Evelyn A Thomchick
email: ethomcick@psu.edu

Kusumal Ruamsook
email: kusumal@psu.edu

Potential Woody Biomass Supply

Keywords: Chain Scenarios: A Conceptual Study Woody biomass, logistics strategies, supply chain scenarios

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Diminishing fossil fuel resources, increased competition for those fuels, and a growing realization of the economic and environmental impacts of the dependence on fossil fuels have led to a need to develop renewable energy resources. Bio-energy is one potential component of a renewable energy scenario for the future, accounting for approximately half (49%) of total renewable energy produced and consumed in the United States in 2012. A wide variety of biomass feedstock is currently used to produce bio-energy, ranging from woody biomass, agricultural wastes, to urban wastes. Of these feedstock varieties, woody biomass accounted for nearly half (45%) of the total biomass-based renewable energy consumed in 2012 (US Energy Information Administration, 2013). These figures demonstrate the considerable contribution of bio-energy, notably woody biomass-based energy, for national renewable energy effort. However, managing biomass feedstock supply and its associated logistics activities are persistent issues. Intrinsically low in bulk densities, biomass feedstock creates logistics challenges in terms of its volume that has to be handled, stored, and transported [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Damery | The Northeast Forest Bio-products Puzzle[END_REF][START_REF] Sokhansanj | Cost Benefit of Biomass Supply and Pre-Processing[END_REF][START_REF] Weiner | The Ripple Effect[END_REF]. Given the low value of biomass materials and logistics challenges involved, the costs of moving biomass feedstock from supply sources to a biorefinery plant can be prohibitive and often exceed market values for the biomass itself [START_REF] Kram | Is Biomass Harvesting Sustainable? Biomass Magazine[END_REF][START_REF] Nicholls | Biomass Utilization for Bioenergy in the Western United States[END_REF].

Managerial predicaments also arise due to the many business entities that make up a woody biomass supply chain. These entities perform two important physical functions of a supply chain. The first function involves production, encompassing forest landowners who produce forest materials; loggers who harvest and convert trees into logs or wood chips; forest product manufacturers whose process residues produce woody biomass; pre-processors who turn forest materials into processed woody biomass such as wood chips and pellets; and bio-refineries who convert woody biomass into heat, electricity, chemicals, and transportation fuels to serve different customers [START_REF] Allen | Logistics Management and Costs of Biomass Fuel Supply[END_REF][START_REF] D'amours | Using Operational Research for Supply Chain Planning in the Forest Products Industry[END_REF][START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF][START_REF] Sokhansanj | Cost Benefit of Biomass Supply and Pre-Processing[END_REF]. The second function involves logistics of moving the various stages of products from one point in the supply chain to the next, encompassing key activities such as transportation, materials handling, warehousing and storage, and inventory control [START_REF] Coyle | Supply Chain Management: A Logistics Perspective[END_REF][START_REF] Fisher | What is the Right Supply Chain for Your Product?[END_REF].

The interconnectivity among various entities, the associated physical functions, and supply sourcing challenges require a total supply chain perspective. Yet, extant studies on woody biomass largely focus on individual entities or sets of activities. The gap in research, coupled with the still evolving nature of the industry, lead to woody biomass supply chains that are currently not well perceived. The primary objective of this paper is to conceptualize potential supply chain scenarios associated with woody biomass. Insights gained are imperative as a basis for further research to promote economically feasible bio-energy as a component of renewable energy scenario for the future.

Methodology

To conceptualize and evaluate potential woody biomass supply chain scenarios, the study draws insights from extensive review of literature. Journal articles in the areas of supply chain and logistics, forest products, biomass feedstock, and bio-energy were selected from archival material available electronically at ProQuest and Academic Search Complete. Other principal data sources are the government and organization websites such as the US Energy Information Administration, the US Forest Service, and the International Energy Agency. Along with these data sources, informal interviews with five researchers in the fields of forest resources, agricultural sciences, and biomass energy were conducted to clarify and verify insights drawn from the literature.

Over 800 pages of documents were examined and coded on three levels according to [START_REF] Glaser | The Discovery of Grounded Theory: Strategies for Qualitative Research[END_REF] and [START_REF] Strauss | Basics of Qualitative Research: Grounded Theory Procedures and Techniques[END_REF]. In the first level, key activities pertinent to the production and logistics functions of supplying woody bio-mass to a bio-refinery are examined. Then, in the second level, we made connections between categories (in this case, the key activities identified above) by exploring the conditions and intera tions that influence the processes of supplying woody biomass. This coding allowed us to integrate data into core categories or potential woody biomass supply chain sc narios. Then, using the core supply chain scenarios as a guide, we selectively coded the existing data obtained from literature survey and additional data gathered from the informal interviews to describe each scenario. At the end of this third we have established the descriptions of potential supply chain scenarios.

Conceptualizing Woody Biomass Supply Chain Scenarios

Three potential woody biomass supply chain scenarios Figures 123. They are: (1) woody biomass sourcing ers, (2) woody biomass sourcing from forest landowners, and (3) woody biomass sourcing from biomass pre follows.

Woody Biomass Sourcing from Forest Product Manufacturer

The first scenario, depicted in Figure 1, is currently preferred by most bio [START_REF] Morgan | Woody Biomass: Can Forests Fuel Our Future? Montana Business Quarterly[END_REF].

Fig. 1. Woody Biomass Sourcing from Forest Product Manufacturers

In this scenario, residues produced by primary wood and their lumber customers secondary forest product manufacturers such as pulp mills, paper mills, and pellet mills [START_REF] Ray | Project-Based Overview of Woody Biomass Initiatives in the Generation of Thermal Energy, Electricity, and Transportation Fuels in the Eastern Hardwood Region of the United States[END_REF]. Classified as industrial private landow manufacturers possess large forest assets for uses in their manufacturing requirements [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF]IBISWorld, 2009;[START_REF] Mcdill | Associate Professor of Forest Management and Program Chair for Forest Science[END_REF]. They are identified, and the properties that characterize each activity are examined. Then, in the second level, we made connections between categories (in se, the key activities identified above) by exploring the conditions and intera tions that influence the processes of supplying woody biomass. This coding allowed us to integrate data into core categories or potential woody biomass supply chain sc Then, using the core supply chain scenarios as a guide, we selectively coded the existing data obtained from literature survey and additional data gathered from the informal interviews to describe each scenario. At the end of this third-level coding,

ve established the descriptions of potential supply chain scenarios.

Conceptualizing Woody Biomass Supply Chain Scenarios

hree potential woody biomass supply chain scenarios are identified and depicted in 3. They are: (1) woody biomass sourcing from forest product manufactu ers, (2) woody biomass sourcing from forest landowners, and (3) woody biomass sourcing from biomass pre-processers. We briefly describe each scenario in turn

Woody Biomass Sourcing from Forest Product Manufacturers

The first scenario, depicted in Figure 1, is currently preferred by most bio-Woody Biomass Sourcing from Forest Product Manufacturers n this scenario, residues produced by primary wood processors, such as sawmills and their lumber customers, are the primary feedstock for both the bio-refineries and secondary forest product manufacturers such as pulp mills, paper mills, and pellet

). Classified as industrial private landowners, these forest product manufacturers possess large forest assets for uses in their manufacturing requirements [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF]IBISWorld, 2009;[START_REF] Mcdill | Associate Professor of Forest Management and Program Chair for Forest Science[END_REF]. They he properties that characterize each activity are examined. Then, in the second level, we made connections between categories (in se, the key activities identified above) by exploring the conditions and interactions that influence the processes of supplying woody biomass. This coding allowed us to integrate data into core categories or potential woody biomass supply chain sce-Then, using the core supply chain scenarios as a guide, we selectively coded the existing data obtained from literature survey and additional data gathered from the level coding,

Conceptualizing Woody Biomass Supply Chain Scenarios

depicted in from forest product manufacturers, (2) woody biomass sourcing from forest landowners, and (3) woody biomass describe each scenario in turn as -refineries such as sawmills refineries and secondary forest product manufacturers such as pulp mills, paper mills, and pellet ners, these forest product manufacturers possess large forest assets for uses in their manufacturing requirements [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF]IBISWorld, 2009;[START_REF] Mcdill | Associate Professor of Forest Management and Program Chair for Forest Science[END_REF]. They contract independent loggers for harvesting timber and delive forestland to their production facil proximately 80 percent of the output loggers generated the tree that has a larger diameter, and thus a higher value nearby sawmill for processing. The upper, thinner parts of the tree that have lower value are commonly delivered to pulp mills and paper mills [START_REF] Crooks | Protecting Forests and Supporting Renewable Energy[END_REF]; D'Amours et al., 2008; Frisk et a typically use trucks and equipment they owned or leased to make the deliveries mills (IBISWorld, 2010), although in some cases transport service providers or co tract haulers (e.g. trucking companies and rail carriers et al., 2009). In turn, the mills delivery mill chips to the bio arrangement between the and size and frequency of deli

Woody Biomass Sourcing from Forest Landowners

The second scenario, depicted in Figure 2, makes use of forest materials that contri ute the potentially most abundant sources of woody biomass in the United States [START_REF] Nicholls | Biomass Utilization for Bioenergy in the Western United States[END_REF].

Fig. 2.

In this scenario, forest biomass is sourced from landowners that can be public ent ties (e.g. federal, state, and local and municipal land and forests), private industrial entities (e.g. saw mills, and pulp and paper mills), or non (e.g. individuals or corporations other than forest product industry) (Biomass R search and Development Board, 2008b; D'Amours et al., 2008). As in the first scena io, forest landowners contract loggers for felling standing timber, delivering the ou puts, and disposing logging residues. Decisions on the methods of residue disposal (e.g. piling and burning, scattering throughout a site to decompose, and processing as contract independent loggers for harvesting timber and delivering logs from their forestland to their production facilities [START_REF] Myers | Supporting Wood Supply Chain Decisions with Simulation for a Mill in Northwestern BC[END_REF]. In general, a proximately 80 percent of the output loggers generated-typically the lower part of the tree that has a larger diameter, and thus a higher value-is transported directly to a nearby sawmill for processing. The upper, thinner parts of the tree that have lower value are commonly delivered to pulp mills and paper mills [START_REF] Crooks | Protecting Forests and Supporting Renewable Energy[END_REF]; D'Amours et al., 2008; [START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF]IBISWorld, 2009IBISWorld, , 2010)). These log typically use trucks and equipment they owned or leased to make the deliveries (IBISWorld, 2010), although in some cases transport service providers or co trucking companies and rail carriers) may also be involved (Bol the mills delivery mill chips to the bio-refineries according to t arrangement between the parties, which vary in terms of transportation mode used, and size and frequency of deliveries [START_REF] Liu | [END_REF][START_REF] Mcdill | Associate Professor of Forest Management and Program Chair for Forest Science[END_REF][START_REF] Roth | [END_REF].

Woody Biomass Sourcing from Forest Landowners

The second scenario, depicted in Figure 2, makes use of forest materials that contri ute the potentially most abundant sources of woody biomass in the United States . Woody Biomass Sourcing from Forest Landowners In this scenario, forest biomass is sourced from landowners that can be public ent ties (e.g. federal, state, and local and municipal land and forests), private industrial saw mills, and pulp and paper mills), or non-industrial private entities (e.g. individuals or corporations other than forest product industry) (Biomass R search and Development Board, 2008b; D'Amours et al., 2008). As in the first scena rs contract loggers for felling standing timber, delivering the ou puts, and disposing logging residues. Decisions on the methods of residue disposal (e.g. piling and burning, scattering throughout a site to decompose, and processing as ring logs from their

). In general, aptypically the lower part of sported directly to a nearby sawmill for processing. The upper, thinner parts of the tree that have lower value are commonly delivered to pulp mills and paper mills [START_REF] Crooks | Protecting Forests and Supporting Renewable Energy[END_REF] ). These loggers typically use trucks and equipment they owned or leased to make the deliveries to the (IBISWorld, 2010), although in some cases transport service providers or conmay also be involved (Bolding refineries according to the , which vary in terms of transportation mode used,

The second scenario, depicted in Figure 2, makes use of forest materials that contribute the potentially most abundant sources of woody biomass in the United States In this scenario, forest biomass is sourced from landowners that can be public entities (e.g. federal, state, and local and municipal land and forests), private industrial industrial private entities (e.g. individuals or corporations other than forest product industry) (Biomass Research and Development Board, 2008b; D'Amours et al., 2008). As in the first scenarrs contract loggers for felling standing timber, delivering the outputs, and disposing logging residues. Decisions on the methods of residue disposal (e.g. piling and burning, scattering throughout a site to decompose, and processing as biomass) may be left to the loggers, or set forth by landowners and third-party environmental certification standards for sustainable forest management practices (Benjamin et al., 2009; [START_REF] Elmore | No Chip Left Behind[END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF].

In turn, bio-refineries transact with the loggers for the forest biomass. Biomass can be delivered in either chipped or non-chipped forms. The former involves in-forest chipping, which is well integrated into existing timber harvesting systems (D'Amours et al., 2008; [START_REF] Gunnarsson | Supply Chain Modeling of Forest Fuel[END_REF]. In this case, loggers with portable woodchipping equipment produce wood chips from logging residues and from whole trees of non-merchantable quality for direct sale to bio-refineries and other buyers such as pulp and paper mills (D'Amours et al., 2008). Unlike clean chip by-products from forest product manufacturers, these logging chips (also called dirty chips and hog fuel) are characterized by high moisture and varied physical quality in terms of size, shape, and chemical makeup [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Kimbell | More Energy from Wood: What Are the Prospects[END_REF][START_REF] Kram | Is Biomass Harvesting Sustainable? Biomass Magazine[END_REF][START_REF] Weiner | The Ripple Effect[END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF]. These characteristics render biomass that has low net energy density by mass, is unsuitable for long-term storage, and is uneconomical for long distance transportation [START_REF] Crooks | Protecting Forests and Supporting Renewable Energy[END_REF][START_REF] Goldstein | Woody Biomass as Renewable Energy Source[END_REF][START_REF] Jackson | Wood2Energy: A State of the Science and Technology Report[END_REF][START_REF] Liu | [END_REF][START_REF] Mcdill | Associate Professor of Forest Management and Program Chair for Forest Science[END_REF][START_REF] Wallace | Associate, BoozïAllenïHamilton[END_REF][START_REF] Richard | Director of Penn State Institutes of Energy and the Environment[END_REF]. Coupled with the often geographically dispersed forest lands, transportation cost could easily exceed the costs of biomass itself [START_REF] Bolding | Productivity and Costs of an Integrated Mechanical Forest Fuel Reduction Operation in Southwest Oregon[END_REF].

Alternatively, non-chipped forest biomass can be transported to bio-refinery facilities where it will be chipped using a larger-scale, more powerful stationary chipper (D' Amours et al., 2008). In comparison to the in-forest chipping, this alternative gives the bio-refineries greater control over the chipping process and offers a cost advantage gained from economies of scale and the powerful equipment used. These advantages, however, are offset by the more expensive cost of transporting nonchipped, as opposed to chipped, forest biomass to the refinery facilities (D'Amours et al., 2008; Jackson et al., 2010).

Woody Biomass Sourcing from Biomass Pre-Processers

The third scenario, illustrated in Figure 3, depicts business opportunities that emerged from the need to pre-process and pre-treat biomass to alleviate production and logistics issues pertinent to biomass. In this scenario, woody biomass suppliers are relatively large facilities with storage capacity and flexible system capable to pre-process and pre-treat a wide range of biomass materials. The biomass supply this scenario resembles that of a huband-spoke system of biomass processing and distribution [START_REF] Carolan | Technical and Financial Feasibility Analysis of Distributed Bioprocessing Using Regional Biomass Pre-Processing Enterprises[END_REF][START_REF] Roth | [END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF][START_REF] Yepsen | Wood Processing Innovations[END_REF]. A typical biomass pre-processer assumes a role of an intermediary who engages in securing and establishing contracts for various types of biomass from various sources. It also manages the collection, storage, pre-process, and delivery of biomass to the bio-refinery and other biomass users [START_REF] Carolan | Technical and Financial Feasibility Analysis of Distributed Bioprocessing Using Regional Biomass Pre-Processing Enterprises[END_REF][START_REF] Sokhansanj | Cost Benefit of Biomass Supply and Pre-Processing[END_REF].

A pre-processor's large-scale, flexible system permits physical transformation (e.g. pre-processed briquettes, pellets, and torrefied biomass) and chemical transformation (e.g. pre-treated to alter chemical component) of a wide range of biomass feedstock to satisfy the quality and quantity requirements of biomass users before making delivery [START_REF] Roth | [END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF]. Hence, aside from improving the quality of biomass as feedstock, these processing technologies possible to economically transport biomass over greater distances and to store large quantities of biomass for longer periods of time. Production cost advantage is also achieved because of economies of scope gained from the variety of feedstock use and flexible product mix, used (Carolan et

Conclusion

This conceptual study identifies reveals the roles and interactions between different members duction and logistic functions of the supply cha moving woody biomass rized in Table 1. First, loggers play an important production role in all vantage of the new biomass market oped and new managerial and operational practices must be devised existing operations and emerging biomass business Research and Development Board, 2008b; Damer 2009; [START_REF] Kram | Is Biomass Harvesting Sustainable? Biomass Magazine[END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF][START_REF] Yepsen | Wood Processing Innovations[END_REF].

Second, forest product manufacturers possess technological and infrastructural a vantages that render them competitive as biomass suppliers. They have access to their abundant industrial forestlands, well site infrastructure for pre new markets of biomass business expansion that [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF][START_REF] Jackson | Wood2Energy: A State of the Science and Technology Report[END_REF][START_REF] Sklar | Ethanol from Wood Waste an Opportunity for Refiners[END_REF]. A more satisfy the quality and quantity requirements of biomass users before making delivery [START_REF] Roth | [END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF]. Hence, aside from improving the quality of biomass as feedstock, these processing technologies are also logistically advantageous, ma possible to economically transport biomass over greater distances and to store large quantities of biomass for longer periods of time. Production cost advantage is also achieved because of economies of scope gained from the variety of feedstock use and flexible product mix, and economies of production scale and powerful equipment used [START_REF] Carolan | Technical and Financial Feasibility Analysis of Distributed Bioprocessing Using Regional Biomass Pre-Processing Enterprises[END_REF][START_REF] Jackson | Wood2Energy: A State of the Science and Technology Report[END_REF][START_REF] Sokhansanj | Cost Benefit of Biomass Supply and Pre-Processing[END_REF]; Taylor Woody Biomass Sourcing from Biomass Pre-Processing Facilities al study identifies three potential woody biomass supply chain the roles and interactions between different members in performing the pr duction and logistic functions of the supply chains. Three important implications moving woody biomass-based bio-energy forward arise from the findings First, loggers play an important production role in all three scenarios. To take a vantage of the new biomass market opportunities, logging capacities must be deve ew managerial and operational practices must be devised to integrate the existing operations and emerging biomass business [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF]Biomass Research and Development Board, 2008b;[START_REF] Damery | The Northeast Forest Bio-products Puzzle[END_REF][START_REF] Elmore | No Chip Left Behind[END_REF][START_REF] Kram | Is Biomass Harvesting Sustainable? Biomass Magazine[END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF][START_REF] Yepsen | Wood Processing Innovations[END_REF].

Second, forest product manufacturers possess technological and infrastructural a vantages that render them competitive as biomass suppliers. They have access to their forestlands, well-established transportation systems, and the on site infrastructure for pre-processing, storage, and handling of woody biomass. The biomass as feedstock for bio-energy present great opportunities for business expansion that leverage these existing capital investment and resources jamin et al., 2009; [START_REF] Frisk | Cost Allocation in Collaborative Forest Transportation[END_REF][START_REF] Jackson | Wood2Energy: A State of the Science and Technology Report[END_REF][START_REF] Sklar | Ethanol from Wood Waste an Opportunity for Refiners[END_REF]. A more satisfy the quality and quantity requirements of biomass users before making delivery [START_REF] Roth | [END_REF][START_REF] Yepsen | Forest Thinning Strategies for Biomass Utilization[END_REF]. Hence, aside from improving the quality of biomass as ous, making it possible to economically transport biomass over greater distances and to store large quantities of biomass for longer periods of time. Production cost advantage is also achieved because of economies of scope gained from the variety of feedstock used and powerful equipment used (Carolan et vertically integrated avenue is to expand into the bio-refinery business and become an "integrated forest product bio-refinery" enterprise that produces bio-products heat, transport fuel, and bio-chemical) in addition to conventional forest products [START_REF] Damery | The Northeast Forest Bio-products Puzzle[END_REF][START_REF] Feng | Integrated Bio-Refinery and Forest Products Supply Chain Network Design Using Mathematical Programming Approach[END_REF][START_REF] Kimbell | More Energy from Wood: What Are the Prospects[END_REF].

Woody Biomass Supply Chain Scenarios: Key Parties, Roles, and Advantages/Disadvatages Finally, infrastructure development is vital to enhance reliability and efficiency of the production and logistics functions of woody biomass supply chains. Transport in particular, is much needed to tap into the abundant, yet highly dispersed forest biomass resources. Additionally, an extensive industrial infrastru ture is required to advance the current experimental operations to commercial stage Industrial infrastructure encompasses not only the bio-refinery facility itself, but also processing and treatment facilities, and communication technology needed to improve logging and biomass harvest efficiencies [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Jackson | Wood2Energy: A State of the Science and Technology Report[END_REF][START_REF] Kimbell | More Energy from Wood: What Are the Prospects[END_REF]. This work was funded by a Pennsylvania State Sustainability Seed Grant awarded by the Pennsylvania State Institutes of Energy and the Enviro ment (PSIEE). We thank Dr. Jude Liu, Dr. Marc E. McDill, Dr. Tom Richard, Dr. Gregory W. Roth, and Mr. Bob Wallace for their valuable insights on the woody bi mass, forest management, and renewable energy industries. refinery business and become an products (e.g. in addition to conventional forest products , and to enhance reliability and efficiency of the production and logistics functions of woody biomass supply chains. Transportais much needed to tap into the abundant, yet highly dispersed forest biomass resources. Additionally, an extensive industrial infrastructure is required to advance the current experimental operations to commercial stages. facility itself, but also processing and treatment facilities, and communication technology needed to improve logging and biomass harvest efficiencies [START_REF] Benjamin | D: Challenges and Opportunities for the Northeastern Forest Bioindustry[END_REF][START_REF] Jackson | Wood2Energy: A State of the Science and Technology Report[END_REF] This work was funded by a Pennsylvania State Sustainability Seed Grant awarded by the Pennsylvania State Institutes of Energy and the Environ-Tom Richard, Dr. Gregory W. Roth, and Mr. Bob Wallace for their valuable insights on the woody bio-

  al., 2007; Jackson et al., 2010; Sokhansanj and Fenton, 2006; Taylor et al., 2010).

Fig. 3 .

 3 Fig. 3. Woody Biomass Sourcing from

Table 1 .

 1 al., 2007; Jackson et al., 2010; Sokhansanj and Fenton, 2006; Taylor Woody Biomass Supply Chain Scenarios: Finally, infrastructure development is the production and logistics functions of woody biomass supply chains. Transport tion infrastructure, in particular dispersed forest biomass resources. Additionally, an extensive industrial infrastru ture is required to advance the current experimental operations to commercial stage Industrial infrastructure encompasses not only the bio pre-processing and treatment facilities, and communication technology needed to improve logging and biomass harvest efficiencies (Benjamin et al., 2009; Jackson et al., 2010; Kimbell et al Acknowledgments. This work was funded by a Pennsylvania State Sustainability Seed Grant awarded by the Pennsylvania State Institutes of Energy and the Enviro ment (PSIEE). We thank Dr. Jude Liu, Dr. Marc E. McDill, Dr. Gregory W. Roth, and Mr. Bob Wallace for their valuable insights on the woody bi mass, forest management, and renewable energy industries.

	upply chains and
	in performing the pro-
	hree important implications in
	arise from the findings summa-
	scenarios. To take ad-
	opportunities, logging capacities must be devel-
	to integrate the
	(Benjamin et al., 2009; Biomass
	y and Benjamin, 2007; Elmore,
	Second, forest product manufacturers possess technological and infrastructural ad-
	vantages that render them competitive as biomass suppliers. They have access to their
	established transportation systems, and the on-
	processing, storage, and handling of woody biomass. The
	energy present great opportunities for
	existing capital investment and resources
	jamin et al., 2009; Frisk et al., 2010 Jackson et al., 2010; Sklar, 2008). A more


Uwe Dombrowski

Sebastian Weckenborg

Determination of the Spare Parts Demand for Maintenance, Repair and Overhaul Service Providers

Keywords: Spare parts management, MRO, Maintenance, Repair, Overhaul

Service providers for maintenance, repair and overhaul (MRO) of aircrafts and components face major challenges. The calculation of an optimal stock level for components is one of these. The optimal stock level is highly influenced by the customers demand. Furthermore the stock level influences the profitability of the processes. The challenge for MRO service provider is to respond to the changing customer base and to adapt the stock level for components dynamically. For this purpose, an approach for the determination of the spare parts demand is described in this paper. The approach consists of a hierarchical derivation of reliability parameters in the first step and finally an assessment of the derived reliability parameters. By this assessment the relevance of different impact factors can be determined. The determination of the spare parts demand and the optimal stock level can thereby be determined much more accurately.

Introduction

The efficient spare parts supply during the entire life cycle of the primary product is a differentiating quality characteristic in competition. It can lead to an improved customer satisfaction and strengthens the company in the market. [START_REF] Dombrowski | Efficient Spare Part Management to Satisfy Customers Need[END_REF], [START_REF] Leichnitz | Bewertungsverfahren von Produktportfolios im Rahmen des Obsolescence Management[END_REF], [START_REF] Quantschnig | Planungsmodell kostenoptimaler Versorgungsstrategien im OEM-Ersatzteilmanagement[END_REF] For the realization of an effective spare parts management, it is important to understand the correlations between different impact factors regarding the spare parts demand. Both the life cycle of the primary product and the life cycle of the component have to be considered. There are different strategies for the spare parts supply during and after the serial production of the primary product. These result from the number of primary products on the market and especially from the technological characteristics of the individual components as well as the reliability parameters. This results in different demand patterns, not only in volume, but also in demand continuity and the demand predictability. [START_REF] Dombrowski | Life Cycle Costing as a Tool for effective Spare Parts Management[END_REF], [START_REF] Loukmidis | Lebenszyklusorientierte Planungsstrategien für den Ersatzteilbedarf[END_REF], [START_REF] Wrehde | Planung und Umsetzung der Nachserienversorgung in der Nachserienversorgung[END_REF], [START_REF] Bothe | Planung und Steuerung der Ersatzteilversorgung nach Ende der Serienfertigung[END_REF] The life-cycle-oriented spare parts management focuses on this specific problem and contains several supply strategies to realize an efficient post-series supply. These supply strategies are the development of a compatible successive product generation, the storing of a final lot, the periodical internal or external production, the reuse of used components and the repair of used components. [START_REF] Hesselbach | Planning Process for the Spare Part Management of Automotive Electronics[END_REF], [START_REF] Dombrowski | Method-supported Product Development for Post-series Supply[END_REF], [START_REF] Dombrowski | Consideration of Changing Impact Factors for of Post-series Supply[END_REF] The aviation industry is characterized by extremely long life-cycles of the primary products with a high degree of efficiency, small lot sizes, a strict regulation and a high degree of individualization. The share of the services business in the aviation industry is very high. [START_REF] Schmidt | Mehrwert schaffen durch Fokussierung auf das Servicegeschäft[END_REF] Airlines often buy the maintenance services, in order to concentrate on their core business. Compared with other industries, the repair of components in the aviation industry is highly profitable and is therefore common practice [START_REF] Meifarth | Ersatzteilmanagement elektronischer Bauteile -Randbedingungen und Anforderungen in der Zivilluftfahrt[END_REF]. [START_REF] Dombrowski | Lebenszyklusorientiertes Ersatzteilmanagement -Neue Herausforderungen durch innnovationsstarke Bauteile in langlebigen Primärprodukten[END_REF] 2

Tasks and Challenges of Maintenance, Repair and Overhaul Service Providers

The range of tasks of a service provider in the aviation field is very large. A classification of the tasks is possible by the distinction between the value adding organizational units of a service provider. These organizational units are the spare parts supply, the spare parts maintenance, the logistics and the aircraft maintenance. The supply of spare parts and spare parts maintenance form the spare parts management which is in the focus of this paper. [START_REF] Bauer | Lebenszyklusorientierte Optimierung von Instandhaltungssystemen für hochwertige Investitionsgüter[END_REF], [START_REF] Hinsch | Industrielles Luftfahrtmanagement: Technik und Organisation luftfahrttechnischer Betriebe[END_REF] The spare parts supply has the inventory and planning responsibility for the spare parts. The spare parts maintenance can be seen as a service for the spare parts supply. The spare parts maintenance is responsible for the repair of spare parts. For this purpose, the spare parts maintenance has its own manufacturing and workshop areas. By the logistics the spare parts are transported from/to the customer. The aircraft maintenance takes place on the basis of strict regulations within different maintenance events. [START_REF] Bauer | Lebenszyklusorientierte Optimierung von Instandhaltungssystemen für hochwertige Investitionsgüter[END_REF] To avoid long-term ground time of an aircraft, so-called Line Replaceable Units (LRU) are used in aircrafts when possible. A change of these LRUs is carried out quickly and the aircraft is therefore immediately ready for use again. Meanwhile, the exchanged part is repaired or overhauled and preserved for the next installation. [START_REF] Wagner | Estimation of daily unscheduled line maintenance events in civil aviation[END_REF] Furthermore large service providers sometimes offer a component pooling service for aircraft operators. If a defect of a component occurs a new component is provided by the pool provider. The removed part will be analyzed by the service provider afterwards and depending on technical and economic aspects the decision is made whether the component will be repaired or replaced. [START_REF] Schmidt | Mehrwert schaffen durch Fokussierung auf das Servicegeschäft[END_REF], [START_REF] Hinsch | Industrielles Luftfahrtmanagement: Technik und Organisation luftfahrttechnischer Betriebe[END_REF] In the context of the spare parts supply the determination of the spare parts demand is a major challenge, especially for service providers. [START_REF] Bauer | Lebenszyklusorientierte Optimierung von Instandhaltungssystemen für hochwertige Investitionsgüter[END_REF], [START_REF] Dombrowski | Demand control loops for a global spare parts management[END_REF] This is caused by a variety of different aspects. The primary products in the aviation industry have very long life cycles. On average, aircrafts fly over 20 years. This leads to many different generations of aircraft and many different generations of components which should be supplied by a service provider. Electronic components in today's aircrafts are from the period in which the aircraft were developed. Furthermore the costs of the components in the aviation industry are very high. Because of these high costs, the very long lifecycles, the small amounts of components the repair of components in the aviation industry is highly economical. [START_REF] Schmidt | Mehrwert schaffen durch Fokussierung auf das Servicegeschäft[END_REF] The failure behavior of many components is not known, the absolute number of failures of a specific component is usually very low. It can be assumed that the different generations of components installed have different failure rates. In many cases, the information about the components installed is missing. This is because the cost of the identification would be very high and also the operator of an aircraft does not have this information. This may be because the operator has leased the aircraft. In this case the probabilities of installation for different components have to be determined.

3

Approach for Determination of Spare Parts Demands

Impact factors regarding the spare parts demand

A basic approach for the determination of the spare parts demand (D) is by using the correlation of the intensity of use for a future period and the failure rate of a component [START_REF] Linser | Performance Measurement in der Flugzeuginstandhaltung[END_REF]. This and other approaches, however, are only partially applicable for MRO service providers. Other approaches can exemplary be found in [START_REF] Regattieri | Managing lumpy demand for aircraft spare parts[END_REF], [START_REF] Bevilacqua | Failure rate prediction with artificial neural networks[END_REF], [START_REF] Ghobbar | Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model[END_REF], [START_REF] Louit | A practical procedure for the selection of timeto-failure models based on the assessment of trends in maintenance data[END_REF], [START_REF] Luxhøj | Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft[END_REF]. While the intensity of use of the primary product (the aircraft) can be determined well, there are difficulties in determining the intensity of use of a component of the aircraft. This is due to the fact that the components used are often not known. This complicates the determination of a reliable failure rate for different components. However, the knowledge about the impact factors regarding the failure rate and thus the spare parts demand is essential for a MRO service provider.

Fig. 1. Data base in a certain observation period

In figure 1 the existing data base and the uncertainties are shown exemplarily. The failures of materials are recorded in a certain observation period for different aircrafts. By the definition of the observation period the data is censored at the beginning and at the end of this period. Furthermore, the data of aircrafts, in which the components are not known, is recorded to. All censored data is considered in the analysis. Different impact factors regarding spare parts demand are mentioned in literature. These impact factors can be assigned to the primary product, the spare part itself, the maintenance strategy or to the market situation and other surroundings [START_REF] Dombrowski | Lebenszyklusorientiertes Ersatzteilmanagement -Neue Herausforderungen durch innnovationsstarke Bauteile in langlebigen Primärprodukten[END_REF], [START_REF] Dombrowski | Demand control loops for a global spare parts management[END_REF]. For simplicity, in the following part the focus will be on a limited number of impact factors. The approach allows the integration of further and other impact factors.

It is assumed that the demand (failure rate) is proportional either to the flight hours (FH), the flight cycles (FC) or the calendar time (CT). In the following these impact factors are described as measurable impact factors. Furthermore categorical impact factors are assumed. These can be the aircraft type, the operator of the aircraft and the material which is used, for example. The influence of these factors has been shown in different studies [START_REF] Wagner | Estimation of daily unscheduled line maintenance events in civil aviation[END_REF] but in general the relationship between the mean demand and flying hours or flight cycles is not well understood [START_REF] Ghobbar | Sources of intermittent demand for aircraft spare parts within airline operations[END_REF].

Hierarchical derivation of reliability parameters

The assessment of impact factors regarding the spare parts demand of MRO service providers is a main objective of this approach. the context of the assessment of the impact factors the handling of the uncertainties is an important aspect, which is not in the focus of this paper. It is important to know that the uncertainties about the components used are closely linked to the assessment of the impact factors.

Fig. 2. Hierarchical derivation of reliability parameters

The assessment of the impact factors is done for a technical function in an aircraft. This could be a flight management computer, for example. In a first step the categorical impact factors are combined according to their potential relevance in any combination. For three different impact factors six different combinations can be created. Each combination represents a hierarchical alternative for the impact factors.

In figure 2 a hierarchical alternative is shown as an example. The first level is always represented by the technical function (H0). In this example the second level is defined by the material (H1), underlying the operator (H2) and finally the aircraft type (A3). A technical function is always defined by all impact factors. This means it is defined by a certain material, an operator and an aircraft type. However, it is not use-

ܴܤܶܯ = σ ொ כிு సభ ,]ܪܨ[ ܴܤܥܯ = σ ொ כி సభ ,]ܥܨ[ ܴܤܦܯ = σ ொ כ் సభ [D] (1)
At each step from one to the next hierarchical level, it is checked whether a separation of the failure rate regarding the overlying hierarchical level is significant. If the reliability parameter is significantly different this value is separated and the underlying hierarchical level receives an individual reliability parameter. In figure 2 material 1 and material 3 are determined to be significant. Material 2 is not significantly different from the technical function and the reliability parameter is set the same as the reliability parameter of technical function. The significance is analyzed in each hierarchical level.

Assessment of the derived reliability parameters

The derived reliability parameters are assessed in the next step. For this purpose the complete data set has been separated into two random data sets. The determination of the reliability parameters is done on the trainings set and the quality of these is reviewed on the basis of the test set. With the values determined in the training set, the resulting changes in the test set are calculated. [START_REF] Dombrowski | Demand control loops for a global spare parts management[END_REF] Table 1. Assessment of the derived reliability parameters The deviation between the actual changes and the calculated changes is used as a measure for the quality of forecast. This is done for each hierarchical alternative and each reliability parameter. By repeating this random separation of the data set into two data sets the best hierarchical alternative as well as a best reliability parameter (MTBR, MCBR and MDBR) can be determined. An example of this assessment is shown in table 1. This example shows two different hierarchical alternatives (A1 and A5) and the reliability parameter for the different combinations. In this example the MTBR is the best reliability parameter and the relevance of the impact factors is set by the hierarchical alternative A1. In the next step it should be analyzed whether it is possible to determine the relative influence of the different reliability parameters.

Customer projects of MRO Service Providers

The integration of new customers, new components or new aircraft types, for example, is a recurring and challenging task for MRO service providers. These integrations are called customer projects in general and are a special characteristic of MRO service providers. [START_REF] Bauer | Lebenszyklusorientierte Optimierung von Instandhaltungssystemen für hochwertige Investitionsgüter[END_REF] Due to missing information regarding material, aircraft type or customer it is difficult to assign a reliability parameter.

Fig. 3. Application of hierarchical derivation on customer projects

Different forms of customer projects can be distinguished. In figure 3 different customer projects are shown. Out of three different impact factors eight customer projects can be derived. The customer projects can be differentiated whether an impact factor is known or new. The complexity increases significantly by adding additional impact factors. In customer project C3, for example, a known customer wants to be supplied with a known material, which is installed in a new aircraft type. For this technical function the hierarchical alternative A4 has been determined methodically. In combination, in this customer project the reliability parameter of the hierarchical level H2 should be used. By the hierarchical assessment of the impact factors it is possible to react to missing information accordingly.

  Time Between Removal, MCBR = Mean Cycles Between Removal, MDBR = Mean Duration Between Removals, N = Number of Failures, FH = Flight Hours, FC = Flight Cycles, D = Days ful to determine an individual reliability parameter for each possible combination.One reason for this is the large variance of possible failure intervals. Because of this the significance of each combination is analyzed. With the three measurable impact factors (FH, FC, CT) three different reliability parameters can be derived. The mean time between removal (MTBR), the mean cycles between removal (MCBR) and the mean duration between removal (MDBR). These are calculated by the sum of all flight hours (or all flight cycles or the calendar time) of the whole aircraft population (m), multiplied with the quantity per aircraft of the component (QPA) in a certain observation period divided by the number of failures (see equation 1).[START_REF] Meifarth | Ersatzteilmanagement elektronischer Bauteile -Randbedingungen und Anforderungen in der Zivilluftfahrt[END_REF]

mean-square error 1,4354 1,6146 1,5849 basis: training set basis: test set reliability parameter actual changes calculated changes measure

	FUNCTION	MATERIAL OPERATOR ACTYPE MTBR MCBR MDBR	N	FH	CYC	D	MTBR MCBR MDBR MTBR MCBR MDBR
	A1 CONTROL UNIT AB MATERIAL 1 OPERATOR 1 ACTYPE 1 41.245 7.536 4.765	4 214.474 38.282 17.344	5,20	5,08	3,64	1,44	1,17	0,13
	A1 CONTROL UNIT AB MATERIAL 1 OPERATOR 1 ACTYPE 2 40.293 7.430 8.374	3 139.010 26.079 25.373	3,45	3,51	3,03	0,20	0,26	0,00
	A1 CONTROL UNIT AB MATERIAL 1 OPERATOR 2 ACTYPE 1 44.004 8.530 5.547	4 197.137 40.261 10.428	4,48	4,72	1,88	0,23	0,52	4,49
	A1 CONTROL UNIT AB MATERIAL 1 OPERATOR 2 ACTYPE 2 44.004 8.530 5.547	2 93.288 13.989 9.873	2,12	1,64	1,78	0,01	0,13	0,05
	A1 CONTROL UNIT AB MATERIAL 2 OPERATOR 1 ACTYPE 1 24.862 8.874 2.580	1 23.370	9.850 2.167	0,94	1,11	0,84	0,00	0,01	0,03
	A1 CONTROL UNIT AB MATERIAL 2 OPERATOR 2 ACTYPE 1 19.872 5.472 2.092	2 47.295 10.725 4.727	2,38	1,96	2,26	0,14	0,00	0,07
	A1 CONTROL UNIT AB MATERIAL 3 OPERATOR 1 ACTYPE 1 7.832 2.834 1.402	3 22.321	6.886 6.981	2,85	2,43	4,98	0,02	0,33	3,92
	A1 CONTROL UNIT AB MATERIAL 3 OPERATOR 1 ACTYPE 2 9.894 2.888 1.008	4 33.243 13.862 6.289	3,36	4,80	6,24	0,41	0,64	5,01
	A1 CONTROL UNIT AB MATERIAL 3 OPERATOR 2 ACTYPE 1 7.832 2.834 1.402	5 41.118 15.587 3.505	5,25	5,50	2,50	0,06	0,25	6,25
							Root-mean-square error 0,5302 0,6058 1,4888
	A5 CONTROL UNIT AB MATERIAL 1 OPERATOR 1 ACTYPE 1 38.273 7.432 2.876	4 214.474 38.282 17.344	5,60	5,15	6,03	2,57	1,32	4,12
	A5 CONTROL UNIT AB MATERIAL 1 OPERATOR 1 ACTYPE 2 32.754 4.732 4.873	3 139.010 26.079 25.373	4,24	5,51	5,21	1,55	6,31	4,87
	A5 CONTROL UNIT AB MATERIAL 1 OPERATOR 2 ACTYPE 1 38.273 7.432 2.876	4 197.137 40.261 10.428	5,15	5,42	3,63	1,32	2,01	0,14
	A5 CONTROL UNIT AB MATERIAL 1 OPERATOR 2 ACTYPE 2 39.283 7.463 2.743	2 93.288 13.989 9.873	2,37	1,87	3,60	0,14	0,02	2,56
	A5 CONTROL UNIT AB MATERIAL 2 OPERATOR 1 ACTYPE 1 22.735 4.736 1.734	1 23.370	9.850 2.167	1,03	2,08	1,25	0,00	1,17	0,06
	A5 CONTROL UNIT AB MATERIAL 2 OPERATOR 2 ACTYPE 1 38.273 7.432 2.876	2 47.295 10.725 4.727	1,24	1,44	1,64	0,58	0,31	0,13
	A5 CONTROL UNIT AB MATERIAL 3 OPERATOR 1 ACTYPE 1 4.872 1.648 1.220	3 22.321	6.886 6.981	4,58	4,18	5,72	2,50	1,39	7,41
	A5 CONTROL UNIT AB MATERIAL 3 OPERATOR 1 ACTYPE 2 14.230 1.972 1.543	4 33.243 13.862 6.289	2,34	7,03	4,08	2,77	9,18	0,01
	A5 CONTROL UNIT AB MATERIAL 3 OPERATOR 2 ACTYPE 1 5.364 2.463 1.102	5 41.118 15.587 3.505	7,67	6,33	3,18	7,11	1,76	3,31
							Root-

Acknowledgements

The results presented in this paper were developed in the research project "Demand control loops for a global component spare parts management". This research project is part of the regional aviation research program LUFO II-2 funded by the German Federal State of Hamburg (Behörde für Wirtschaft, Verkehr und Innovation).

Summary

The efficient spare parts supply during the entire life cycle of the primary product is a differentiating quality characteristic in competition. For the realization of an effective spare parts management, it is important to understand the correlations between different impact factors regarding the spare parts demand. The aviation industry is characterized by extremely long life-cycles of the primary products with a high degree of efficiency, small lot sizes, a strict regulation and a high degree of individualization. In the context of the spare parts supply the determination of the spare parts demand is a major challenge, especially for service providers. Existing approaches for the determination of the spare parts demand are only partially applicable for MRO service providers. These challenges are faced in a new approach. Within this approach the relevance of different impact factors can be determined and the stock can thereby be analyzed much more accurately. Therefore the cost effectiveness of the services and the customer satisfaction can be increased.


Jaehun Lee
email: jaehun_lee@postech.ac.kr

Kiwook Jung
email: kiwook@postech.ac.kr

Bo Hyun Kim
email: bhkim@kitech.re.kr

Hyunbo Cho
email: hcho@postech.ac.kr

Semantic Web-Based Supplier Discovery Framework

Keywords: Supplier Discovery, Supply Chain Building, Semantic Web

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Rapid globalization of business across emerging markets has changed business competition from a 'company versus company' model into a 'supply chain versus supply chain' model. A supply chain is defined as a network of participants who procure materials, develop products, and deliver them to customers according to coordinated plans [START_REF] Goffin | Managing suppliers: when fewer can mean more[END_REF]. As companies move forward to source globally, supply chain management has gained attention more than ever before. In particular, the discovery and selection of capable suppliers has become a prerequisite for a global supply chain operation. Conventional supplier discovery practices, such as visiting expos, making phone calls, may not contribute to search for new suppliers located overseas.

Manufacturing e-marketplaces have helped companies discover new suppliers and/or buyers fast and effective for their products and services of interest. A few examples include Alibaba.com, mfg.com, and ec21.com. Fig. 1 shows a supplier discovery scenario in e-marketplace, where a buyer attempts to find global suppliers capable of manufacturing a car front bumper mold. Due to the description of require-ments and capabilities in isolation, their true meanings may not be uniformly interpreted from each other. The issue of semantics between suppliers and buyers, then, remains an obstacle.

Fig. 1. Supplier discovery scenario

In response to this problem, the main objective of this paper is to propose a semantic web-based supplier discovery framework for building a long-term strategic supply chain.

2

Semantic-Web based Supplier Discovery Framework

Dimensions of Buyer's Requirements and Supplier's Capability Information

Buyer's requirements consist of product requirements (that is, 'what to manufacture?'), and supplier requirements (that is, 'whom to manufacture with?'). On the other hand, supplier's capability comprises manufacturing capability and nonmanufacturing capability. The sub-requirements will be matched with the subcapabilities, when a buyer searches for suppliers of interest. Matching buyer's requirements with supplier's capability requires semantic searches, because (1) buyer's requirements are not explicitly described, (2) suppliers use heterogeneous formats and terminologies in their capability description, and (3) buyers and suppliers use different level of details in describing their wishes. Therefore, the buyers may not well interpret suppliers' capabilities, and the suppliers may not recognize what the buyers want, either.

A literature survey shows that most discovery methods use product requirements only to match with the supplier's manufacturing capability at a semantic level [START_REF] Ameri | A Matchmaking Methodology for Supply Chain Deployment in Distributed Manufacturing Environments[END_REF][START_REF] Cai | SWMRD: a Semantic Web-based manufacturing resource discovery system for cross-enterprise collaboration[END_REF][START_REF] Jang | Discovering and integrating distributed manufacturing services with semantic manufacturing capability profiles[END_REF][START_REF] Kulvatunyou | A semantic web service framework to support intelligent distributed manufacturing[END_REF]. These methods are not appropriate for building a long-term strategic supply chain, but for a single trading of specific products [START_REF] Virolainen | A survey of procurement strategy development in industrial companies[END_REF].

Overall Supplier Discovery Framework

A key ontology is developed to represent the supplier's capability information and the buyer's requirements. The framework includes ontology building, reasoning and semantic matching. Its brief diagram is shown in Fig. 2.

Fig. 2. Semantic-web based supplier discovery framework 1)

A collaboration ontology is pre-built in order to be used for reasoning. This ontology is built in the form of Web Ontology Language (OWL), and OWL Rules Language (ORL).

2)

Supplier's potential capability is reasoned from classes, properties, reasoning rules, and instances. OWL includes classes and properties, ORL is used for rules, and Resource Description Framework (RDF) for instances. Reasoning tool automates the reasoning process, and it stores the ontology model and its reasoned instances in the 'triple instance store'.

3)

Finally, the buyer's requirement is semantically matched with supplier's capability information using query language.

3

Building a Collaboration Ontology

An Ontology for Supplier's Capability Information

Since the quality of the semantic search is directly determined by the richness of the representation, an ontology plays an essential part in this framework. The collaboration ontology is built using the formal representation language OWL, the most expressive semantic markup language [START_REF] Mcguinness | OWL web ontology language overview[END_REF]. Each ontology concept is represented as a class, using owl:Class. An OWL class is characterized by relationship-type properties using owl:ObjectProperty, or by data-type properties using owl:DatatypeProperty. Fig. 3 shows a supplier ontology in which classes represent supplier's capability information consisting of manufacturing capability and non-manufacturing capability. For example, tool, process, part and product classes are used for reasoning regarding manufacturing capability, while patent, location, and customer classes are used for reasoning regarding non-manufacturing capability [START_REF] Dickson | An analysis of vendor selection systems and decisions[END_REF][START_REF] Thanaraksakul | Supplier evaluation framework based on balanced scorecard with integrated corporate social responsibility perspective[END_REF].

Reasoning Supplier's Potential Capability

Suppliers may not be fully aware of the buyer's requirements that they can potentially satisfy. Therefore, reasoning supplier's potential capability based on the input data is essential. In this framework, the 'triple instance store' is used to retain reasoned supplier's potential capability and input data. Fig. 5 presents an exemplary concept diagram about how to reason manufacturing capabilities with the supplier ontology. A car bumper mold is manufactured by milling and drilling. In addition, milling is made possible if a supplier has a milling machine, and drilling is made possible if a supplier has a high speed drilling machine and a high pressure coolant system. Thus, we can reason a supplier can manufacture a bumper mold and also provide milling and drilling process, if a supplier has a milling machine, a high speed drilling machine, and a high pressure coolant system.

Fig. 5. Concept diagram for reasoning manufacturing capability

In addition, the following code presents an exemplary rule about how to reason non-manufacturing capabilities with the supplier ontology. If the location of supplier is different from that of supplier's customer, supplier's customer is verified to be a foreign buyer.

( ?c isa Supplier ) ( ?c hasCustomer ?r ) (c?isLocatedAt ?p1) ( ?r isLocatedAt ?p2)(?p1 isSame ?p2) -> (?r isForeignBuyer true^^boolean)

Semantic Matching of Buyer's Requirements with Supplier's Capability Information

Semantic matching of buyer's requirements with supplier's capability information is required to solve the issue of buyers and suppliers using different level of details in their descriptions. In this framework, (1) the buyer's product requirements and the supplier's manufacturing capability, and (2) the buyer's supplier requirement and the supplier's non-manufacturing capability are semantically matched. Fig. 6 presents an exemplary concept diagram about how to match buyer's product requirements with supplier's manufacturing capability information. First, all the manufacturing capabilities of the supplier, such as bumper mold, are reasoned via the described processes. Subsequently, buyer's product requirements are matched with the supplier's manufacturing capability. In this framework, the bumper mold instance in the buyer ontology is matched with the front bumper mold instance in the supplier ontology, because they have the identical instances. Fig. 6. Concept diagram for semantic matching: the buyer's product requirements and the supplier's manufacturing capability Fig. 7 presents an exemplary concept diagram about how to match buyer's supplier requirements with supplier's non-manufacturing capability information. For example, a buyer wants to work with a 'global company.' This requirement is explicated as shown in Table 1: A supplier can be interacted in English, has overseas branch offices, has transaction experiences with foreign buyers, and can deliver products abroad. Subsequently, buyer's supplier requirements are matched with the supplier's nonmanufacturing capability. In this framework, the supplier instance is matched with the global company instance in the buyer ontology, because they have instances which have identical properties. Experiment is carefully conducted to measure the computational time of reasoning and matching by changing the number of suppliers. The results are shown in Table 2. Since suppliers are usually registered in the framework, the framework always has adequate time to execute reasoning on supplier's potential capability. Therefore, the reasoning time may be regarded as sufficiently fast. On the other hand, semantic matching of buyer's requirements with supplier's capability information is executed within a second. It proves the framework can be widely used in practice.

Conclusion

The proposed semantic-web based supplier discovery framework includes ontology building, reasoning and semantic matching. 1) A key ontology is developed to represent the buyer's requirements and the supplier's capability information.

2) Supplier's potential capability based on the input data is reasoned, since suppliers may not be fully aware of the buyer's requirements that they can potentially satisfy. 3) Buyer's requirements are semantically matched with supplier's capability information.

Up until now, supplier discovery methods have only focused on matching product requirements with the supplier's manufacturing capability at a semantic level. As the proposed framework extends to consider supplier requirements as well as product requirements, the framework facilitates the building of a long-term strategic supply chain. Also, sustainable supply chain can be built since qualified suppliers are discovered strategically.

Finally, we have implemented a prototype of the framework to test feasibility. It proves the framework can be widely used in practice. In future work, to reflect industry realities, we will extend the ontology by capturing the supplier's capability information systematically.

Fig. 3 .Fig. 4

 34 Fig. 3. Concept diagram of supplier ontology in a collaboration ontology

Fig. 4 .

 4 Fig. 4. Concept diagram of buyer ontology in a collaboration ontology

Fig. 7 .

 7 Fig. 7. Concept diagram for sematic matching: the buyer's supplier requirement and the supplier's non-manufacturing capability

Table 1

 1 describes explicit meanings for exemplary supplier requirements.

Table 1 .

 1 Explicit meanings of supplier requirements

	Supplier requirements	Explicit meanings
	A global company	A supplier can be interacted in English, has overseas
		branch offices, has transaction experiences with for-
		eign buyers, and can deliver products abroad
	A company with delivery	A supplier is located close to airport/port, has various
	competitiveness	delivery conditions, and has achieved high delivery
		performance
	A company that provides	A supplier has high market share, has transaction
	assured quality	experiences with principal customers, has received
		certifications by principal customers, has received
		quality awards

Table 2 .

 2 Computational time of reasoning and matching

	# of suppliers	Reasoning time (sec)	Matching time (sec)
	100	38.39	<0.1
	200	51.82	<0.1
	600	84.12	<0.1
	1,000	101.12	<0.1


Wonmo Kim

Timothy W Simpson

Toward Automated Design for Manufacturing Feedback

Keywords: manufacturing assessment system, design for manufacturing, manufacturing feedback

Iterative loops and rework between design, manufacturing, and testing delay the development lead time for complex products like vehicles. This research focuses on creating an automated design for manufacturing (DFM) feedback system (ADFS) framework that reduces these iterations by providing early, fast, and informative feedback on manufacturability to designers. The proposed ADFS analyzes manufacturability in terms of part geometry with respect to a given set of process capabilities based on DFM guidelines for vehicle manufacturing. In order to increase the fidelity of the search, a heuristic approach to obtain manufacturing process fitness with respect to a part design is introduced. The proposed system framework will help to identify suitable manufacturing processes more quickly as well as provide visual feedback for geometric advice at the feature level with regard to the selected processes.

Introduction

Background

Designing a complex product like a motor vehicle is often delayed due to modifications along with subsequent verifications [START_REF] Giffin | Change Propagation Analysis in Complex Technical Systems[END_REF]. These iterative loops between design and testing teams make the whole new product development linger at the design stage. It has been reported that the design process can usually take up to 24 months in North America [START_REF] Morgan | The Toyota Production Development System[END_REF] and 15 to 20 years [START_REF] Ltc Wiedenman | Adaptive Vehicle Make (AVM)[END_REF] for military vehicles particularly from the concept initiation to the production phase.

It would be desirable if these iterative loops can be shorten or ultimately, eliminated. If the manufacturing feedback information can be delivered to designers timely by adopting a stand-alone feedback system tool, then it can significantly reduce product lead time, in general, and vehicle development, in particular.

However, so far no manufacturing assessment system (MAS) has been developed that takes multiple manufacturing processes related to vehicle production into account simultaneously [START_REF] Shukor | Manufacturability analysis system: Issues and future trends[END_REF]. Adopting design for manufacturing (DFM) guidelines into a MAS can be an alternative to respond such difficulty [START_REF] Gupta | Systematic approach to analysing the manufacturability of machined parts[END_REF]. If geometric features of a design satisfy DFM guidelines of a manufacturing process, then it can be said that the design is manufacturable in terms of the corresponding manufacturing process.

Though a design may be judged as manufacturable, some potential hindrances such as visibility issues, fixturing, set-ups, etc. can still occur during the actual manufacturing execution of a design. Those details can only be investigated by the high-level assessment for a particular process. Likewise, an analysis of DFM guidelines alone cannot determine which manufacturing process is the most appropriate to consider. Therefore our goal is to provide feedback information to designers for multiple processes rather than pick a single candidate process.

Research Objective

A robust systematic framework is needed to feed-forward appropriate manufacturing processes to the next higher-level assessment as well as to provide feedback to designers based on this manufacturability assessment. It is clear that reducing iterative loops requires a stand-alone system in automated fashion to interact with designers in a timely manner. Hence, this research aims to develop an automated DFM feedback system (ADFS) framework to provide early, fast, and informative feedback for manufacturability to designers as well as feed-forward for detailed investigation. There are two main deliverables as objectives:

1. Feed-forward: the ADFS helps determine which manufacturing processes are the most appropriate for the detailed level assessment for more information. 2. Feedback: the ADFS provides geometric advice at the feature level in an intuitive and visual manner.

Literature Review

Manufacturability Assessment System

In the context of concurrent engineering, generating detailed manufacturing feedback in an interactive way is critical in MAS to reduce development time span [START_REF] Mcmains | Design for manufacturing feedback at interactive rates[END_REF]. With the widespread of CAD systems, studies regarding stand-alone manufacturability assessment tools were also spurred with the support of feature recognition methodology through CAD software [START_REF] Pham | A concurrent design system for machined parts[END_REF]. However, those researches were mostly limited to only machining process and concentrated to calculating manufacturing cost by generating adequate process plans [START_REF] Maropoulos | Assessing the manufacturability of early product designs using aggregate process models[END_REF]. According to Kalpakjian et al. [START_REF] Kalpakjian | Manufacturing engineering and technology[END_REF], rough statistical analysis of published works shows that approximately 92% of the researches are focusing on the applicability of MAS to machining processes only.

Utilizing DFM Guidelines

The main benefit of practical use of DFM guidelines is that each guideline of a manufacturing process can serve as one of several criteria, so that they can be also utilized as a source for generating redesign recommendations for failing features. As an ex-ample, Jacob et al. [START_REF] Jacob | Automated manufacturability assessment of rotational parts by grinding[END_REF] present a geometric reasoning methodology by adapting cross-comparisons of geometric features to coded design rules for grinding. When the system recognized salient features, corresponding design rules are invoked to generate manufacturing feasibility decisions automatically. Providing redesign recommendation was implemented as parametric ranges in text format verified by a knowledge base. However, the application was also bound to only single process perspective.

Proposed Methodology

Fast Heuristics

The capability of DFM guidelines enables a "Fast Heuristics" approach that can assess manufacturability without cost estimation analysis. Although calculating manufacturing cost is the most complete measure of manufacturability [START_REF] Kalpakjian | Manufacturing engineering and technology[END_REF], investigating it is not an easy task in the early stages of design. According to Özbayrak at el. [START_REF] Özbayrak | Activity-based cost estimation in a push/pull advanced manufacturing system[END_REF], estimating a manufacturing cost for a unit of product design based on activity-based costing approach requires 15+ factors to be considered. Moreover, performing cost estimation becomes worse if there are multiple candidate processes to investigate.

Process Filtering. Therefore, a heuristic filtering approach is proposed that aims to analyze manufacturability faster without losing robustness. In the system shown in Fig. 1, the part geometry extracted from submitted designs will be assessed with respect to a given set of process capabilities collected from the DFM guidelines. Here, those capabilities serve as filters that rule out infeasible manufacturing processes. A different manufacturing process must have its own filter set, as design rules for each manufacturing process are different. When a converted CAD file in XML format comes into the system, a trigger invokes a manufacturing process and its corresponding filter set from the knowledge base. Then each feature's geometry is evaluated against the appropriate filter element in a set. The output database stores "Go"&"Nogo" results of every process with respect to a design as well as information of failing features. The system loop iterates until all of the manufacturing processes are analyzed for the submitted design. Through this filtering system framework, it is ex-

Geometric Reasoning & Computation Trigger CAD Knowledge Base Decisions Outputs • Go • NoͲgo • Go ද
pected that a series of analyses toward candidate manufacturing processes delivers only suitable manufacturing processes (feed-forward) so that design advice at the feature level can be provided later according to DFM guidelines. Table 1 shows an example filtering analysis for the bracket shown in Fig. 2. For this preliminary analysis, minimum/maximum section thickness and minimum corner radii allowance are used for the filtering. The reason for the target thickness to be machined (4 in.) is because the machining process removes chunk of raw materials for both sides. For casting, the section thickness limits the width of center and bottom pillars. Also, it is presumed that forging and stamping require fixturing to position the datum B' to the ground. Processes that are grayed out at Table 1 are infeasible processes to manufacture this example part.

Fig. 2. Example bracket

Table 1. Example filtering 1: manufacturing capabilities of various processes

Process Fitness Function. The filtering process yields a series of "Go" and "No-go" responses for the manufacturing processes that were assessed. To increase the fidelity of the evaluation for the feed-forward, we propose to develop a process fitness (PF) function that is derived heuristically. If the number of processes in the knowledge base is 16, for example, then the system generates 16 stacks of response data of each process. The fitness will be derived from the statistics of all 16 responses and is presented as percentage value of "Go" proportioned to the total number of filtering attempts. Therefore, each PF provides a capability metric for each manufacturing process with respect to a given design; hence, it can be used to re-sequence filters in a set to derive the feed-forward faster. The modeling of the PF is shown in Fig. 3.

Fig. 3. PF modeling

Suppose that the knowledge base contains 16 manufacturing processes that are available. The group of manufacturing processes can be represented as:

ൌ ሼ ଵ ǡ ଶ ǡ ଷ ǡ ǥ ǡ ଵ ሽ
The number of filters N in a filter set is different among manufacturing processes. A filter of a manufacturing process can be represented as:

ൌ ൛ ଵ ǡ ଶ ǡ ଷ ǡ ǥ ǡ ൟǡ ݅ ൌ ͳ ͳǡ ݆ ൌ ͳ
Note that the number of filters is depicted as since it is depends on the ith manufacturing process. Toward any of submitted design ǡ ݇ ൌ ͳ ܯǡ performing a filtering analysis with a set of filters F of a manufacturing process bears a response that can be represented as:

୧୨ ሺ ୩ ሻ ՜ Dzdz DzǦdz
Suppose that the system performs filtering analyses for a design ଵ with a designated set of filters for process ଵ . After a loop of analyses is performed, the filtering system generates a stack of responses as described in Eq. 1.

ۏ ێ ێ ێ ۍ ଵଵ ଵଶ ଵଷ ڭ ଵ ے ۑ ۑ ۑ ې ൈ ሾ ଵ ሿ ൌ ۏ ێ ێ ێ ۍ ڭ ے ۑ ۑ ۑ ې ሺͳሻ Design • Machining • Casting • Stamping • EDM • ද Knowledge Base • Section thickness • Corner radii • Material • Tolerance • ද Filters PF Stamping
Process fitness of process with respect tothe design is derived by: ൌ ᦢᦢ ݆ when i = manufacturing process index,1 to 16.

For a group of M designs, the filtering system produces following a response matrix extended from Eq. 1 as follows:

ۏ ێ ێ ێ ۍ ଵଵ ଵଶ ଵଷ ڭ ଵ ے ۑ ۑ ۑ ې ൈ ሾ ଵ ǡ ଶ ǡ ଷ ǡ ǥ ǡ ெ ሿ ൌ ۏ ێ ێ ێ ۍ ǥ ڰ ڭ ڭ ǥ ے ۑ ۑ ۑ ې

ሺʹሻ

In this matrix, each column of the response matrix stands for the results from filtering analyses for the ݅process toward a design ெ .

Sequencing Filters. If there is an important feature property that has a small allowance range, then a filtering analysis using that feature geometry would rule out the corresponding manufacturing process frequently. Performing that analysis before any other filters would reduce the number of remaining candidate processes quicker, so that it helps to extract a process faster and increase fidelity of the framework especially for the feed-forward.

If there is enough number of sample designs, it is possible to derive a series of PF values for a process toward M number of designs from the matrix in Eq. 2. From the response matrix, analysis results of a filter toward M number of designs are the jth row of the matrix. The importance of a filter can be simply derived from the proportion of "No-go" responses to the number of all filtering attempts toward M designs as following:

ϐ ൌ ᦢǦᦢ ܯ ܯ
Instead of counting the number of positive responses, negative responses are collected because the effectiveness of filtering infeasible candidates is the matter; a filter has a higher capability if it rules out the process more often.

System Framework

The proposed methodology framework for generating automated DFM feedback is presented with two separated phases. At the first phase, collected sample designs are applied to the filtering system to derive filter sequences for manufacturing process in the knowledge base. The information flow of the first phase is presented at Fig. 4. When the sequencing is done, the filtering system will be refined by adopting the results from Phase 1 as described in Fig. 5. The PF serves as an internal metric in the ADFS. If a manufacturing process obtains 100% of the PF value, then it will be judged as manufacturable in terms of the process and can be passed forward for more detailed assessment. Else, a process with the highest PF will be selected and visual feedback on its failing features will be delivered back to the designer.

Closing Remarks

Through the proposed stand-alone ADFS, it is expected that designers can refine their designs for manufacturability from visualized feedback so that iterations can be reduced. Detail development of the visualization feedback is currently ongoing work. Additionally, there are many research items that can be extended from this research. Although the proposed ADFS is built based on filtering methodology, violating a DFM design rule (i.e., a filter) does not always mean that the design is not manufacturable. In fact, a non-manufacturable design for a selected process may become manufacturable by adding additional processes. In this research, those "superset" of multiple processes and its sequencing are currently not considered. Also, providing dynamic visual geometric recommendations at the feature level would provide richer options that enable designers to "tweak" feature parameters visually on the screen. Along with such techniques, designers can search various parameter options on specific CAD software while it does not hurt the proposed part functionality.

Evaluation of the ADFS is ongoing and will be performed through benchmarking simulation for various design inputs. Any designs revealed as manufacturable (contain

ADFS

100% PF) will be submitted for detailed evaluation in commercial software (e.g., [START_REF]aPriori, Costing without Complexity[END_REF]) so that the decision that ADFS has made can be investigated.

Fig. 1 .

 1 Fig. 1. Filtering system

Fig. 4 .

 4 Fig. 4. Phase 1: Derive sequences of filters

Fig. 5 .

 5 Fig. 5. Phase 2: Generate feed-forward/ feedback outputs

Acknowledgements. We acknowledge support provided by the Defense Advanced Research Projects Agency (DARPA/TTO) under contract HR0011-12-C-0075 iFAB Foundry. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA or the U.S. Government.


Jaehun Sim

Vittal Prabhu
email: prabhu@engr.psu.edu

The Harold

Inge Marcus

Game Theoretical Approach to Supply Chain Microfinance

Keywords:

This paper considers a supply chain microfinance model in which a manufacturer acts as a lender and a raw material supplier as a borrower. Using a game theoretical analysis, the study investigates how investment levels, raw material prices, and profit margins are influenced by loan interest rates under two types of decentralized channel policies: manufacturer Stackelberg and vertical Nash game. In addition, the study shows how the profits of a manufacturer and a supplier are changed under each supply chain channel structure.

Introduction

Over the past several years, business leaders have realized that economic growth and success can be enhanced by their efforts toward both environment and social values [START_REF] Stead | Sustainable strategic management: An evolutionary perspective[END_REF]). This central concept, corporate sustainability, has become a fundamental principle in the search for sustainable competitive advantages for companies [START_REF] Savitz | The triple bottom line: How today's best-run companies are achieving economic, social, and environmental success-and how you can too[END_REF].

For example, global leaders such as Shell, Philips, BASF, and Toyota have incorporated this new concept into their business strategies [START_REF] Ahn | Building a conducive environment for life science-based entrepreneurship and industry clusters[END_REF]).

Since the success of microfinance institutions (MFIs) in the early 1990s, an effective microfinance program can be considered a corporate sustainability approach for alleviating poverty by offering small amount loans with no collateral requirement to the poor in both developing and developed countries. With the objective of empowering the poor to become selfemployed, the microfinance program provides the poor with access to financial and social services in the form of credit opportunities, education, health care, insurance, and savings [START_REF] Sengupta | The microfinance revolution: An overview[END_REF].

Despite their flexibility, MFIs experience difficulty in increasing outreach toward their particular clientele and keeping the sustainability of the institutions at the same time. This difficulty can be attributed to several problems, including high levels of poverty and risk, information asymmetries, incentive incompatibilities, and imperfect enforcement mechanisms (Conning and Udry, 2007). Also, due to the unique features of microfinance, the interest rate is considered to be an important problem because it does not perfectly represent the lender's opportunity costs of loans nor increase the lender's profits, as in a traditional financial market.

To offer a financial sustainability solution, this study incorporates the concept of supply chain finance and proposes the concept of supply chain microfinance to examine the relationships between investment levels, raw material prices, profit margins, and loan interest rates. The basic idea of supply chain microfinance is that the high creditworthiness of a manufacturer can provide a raw material supplier with the low creditworthiness a loan at a low interest rate.

As shown in Figure 1, supply chain microfinance has three players: a bank, a manufacturer, and a supplier. It is assumed that a poor supplier does not have its own financial resources and has little chance to borrower a loan from the bank in order to invest in a project. Even if the supplier obtains a loan, his or her low credit score leads to a high interest rate.

In the supply chain microfinance environment, the high credit score of a manufacturer attracts an investment from a bank with a low interest rate.

In turn, the manufacturer lends to the poor supplier at a low interest rate.

When the supplier invests in a project with an investment M I from the manufacturer, the supplier expects a rate of return 3

The Game Model

This study considers one supplier and one manufacturer under two subproblems: manufacturer Stackelberg and vertical Nash game. The modeling approach is similar to Ghosh's approach [START_REF] Ghosh | A comparative analysis of greening policies across supply chain structures[END_REF]. In this decentralized channel policy, it is assumed that the manufacturer makes decisions on the investment level of corporate social responsibility and the profit margin. On the other hand, the supplier makes a decision on the raw material price.

Based on economic theories, a demand function is assumed to have a linear function of a market size , M manufacture price , P and the investment level of corporate social responsibility . I In addition, the product price is the sum of raw material price s and profit margin .

m The demand function is given as follows.

I P M q α β + - = (1)
In this equation, α is the demand coefficient of the corporate social re- sponsibility and β is the influence of price fluctuation on demand. It is as- sumed that the effort of the corporate social responsibility increases the demand by . I α In addition, the cost of corporate social responsibility such as fair price is assumed to be . 2 I θ Based on these assumptions, the manu- facture profit and the supplier profit are given as follows.

2

) ) ( )( ( I IR IR I m s M c s m B M M θ α β - - + + + - - - = Π (2) M P S IR IR I m s M s - + + + - = Π ) ) ( ( α β (3)
In these equations, c is a production cost, θ is the cost coefficient of corporate social responsibility, M R is an interest rate of a manufacturer, and B R is an interest rate of a bank. In the manufacturer Stackelberg, the manu- facturer decides the investment level of corporate social responsibility and the product margin in response to the supplier. Then, the supplier decides the raw material price given the investment level and profit margin. In this policy, it is assumed that the manufacturer plays a Stackelberg leader. The optimal investment level, the optimal manufacture margin, the optimal raw material price, and the optimal product price are given as follows.

M B R R c M I - + + + - = (4) αβ β β βθ θ α αβ α 2 2 2 * M B R R c M m + - - + + = (5) αβ β β βθ θ α αβ α 2 2 2 2 2 2 * M B R R c M s + - - - + - - = (6) αβ β β βθ θ α α 2 2 2 2 2 * * * M B R R M m s P - + + + = + = (7)
In the vertical Nash game, the supplier chooses the raw material price in response to the investment level and the profit margin, and the manufacturer chooses the investment level and the profit margin in response to the raw material price. In this policy, it is assumed that neither the manufacturer nor the supplier has the power to control the market. The optimal in-vestment level, the optimal profit margin, the optimal raw material price, and the optimal product price are given as follows. As shown in Figure 2, the manufacturer's interest rate has a decreasing effect on the investment level of corporate social responsibility. Conversely, the manufacturer's interest rate has an increasing effect on the profit margin.

The manufacturer's interest rate has a decreasing effect on the supplier's raw material price. Similarly, the manufacturer's interest rate has a decreasing effect on the manufacturer's product price. On the other hand, under the Nash game, the numerical analysis indicates that the interest rates of a manufacturer have little impact on corporate social responsibility, the manufacturer's profit margin, or the raw material cost.

Conclusion

This study investigates the impact of corporate social responsibility on the supply chain microfinance under two decentralized channel policies.

The result shows that the manufacturer's interest rate has a positive influence on the manufacturer's margin under manufacturer Stackelberg. In the supply chain microfinance business environment, this study shows that manufacturer Stackelberg is an appropriate model which fully incorporates the characteristics of microfinance. In the future, this study will explicitly model two features; corporate social responsibility and supply risk, to assess how the corporate decreases the supply risk and impacts the sustainable supply chain.

PRFig. 1 .

 1 Fig. 1. Basic Model of Supply Chain Microfinance

  In this study, the manufacturer Stackelberg and Nash game cases are considered to analyze the impacts of the manufacturer's interest rate. Due to the characteristics of supply chain microfinance, supplier Stackelberg is not considered because the supplier Stackelberg is not reasonable in the microfinance environment in which the manufacturer has more power than the supplier to control the market. For a numerical analysis of manufacturer Stackelberg, the following values are used, M =200, α =0.2, β =0.5, c =6, θ =3.5, M R =0.2, and B R =0.1.

Fig. 2 .

 2 Fig. 2. The Effects of Manufacturer's Interest rates