{"id":12531,"date":"2025-01-22T14:57:13","date_gmt":"2025-01-22T14:57:13","guid":{"rendered":"https:\/\/www.insisoc.uva.es\/?page_id=12531"},"modified":"2025-01-28T12:55:22","modified_gmt":"2025-01-28T12:55:22","slug":"ins-abamca-about","status":"publish","type":"page","link":"https:\/\/www.insisoc.uva.es\/index.php\/ins-abamca-about\/","title":{"rendered":"Ins-Abamca-About"},"content":{"rendered":"

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[\/et_pb_fullwidth_menu][\/et_pb_section][et_pb_section fb_built=\u00bb1″ custom_padding_last_edited=\u00bbon|desktop\u00bb disabled_on=\u00bboff|off|off\u00bb _builder_version=\u00bb4.16″ custom_padding=\u00bb0px|0px|0px|0px\u00bb custom_padding_tablet=\u00bb50px|0|50px|0″ custom_padding_phone=\u00bb\u00bb transparent_background=\u00bboff\u00bb padding_mobile=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_row disabled_on=\u00bbon|on|\u00bb _builder_version=\u00bb4.16″ background_size=\u00bbinitial\u00bb background_position=\u00bbtop_left\u00bb background_repeat=\u00bbrepeat\u00bb z_index_tablet=\u00bb500″ box_shadow_horizontal_tablet=\u00bb0px\u00bb box_shadow_vertical_tablet=\u00bb0px\u00bb box_shadow_blur_tablet=\u00bb40px\u00bb box_shadow_spread_tablet=\u00bb0px\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb4_4″ _builder_version=\u00bb4.16″ custom_padding=\u00bb|||\u00bb global_colors_info=\u00bb{}\u00bb custom_padding__hover=\u00bb|||\u00bb][et_pb_divider show_divider=\u00bboff\u00bb disabled_on=\u00bbon|on|off\u00bb admin_label=\u00bbSeparador SOLO ORDENADOR\u00bb _builder_version=\u00bb4.16″ z_index_tablet=\u00bb500″ box_shadow_horizontal_tablet=\u00bb0px\u00bb box_shadow_vertical_tablet=\u00bb0px\u00bb box_shadow_blur_tablet=\u00bb40px\u00bb box_shadow_spread_tablet=\u00bb0px\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\u00bb1_3,2_3″ disabled_on=\u00bbon|on|\u00bb _builder_version=\u00bb4.16″ background_size=\u00bbinitial\u00bb background_position=\u00bbtop_left\u00bb background_repeat=\u00bbrepeat\u00bb z_index_tablet=\u00bb500″ box_shadow_horizontal_tablet=\u00bb0px\u00bb box_shadow_vertical_tablet=\u00bb0px\u00bb box_shadow_blur_tablet=\u00bb40px\u00bb box_shadow_spread_tablet=\u00bb0px\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb1_3″ _builder_version=\u00bb4.16″ custom_padding=\u00bb|||\u00bb global_colors_info=\u00bb{}\u00bb custom_padding__hover=\u00bb|||\u00bb][et_pb_image src=\u00bbhttps:\/\/www.insisoc.uva.es\/wp-content\/uploads\/2017\/06\/INFORMS.jpg\u00bb alt=\u00bbDavid Poza ABAMCA Director\u00bb title_text=\u00bbDavid Poza\u00bb url=\u00bbhttps:\/\/www.insisoc.uva.es\/index.php\/javier-pajares-gutierrez\/\u00bb url_new_window=\u00bbon\u00bb align=\u00bbcenter\u00bb src_tablet=\u00bbhttps:\/\/www.insisoc.uva.es\/wp-content\/uploads\/2017\/06\/INFORMS.jpg\u00bb src_phone=\u00bbhttps:\/\/www.insisoc.uva.es\/wp-content\/uploads\/2017\/06\/INFORMS.jpg\u00bb src_last_edited=\u00bbon|phone\u00bb align_tablet=\u00bbcenter\u00bb align_phone=\u00bb\u00bb align_last_edited=\u00bbon|desktop\u00bb disabled_on=\u00bbon|on|\u00bb admin_label=\u00bbFoto Javier Pajares ORDENADOR\u00bb _builder_version=\u00bb4.27.4″ custom_margin=\u00bb||9px|||\u00bb custom_padding=\u00bb0px|||||\u00bb z_index_tablet=\u00bb500″ box_shadow_style=\u00bbpreset1″ 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David Poza<\/strong><\/a><\/span><\/h4>\n

ABAMCA Director<\/em><\/p>\n

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Agent-Based Additive-Manufacturing markets to improve business competitiveness: exploiting the potential of Combinatorial Auctions (ABAMCA)<\/strong><\/h2>\n

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Mercados de fabricaci\u00f3n aditiva basados en agentes para mejorar la competitividad empresarial: explotando el potencial de las subastas combinatorias (ABAMCA)<\/strong><\/h4>\n

En el contexto de la fabricaci\u00f3n aditiva, el objetivo del proyecto ABAMCA es investigar y desarrollar nuevas metodolog\u00edas para la asignaci\u00f3n de clientes industriales (quienes subcontratan la producci\u00f3n de subcomponentes) a diferentes proveedores (fabricantes que est\u00e1n cualificados para la producci\u00f3n de esos subcomponentes empleando impresi\u00f3n 3D). En concreto, el proyecto aplicar\u00e1 subastas combinatorias y metodolog\u00edas de modelado basado en agentes para reducir el actual desequilibrio entre la oferta y la demanda en el mercado de fabricaci\u00f3n aditiva para la fabricaci\u00f3n de subcomponentes.<\/p>\n

El resultado final de este proyecto ser\u00e1 un modelo piloto de plataforma marketplace en la que las empresas cliente podr\u00e1n solicitar la producci\u00f3n de los subcomponentes requeridos, y las empresas fabricantes podr\u00e1n pujar por las combinaciones de pedidos que les interesa producir en tanto que optimizan la capacidad productiva de sus m\u00e1quinas de fabricaci\u00f3n aditiva. La plataforma ser\u00e1 validada mediante su aplicaci\u00f3n a diferentes casos de estudio en empresas espa\u00f1olas de fabricaci\u00f3n aditiva.<\/p>\n

KEYWORDS:<\/strong><\/h5>\n

Additive manufacturing, agent-based modeling, combinatorial auctions, market design<\/p>\n

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Background and justification<\/strong><\/h3>\n

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Additive manufacturing (AM) is a technology with transformative and disruptive potential in almost every industry (Glas et al. 2021; Byskov and Vedel-Smith 2023). However, due to the high cost of high-performance AM machines – especially metal AM machines -, it might be more interesting for most industrial companies to outsource the production of some of the subcomponents to specialized AM manufacturers (Li et al. 2019). These consequent changes in the supply chain are leading to a redesign of production networks in which new markets are emerging (Meyer et al. 2021). These markets involve a set of subcomponent-demanding companies (i.e., customer companies) and a set of companies that use additive manufacturing to provide the demanded subcomponents (i.e., supplying companies).<\/span><\/p>\n

Currently, customer-supplier allocation is mainly done in a distributed manner in these markets (Stein et al. 2019; Graesch et al. 2021). This implies that when a customer company decides to outsource the manufacturing of a subcomponent, it needs to explore the 3D printing market to find a supplying company that fits its needs. This distributed allocation process presents certain inefficiencies, both from the customer (demand) and manufacturer (supply) sides:<\/span>
\u2022 Customer companies need to invest resources to contact different manufacturers to determine whether they have sufficient capacity\/availability to manufacture the requested parts and if so, negotiate a contract with the selected manufacturer.<\/span>
\u2022 Additive-manufacturing suppliers have limited capacity. Depending on the volume of production demanded by a customer, a manufacturer may be forced to use its resources inefficiently. For example, if it needs to produce a small number of parts for a single customer, the manufacturer may need to use a machine far below its capacity.<\/span><\/p>\n

Besides, since AM is not yet a mature technology, AM manufacturers may differ in their capabilities and thus it may be difficult for demanders to find an appropriate supplier (Rayna et al. 2015). On the opposite side, AM suppliers need to evaluate their capability for manufacturing the specific goods demanded to them and analyze whether it is profitable to produce the orders. This imbalance between supply and demand and the high transaction costs incurred by demanders and suppliers of AM services are two inefficiencies that have been identified by researchers in recent years (Cui et al. 2022; Wu et al. 2022)<\/span><\/p>\n

In additive manufacturing (AM), scheduling parts with similar characteristics in the same builds helps to obtain an efficient production plan (Oh et al. 2020; Zhang et al. 2020). For example, a 3D printing tray can be shared to generate, simultaneously, orders from different customers (Hedenstierna et al. 2019). Our initial hypothesis is that if manufacturers had access to information about which subcomponents are in demand in the market (i.e., the parts demanded not only by their own customers but by all customers in the market), they could offer to different potential customers the production of some of the required parts, using the same machine to produce a combination of parts for different customers. We consider that this market configuration will lead to a more efficient and sustainable use of the productive capacity of the sector, as it would result not only in a higher welfare of the supplier AM companies (i.e., a more efficient use of their resources) but also in a higher welfare of the client companies (i.e., access a wide range of producers and more competitive prices).<\/span><\/p>\n

In this context, our proposal consists of creating a centralized platform that allows for an efficient allocation of customers and suppliers in the emerging additive manufacturing market for subcomponents. For this purpose, we will use as a methodology the theory of combinatorial auctions combined with Agent-Based Modeling (see Section 3.2).<\/span><\/p>\n

The figure below shows a schematic of how the business transactions will be coordinated in the platform. Customer companies (represented in columns) request subcomponents to be manufactured. Additive-manufacturing suppliers (represented in rows) compete to win those production orders. Each order request will be considered an item of the auction. A third party called the manager (or auctioneer) is the agent who coordinates trade between customers and suppliers. Suppliers will have the opportunity to send production offers (i.e., bids) to manufacture combinations of orders that optimize their production capacity. In the example shown Figure 1, each of the three customer companies requests the production of one order (i.e., items colored in red, green, and yellow). \u2018Manufacturer a\u2019 bids for the production of the 3 orders (i.e., the combination of items highlighted in blue); \u2018Manufacturer b\u2019 bids for the production of 2 orders (i.e., the combination of items highlighted in green) and \u2018Manufacturer c\u2019 bids for the production of one order (i.e., the item highlighted in yellow). Over successive rounds, customer companies can raise their bids (i.e., the price they are willing to pay in each round for the orders requested) consistently below their reservation price, and manufacturers can lower their bids (i.e., the fee for which they are willing to produce each combination of items) always above their manufacturing costs. The auctioneer will solve the allocation of orders to suppliers.<\/span><\/p>\n

This platform entails advantages for both sides of an AM market. On one side, customers can access a large number of manufacturers with minimum effort. On the other hand, AM suppliers can combine orders from different customers to optimize their production capacity.<\/span><\/p>\n

Some existing digital marketplaces help match supply and demand. However, those systems merely facilitate bilateral conversations between customers and suppliers, so they do not actually make any customer-supplier allocations and therefore, no optimization is made<\/u> (Holzmann et al. 2020).<\/span><\/p>\n<\/div>\n

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Schematic of the coordination of the business transactions in the platform<\/a><\/p>\n<\/div>\n

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References:<\/strong><\/h3>\n

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