC and logistics will replace the current demand for fulfilling material products by AM machines.


2021 ◽  
Vol 14 (2) ◽  
pp. 87
Author(s):  
Rubayet Karim ◽  
Koichi Nakade

Purpose: Managing the inventory of spare parts is very difficult because of the stochastic nature of part’s demand. Also, only controlling the inventory of the spare part is not enough; instead, the supply chain of the spare part needs to be managed efficiently. Moreover, every organization now aims to have a resilient and sustainable supply chain to overcome the risk of facility disruption and to ensure environmental sustainability. This paper thus aims to establish a model of inventory-location relating to the resilient supply chain network of spare parts.Design/methodology/approach: First, applying queuing theory, a location-inventory model for a spare parts supply chain facing a facility disruption risk and has a restriction for CO2 emission, is developed. The model is later formulated as a non-linear mixed-integer programming problem and is solved using MATLAB.Findings: The model gives optimal decisions about the location of the warehouse facility and the policy of inventory management of each location selected. The sensitivity analysis shows that the very low probability of facility disruption does not influence controlling the average emission level. However, the average emission level certainly decreases with the increment of the disruption probability when the facility disruption probability is significant.Practical implications: Using this model, based on the cost and emission parameters and the likelihood of facility disruption, the spare part’s manufacturer can optimize the total average cost of the spare part’s supply chain through making a trade-off between productions, warehouse selection, inventory warehousing and demand allocation.Originality/value: Previous research focuses only on developing a framework for designing an efficient spare parts planning and control system. The inventory-location model for spare parts is not addressed in the sense of risk of facilities disturbance and emission. This research first time jointly considered the probabilistic facility disruption risk and carbon emission for modeling the spare part’s supply chain network.


2021 ◽  
Vol 11 (2) ◽  
pp. 178-193
Author(s):  
Juliana Emidio ◽  
Rafael Lima ◽  
Camila Leal ◽  
Grasiele Madrona

PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.


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