Businesses must ensure effective functioning of their worldwide supply networks, involving decisions to manufacture or outsource, choosing suppliers and monitoring the chain in order to be competitive (Vachon and Klassen, 2006) As such, managers must consider the relationship of those involved in its integration such as internal evaluation, compatible technologies for its operations, cost effectiveness, transaction control and supplier relations (Schiller, 2017). This paper will focus on the compatible technologies for its operations and cost effectiveness in the supply chain because time does not permit to look at all the factors mentioned above in depth. The additive manufacturing production method, spare parts supply chain and previous business cases are reviewed below.
Additive manufacturing
Additive manufacturing is the process of making objects by joining materials from a 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies. In fact, AM technology is used for rapid prototyping, rapid manufacturing and rapid tooling. These terms are confusing with seemingly similar processes (Gardan, 2015). In its early years, AM was mostly applied for the fabrication of conceptual and functional prototypes, also known as Rapid Prototyping. Rapid Tooling (RT) also makes up some of the current AM activity which involves the fabrication of moulds and dies. The concept of Rapid Manufacturing (RM) – the production of end-use parts from additive manufacturing systems is emerging today; though its economic impact remains modest. the initial 3-dimensional data can be generated by computer tomography (CT), magnetic resonance imaging (MRI), or using 3D digitizing systems
Since AM was first commercialized in California, USA in the early 1980’s by MIT, the technology has matured at an exponential rate. Parts manufactured on AM machines were used to validate design data and make prototypes. Now, AM is used to manufacture literally anything from customized jewelry to engines of an aircraft. The recent advances in production materials and faster production techniques of AM has enabled organizations to use AM far more widely than ever before and even to the extent to mass produce up to a certain limit (Mueller, 2012).
The term additive manufacturing is used to represent many different 3D printing technologies which differ according to the energy source (laser, electron etc.). The different kind of 3D printing technologies currently available to manufacturers are stereolithography (SLA), Digital Light Processing (DLP), Fused Deposition Modeling (FDM), Selective Laser Sintering (SLS), Selective Lase Melting (SLM), Electronic Beam Melting (EBM) and Laminated Object Manufacturing (LOM) (Khajavi et al., 2014).
FDM and SLS are the most commonly used technologies (Wohlers, 2013). 3D printing machines that use FDM Technology build objects layer by layer from the very bottom up by heating and extruding thermoplastic filament. This process is similar to stereolithography. Whereas, SLS is a technique that uses laser as power source to form solid 3D objects. The object being printed does not need support structures as the object is constantly surrounded by unsintered powder (Khajavi et al., 2014).
AM technologies have changed the production process and supply chain significantly and the use of AM technology has grown rapidly due to the expiration of the patent of FDM in 2009 and SLS in 2014 (Holmström et al., 2010). Over the past decade, AM equipment has followed the digital technology progression model ("Moore's law"), and for this reason machine prices have dropped which was aided by the expiration of the patents. This has led to an increase in demand for 3D printers.
The characteristics of AM make it a potentially disruptive technology for supply chain management. Holmstrom et al. (2010) highlight the following benefits of AM over traditional manufacturing methods:
• No need for tooling (economies of scale does not exist, which makes customization and design revisions possible).
• Feasibility of producing small production batches economically.
• Possibility of quickly changing design.
• More economical custom product manufacturing (batch of one) plus the capability to produce complex geometries.
• Potential for simpler supply chains with shorter lead times and lower inventories.
These characteristics will allow supply chain managers to manufacture customized goods at various locations and batch sizes which will result in big savings from tooling costs. The development of AM technology will bring opportunities for original equipment manufacturer (OEM) to change their supply chains by introducing distributed production.
Changing the location of production facilities is one of the key decisions that may lead to significant improvements in the spare parts supply chain. The organization could either concentrate production facilities in a centralized location and serve the world market from that location, or another option is to decentralize production in various regional or national locations close to the market (Ari, Markku and Hannu, 2017). Production supply chain configuration may bring benefits such as lead time reduction, which according to Macchion et al (2015) allows improvement in the service level and profitability. Thus, such important decisions have the potential to allow a firm to gain a competitive advantage if implemented correctly. Although, having a distributed production strategy may have disadvantages associated with it such as lack of effective management and poor information flow from distributed production sites. Therefore, there is a necessity to conduct a cost benefit analysis(CBA) before making any decision on the implementation of the distribution strategy as distinct operation settings affect the trade offs.
Distributed production allows production to be located close to demand as it is needed and relocated to other locations as appropriate. It is feasible for the production system to be operational whilst in transit, allowing products to be made whilst the traditional delivery process is taking place. This idea has been converted to a reality by Amazon who have recently filed a patent for a system which performs 3D printing in transit (Amazon, 2015). Transport time that is normally categorized as waste could be made into value adding activity or could be eliminated entirely. However, by doing this, OEM's will lose control of the Intellectual Property(IP) rights as all the different locations will need to have the CAD file to print the part. This may lead to a platform where users could find all the CAD files and hence printing becomes tremendously cheap because they will not have to pay the OEM the fee to use the CAD file. This is what has currently happened in the music industry where songs from all different singers can be found on a music platform such as Spotify, Sound Cloud and hence the singers do not make as much money as they used to from selling the albums via CD's.
Business cases
Additive manufacturing (AM) is becoming more and more interesting for manufacturing firms globally. However, studies on processes, applications and implications of the AM are impending, as such, there are still gaps to be filled in the research field.
Business models and case studies have been developed to show different cost structures and aid managers in the decision-making process on whether to use AM or traditional methods (Hopkinson and Dickens, 2003; Ruffo et al., 2006; Baumers et al.,2012; Rickenbacher et al., 2013). However, none of these models include the full cost incurred i.e. post processing required for complex products to improve surface finish which adds up significantly to the total cost of production. Production costs are the most important aspect to be analyzed in the decision-making process on what technology to use as a production method.
Hopkinson and Dickens (2003) model was amid the first one that considered the analysis of AM costs. The authors predicted the development of the technology to allow manufacturing in large scales. The model showed that it was only beneficial to use AM up to a certain production limit and it would be better to use traditional methods of production over this limit. Subsequently, Ruffo et al.'s (2006) model points out some of the key factors left out from Hopkinson and Dickens model such as power consumption, economies of scale and economies of scope, and tries to resolve these problems in its model. Rufo at al. (2016) attempts to incorporate all the different costs in its model such as the possibility to recycle the powder from laser sintering which makes it a more accurate model than Hopkinson and Dicken's model. However, both these models do not consider the costs of post processing which may be significant in the total cost of production.
Other cost models (Baumers et al., 2012 ; Rickenbacher et al., 2013) were later on developed which were all based on the notion of Hopkinson and Dicken, and Ruffo et al.'s models. The classification of the cost as direct cost or indirect cost was the only thing that changed. Lindemann et al. (2012) are the first researchers who included post processing costs such as quality control, heat treatment, surface treatment and support removal in their costing model. This helps to better understand the costs related to AM technology. Every existing model has advantages and disadvantages, although no single model covers all the criteria. Thus, it is important to build on the strengths of existing models and convert the weaknesses of existing models into strengths. Schroder et al. (2015) used this technique and hence it is one of the models that realistically represent the costs in the comparison of AM to traditional methods of production.
Even if each aspect is crucial in the manufacturing systems, the impact on the costs is the most important aspect that a decision maker has to analyze before choosing a new technology. To understand AM advantages, it is necessary to analyze its impact on production management area. Nowadays, the high costs of the machines and materials make technology more expensive than traditional ones, and its use seems to be good only for a low volume of production (Ruffo et al., 2006). Furthermore, some researchers have based their studies on cost models of additive technology based on different costs structures of AM. All the cost models of AM focus on a specific technology aspect like large scale production, time and energy estimate, relevant activities involved and sensitivity analysis on the parameters of the costing model. None of the models look at the whole picture and all the different possibilities of production.
Spare parts supply chain
Supply Chain Management (SCM) is a concept that gained wide popularity in the 1990 and has been widely researched on by practitioners. However, SCM is not a concept without problems. One of the problems include the lack of a universally accepted definition of SCM (Mentzel et al., 2001). For the purpose of this paper, a supply chain is all the activities involved in sourcing and procurement, conversion, and all logistics management activities. It also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third party service providers and customers. In essence, SCM therefore integrates supply and demand management within and across companies. Spare parts supply chain is the activities that that allow a spare part to be made available to customers in the fastest and quickest way possible, which is a few hours in most cases. Customers would be willing to pay a higher price for the spare part if it is available in a short period of time which would be possible by using AM technologies. This service could be provided by logistic providers or a central base for each region.
The impact of AM on the conventional supply chain can be complex as it reduces the number of stages in the supply chain. Spare parts supply chain attempts to reduce the operating costs as this is the main concern for organizations while keeping customer satisfaction at an acceptable level (Khajavi et al., 2014). To achieve this, suppliers have to overcome unpredictability of demand and make decisions about trade-offs between operating cost, inventory level and delivery time (Huang et al., 2012). Most of the studies and models imply that an AM supply chain would be superior to a conventional one, however, the comparison lacks the research to quantitatively verify the implications of AM on the supply chain.
According to the description provided by the International Institute of Management, the main characteristics of an efficient supply chain are the following:
• Delivering high quality customer responses
• Converting input into outputs efficiently
• Improve asset utilization
Supply chain management attempts to reduce operating costs while keeping customer satisfaction at a satisfactory level. To achieve this, an organization needs to know the demand of the spare parts which is unpredictable, especially for new spare part launches for which data about product failure is unavailable (Simao and Powell, 2009). In uncertain demand situations, delivering customer satisfaction leads to higher inventory levels in more locations. These challenges make it difficult for the supply chain managers to deliver a high level of service with a low cost with regard to spare parts.
For instance, in 2009, the military in the United States spent $194bn on its logistics operations and spare parts supply chain management. At the end of the same year, they held an astounding 4.6 million units of spare parts in inventory costing $94bn (Simao and Powell, 2009). These shows the opportunity for considerable benefits even with very small improvements in the spare parts and logistics operation.
Gap in the literature
Till date, researchers main focus has been on general aspects and technological characteristics of AM. Lifecycle cost, supply chain and sustainability have not been considered as much as general aspects and technological characteristics of AM. (Ari, Markku and Hannu, 2017). The existing scenarios involving factory scale use of AM equipment are focused upon facilities that provide a national level of coverage (Sasson and Johnson, 2016) Therefore, it is important to look at AM in a centralized manufacturing and decentralized manufacturing perspective.