Integrated inventory ranking system for oilfield equipment industry

Ben Hmida, Jalel; Parekh, Siddharth; Lee, Jim Article Integrated inventory ranking system for oilfield equipment industry Journal of Industrial Engineering and Management (JIEM) Provided in Cooperation with: The School of Industrial, Aerospace and Audiovisual Engineering of Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC) Suggested Citation: Ben Hmida, Jalel; Parekh, Siddharth; Lee, Jim (2014) : Integrated inventory ranking system for oilfield equipment industry, Journal of Industrial Engineering and Management (JIEM), ISSN 2013-0953, OmniaScience, Barcelona, Vol. 7, Iss. 1, pp. 115-136, https://doi.org/10.3926/jiem.511 This Version is available at: https://hdl.handle.net/10419/188594 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/3.0/ Journal of Industrial Engineering and Management JIEM, 2014 – 7(1): 115-136 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 http://dx.doi.org/10.3926/jiem.511 Integrated Inventory Ranking System for Oilfield Equipment Industry Jalel Ben Hmida, Siddharth Parekh, Jim Lee University of Louisiana at Lafayette (United States) j [email protected], [email protected], [email protected] Received: July 2012 Accepted: January 2014 Abstract: Purpose: This case study is motivated by the subcontracting problem in an oilfield equipment and service company where the management needs to decide which parts to manufacture inhouse and which parts to purchase from suppliers when the capacity is not enough to make all required parts. A higher level quality can be achieved for the parts manufactured in house and the lead time can also be well controlled. Currently the company is making subcontracting decisions based on management’s experience. Design/methodology/approach: Working with the management, a Decision Support System (DSS) is developed to rank parts by integrating three inventory classification methods considering two quantitative factors including cost and demand, and one qualitative factor based on management experience. The proposed integrated inventory ranking procedure will make use of three classification methods: ABC based on cost, FSN based on demand, and VED based on management experience. Findings: An integration mechanism using weights is developed to rank the parts based on the total priority scores. The ranked list generated by the system helps management to identify the most critical parts to manufacture in-house. Originality/value: The integration of all three inventory classification techniques, covering both quantitative and qualitative factors, into a single system is a unique feature of this research. -115- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 is important as it provides a more inclusive, big picture view of the DSS for management’s use in making business decisions. Keywords: inventory management, inventory classification methods, ABC, FSN, VED, subcontracting, decision support system 1. Introduction Inventory management is important for the successful operation of most industries due to the amount of money inventory represents. Due to the variation in product demand, the industry may not be able to manufacture all the necessary parts on time. In this case, subcontracting becomes vital in order to avoid shortage and to maintain continuity in the manufacturing process. Subcontracting is defined as “a situation where the firm offering the subcontract requests another independent enterprise to undertake the production or carry out the processing of a material, component, part or subassembly for it according to specifications or plans provided by the firm offering the subcontract” (Holmes, 1986). When the available capacity is not enough, a decision has to be made about what parts should be manufactured in-house and what parts should be subcontracted. The growing pressures of time-based global competition have led to the recognition of subcontracting as an important contributor to the competitive advantage of the firm (Kumar & Wilson, 2009). Manufacturing industries typically deal with thousands of parts and many suppliers. Extensive research has been done on the selection of suppliers or subcontractors (Krause, Handfield & Scannell, 1998; Carr & Pearson, 1999; Ferrer, 2003; Li, Murat & Huang, 2009) and supplier uncertainty (Petrovic, Petrovic & Rajat, 1998; Hillier 2002; Yang, Ronald & Chu, 2005; Kim, Chatfield, Harrison & Hayya, 2006; Mohebbi & Hao, 2006). However, in-depth research in identifying which parts to manufacture in-house or subcontract is not common. Several approaches have been used in the industries for make-or-buy decisions. The first approach is to manufacture the parts with early due dates and subcontract the ones that would not be possible to manufacture within the time frame. The problem with this approach is that the productivity of the plant decreases because different setups are used for different parts. Another approach that is being followed in the industries is to manufacture the parts that have similar setups to the part that is currently being manufactured, and subcontract the rest. Even though this approach increases the productivity of the plant, the inventory levels may go up (Atamtürk & Hochbaum, 2001). -116- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 The third approach is to prioritize the inventory by using general inventory classification methods. Inventory classification is a crucial element in the operation of any production company. Because of the huge number of inventory items in many companies, great attention is directed to inventory classification into the different classes, which consequently require the application of different management tools and policies (Chase, Jacobs, Aquilano & Agarwal, 2006). ABC analysis is one of the most commonly employed inventory classification techniques. Conventional ABC classification was developed for use by General Electric during the 1950s. The classification scheme is based on the Pareto principle, or the 80/20 rule, that employs the following rule of thumb: ‘‘vital few and trivial many.” The process of ABC analysis classifies inventory items into A, B, or C categories based on so-called annual dollar usage. Annual dollar usage is calculated by multiplying the dollar value per unit by the annual usage rate (Cohen & Ernst, 1988; Partovi & Anandarajan, 2002). Inventory items are then arranged according to the descending order of their annual dollar usage. Class A items are relatively small in number, but account for the greatest amount of annual dollar usage. In contrast, class C items are relatively large in number, but make up a rather small amount of annual dollar usage. Items between classes A and C are categorized as class B. Chen, Li and Liu (2008) examines several multiple criteria ABC analysis procedures which have been criticized for its exclusive focus on dollar usage. Other criteria such as lead-time, commonality, obsolescence, durability, inventory cost, and order size requirements have also been recognized as critical for inventory classification (Flores & Whybark, 1987; Jamshidi & Jain, 2008; Ng, 2007; Ramanathan, 2006). In order to accommodate multi-criteria inventory classification, many researchers have proposed methods that consider factors other than annual dollar usage. Flores and Whybark (1987) developed a cross-tabulation matrix method for use in bi-criteria inventory classification; they found that the method becomes increasingly complicated when three or more criteria are involved in evaluations. Flores, Olson and Dorai (1992) have proposed the use of joint criteria matrix for two criteria. Analytic hierarchy process (AHP) developed by Saaty (1980) has been successfully applied to multi-criteria inventory classification by Flores et al. (1992). The advantage of the AHP is that it can incorporate many criteria and ease of use on a massive accounting and measurement system, but its shortcoming is that a significant amount of subjectivity is involved in pairwise comparisons of criteria. They have used the AHP to reduce multiple criteria to one consistent measure. Hadi-Vencheh (2010) proposed a simple nonlinear programming model, which determines a common set of weights for all the items. Yu (2011) compared artificial-intelligence (AI)-based classification techniques with traditional multiple discriminant analysis (MDA). Kabir and Hasin (2012) developed an improved multi-criteria inventory classification model using Fuzzy Analytic hierarchy process (FAHP) approach. Although the FAHP approach proved to be a convenient method in tackling practical multi-criteria decision making problems an -117- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 improvement could be done in the determination of the weights of each component to handle uncertainties in the decision making. Larson (1980) proposed a method FSN to classify parts as fast-moving (F), slow-moving (S) or non-moving (N) based on demand. Mukhopadhyay, Pathak and Guddu (2003) proposed VED method to classify parts as Vital (V), Essential (E), or Desired (D). The integration of ABC with other factors has been considered by several studies. ABC is integrated with a mixed integer program (MIP) in Kirche & Srivastava (2005) for order management. Bhattachaya, Sarkar and Mukherjee (2007) developed a procedure based on ABC and perishability of items. Hincapie, Lee and Emblom (2011) developed a decision support system which ranks component parts by integrating multi-criteria classification methods. The decision is to manufacture the parts that have the highest priority. Nahmias and Demmy (1981) modeled a system which maintains inventory to meet both high priority and low priority demands. They evaluated the control structure such that various methods of comparing refill rates may be developed both when rationing of the reorder point, quantity, and support level is and is not in effect. Among the three general classification methods, ABC focuses on cost and FSN focuses on demand. Both ABC and FSN are quantitative methods and do not take into account the perceived importance of the part in the eyes of the manager. VED allows the management to assign parts priority subjectively, but it does not fully utilize the available inventory data. The problem of these classification methods is that no one considers the integration of both quantitative (ABC and FSN) and qualitative factors (VED) in one model. An integrated approach needs to be developed that can rank the inventory based on multiple criteria, taking into account both quantitative and qualitative factors. Once this new approach is developed, a Decision Support System (DSS) can be implemented to rank the inventory. A DSS is defined as a class of information systems that support decision-making activities (Holsapple & Whinston, 1996). Various DSS’s have been developed for inventory management. Sadrian and Yoon (1994) developed a Procurement Decision Support System (PDSS) to improve the procurement practices of a company. Ronen & Trietsch (1988) developed a DSS for purchasing components and materials for large projects taking into account lateness penalties. Walker (2000) developed of a decision support tool for the singleperiod inventory problem. DSS using simulation (Bed & Nagarur, 1994), stochastic sampling (Jeong, Leon & Villaboros, 2007), and fuzzy logic (Lan, Ding & Hong, 2005; Zeydan & Colpan, 2009) have also been developed to deal with various manufacturing applications, but none of these systems addresses the problem of ranking parts for subcontracting or manufacturing inhouse decisions using both quantitative and qualitative factors. This case study is motivated by the subcontracting problem in an oilfield equipment and service industry. Currently the company is making subcontracting decisions based on management’s experience without using any data, and discussion with the management indicates that both quantitative and qualitative factors are important. Our objective of this case study is to 1) develop an integrated inventory ranking procedure that takes into account both quantitative factors such as -118- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 cost and demand, and qualitative factors such as functionality, efficiency, and manufacturing expertise or quality; and 2) develop a DSS to implement the integrated inventory ranking procedure and produce a ranked list to help the management in make-or-buy decisions. The proposed integrated inventory ranking procedure will make use of three classification methods: ABC, FSN, and VED. Based on our discussion with the management and review of existing inventory data, we believe that the integration of the three methods will provide useful information to make subcontracting decision. At the same time, our DSS is designed in a modular fashion which can be modified to add more classification methods. Section 2 of this paper provides the logic we follow to implement each of the three inventory classification methods. Section 3 presents the integration mechanism and the theory foundation of our integrated procedure. The development of a DSS to implement our procedure is discussed in section 4. In section 5, the case study is performed using real data from the oilfield equipment and service industry. Using the DSS, the company developed a ranked list of about 50 critical parts to be manufacture in-house. Our concluding remarks are given in section 6. 2. Inventory classification methods The proposed integrated inventory ranking procedure uses three classification methods: ABC, FSN, and VED. The ABC and FSN methods consider quantitative factors such as cost and demand, and the VED focuses on qualitative factors such as functionality, efficiency or quality. All the qualitative factors have cost implications. When the cost is difficult to estimate, VED allows management to subjectively assign a class based on past experience. An illustrative example of the application of each of these classification schemes in our research is given in the sections below. 2.1. ABC classification The ABC classification ranks the parts based on their dollar usage value in a given period. The high value parts (about 20%) are classified as A, the middle value parts (about 30%) are classified as B, and the lower value parts are classified as C. The procedure used in our study to perform the classification entails the following steps: 1. Compute quantity and dollar usage of each item from inventory data. Dollar usage of each item = Quantity * Unit Cost 2. Arrange parts in descending order based upon the dollar usage values. -119- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 3. Compute % of dollar value for each item. % dollar value = (dollar usage value / total dollar usage value) * 100 4. Compute % quantity for each item. % quantity = (Quantity / Total quantity) * 100 5. Compute the cumulative percentages. 6. Classify the top 20% of the parts as A, the next 30% of the parts as B and the remaining parts as C. The cutoffs for ABC in the procedure above are used as a general rule and the management may differ in the way they classify the inventory. So, the cutoff % for classification should be based on user input. For example, in Table 1 part#1 (6.45%) is classified as A, part#2 (32%) classified as B and part#3 (64.52%) as C. Part# Quantity used Unit cost Total cost %Cost Cumulative %cost %Quantity Cumulative %Quantity Class 1 10 200000 2000000 71.05% 71.05% 6.45% 6.45% A 2 40 10000 400000 14.21% 85.26% 25.81% 32.26% B 3 50 7000 350000 12.43% 97.69% 32.26% 64.52% C 4 20 1500 30000 1.07% 98.76% 12.90% 77.42% C 5 35 1000 35000 1.24% 100.00% 22.58% 100.00% C Table 1. Example of ABC classification 2.2. FSN classification With FSN, the parts having the highest demand (top 25%) are classified as fast moving and the parts having the least demand (bottom 25%) are classified as non-moving. The middle 50% of the parts are classified as slow moving. The FSN procedure used in our study to perform the classification involves the following steps: 1. Obtain the total demand for each part in a given period. 2. Arrange the parts in ascending order based on total demand. 3. Compute the first and third quartiles (Q1 and Q3) from demand data. 4. Classify the parts using the following logic: •If total demand > Q3 then Classify as Fast Moving •If total demand < Q1 then Classify as Non-Moving •Otherwise, Classify as Slow Moving -120- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 In this classification, Q1 and Q3 are used as default values for the cutoffs. The final decision on the cutoffs should be based on management. An example of a FSN classification is shown in Table 2. Here we see that the fast moving parts are those parts that have a total demand over 600, the slow moving parts are those parts having a total demand in between 400 and 600, and the non-moving parts are the ones with a total demand below 400. Part# Total demand Class 1 50 N 2 400 S 3 500 S 4 600 S 5 1700 F Table 2. Example of FSN classification 2.3. VED classification VED is based on the criticality of parts judged by the management where parts are classified as Vital, Essential, or Desirable. It can also be used for other aspects of decision making such as the due dates and setup times. This is a subjective decision made by the management and varies from part to part. In general, an item is classified as Vital in any of the following circumstances: •Functionality: If the non-availability of the item shuts down the process completely and there is no standby unit as a spare. •Efficiency: If the non-availability of the item completely reduces the efficiency of the manufacturing process. •Quality: If the item is unique and/or the company involved is a world-class manufacturer of the item. An item is classified as Essential in any of the following circumstances: •Functionality: If the non-availability of the item shuts down the process but a standby unit exists. •Efficiency: If the non-availability of the item reduces the efficiency of the process. •Quality: If the quality of the item manufactured in-house is slightly better than purchased item. An item is classified as Desirable in any of the following circumstances: •Functionality: If non-availability of the item does not affect the operation of the manufacturing process -121- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 •Efficiency: If non-availability of the item does not significantly affect the efficiency of the process. •Quality: If the quality of the item manufactured in-house is no better than purchased item. Once the three classifications are done, a method for combining them is needed. The different methods of combining the classifications along with their analyses are discussed in the next section. 3. Integrated inventory ranking procedure In ranking the importance of parts, several factors must be considered. When three classification methods are integrated, it is possible that the management feels that the levels of importance of the three methods are different. Weights can then be assigned to the methods and they may change from one industry to the other. The integrated mechanism that this DSS introduces lets the user (i.e., management) decide what factors are of importance and in what order. So, if qualitative factors need to have higher priority than quantitative factors, higher weights need to be assigned to VED compared to ABC and FSN. Providing the management an option to choose which factor is most important in his/her scenario is a key design objective of our DSS. A good DSS also needs to have the flexibility and capability to answer “what if” questions. While the factors considered in the classification methods are not mutually independent, the three classification methods may assign different priority scores to the same part, resulting in three different ranked lists. For example, if the part with the highest demand is the most expensive part, then both FSN and ABC will assign the part with high priority. If the cost to make the part is relatively low but demand is high, then this part may not receive high priority based on ABC. A part with low demand and low cost may also be classified as “Vital” under VED if the quality of the part manufactured in-house is significantly better than purchased from subcontractor. The notation that would be followed to indicate the priority levels for ABC, FSN, and VED classifications respectively is [ABC, FSN, VED]. If we consider three priority levels for the purpose of integration, then a total of 27 different combinations (33) are possible which can be generalized into 3 categories: •Identical priority levels for all classifications (e.g., [1,1,1]) •Mixed priority levels for all classifications (e.g., [1,1,2]) •Distinct priority levels for all classifications (e.g., [2,1,3]) -122- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 The reorder table consists of both the parts that can be manufactured and the parts that need to be purchased. Several times, all the manufactured parts that need to be reordered cannot be manufactured in time to meet the demand and these parts will then need to be subcontracted. A decision needs to be made about what parts in this reorder list should be manufactured in-house and what parts should be subcontracted. For IRSIM to function, the input that is given should consist of the day-to-day transactions that take place in terms of demand. Some historical data are shown in Table 8. It consists of information such as the record number pertaining to a transaction, the part number, its description, the date and amount of demand, the price of each part and also information about the employee that placed the order. Once the data file is converted and stored in the transaction table of IRSIM, the user can then use IRSIM to perform the analysis. Part # Description Drawing # Quantity in Stock Reorder Point Reorder Quantity 75007 LUG, BACKING-7 5/8", 10 3/4", 13 3/8", 21" CASING TONG 75007 12 25 100 75012 HOUSING, MUB-5206 BEARING-7 5/8", 10 3/4", & 14" TONG 75012 19 25 100 75014 STUD, CAM-14" TONG 75014 250 300 500 75015 ROLLER, CAM-14" TONG 75015 272 300 500 75035 PIN, JAW HINGE-7 5/8, 10 ¾ & 14" TONG 75035 21 30 70 75036 PIN, JAW ROLLER-4 1/2" & 5 1/2" - 10 3/4" TONG CT-10-M012 5 15 25 75037 ROLLER, JAW-13 3/8" & 13 3/8" HT & 10 3/4" CT-10-M015 4 25 50 75065 ROLLER, CAM-14" TONG 75065 22 150 300 75158 ROLLER, DOOR-7 5/8" TONG 75158 4 6 10 75191 PIN, SLIP HINGE; BJ 175 TON OR B+V 250 MT 75191 2 8 10 75195 PIN, YOKE-175, 350 & 500 TON B J TOOLS 75195 18 30 60 75200 ROD, LOCK FOR BJ 350 & 175 75200 0 10 20 75206 PLUNGER, OVERLOAD FOR BJ TOOLS MISC. 190 26 45 100 75220 ROD, LOCK-500 TON BJ 75220 1 3 6 75318 STUD, CAM-36" TONG / 21" CASING TONG 75318 29 50 100 75319 ROLLER, CAM-36" TONG / 21" CASING TONG 75319 45 50 100 75557 BODY, TONG DOOR INTERLOCK VALVE 75557 17 25 50 75558 SPOOL, TONG DOOR INTERLOCK VALVE 75558 17 25 50 75561 HOUSING, A-35 TONG UNLOADER VALVE MISC. 425 11 25 50 75562 POPPET, A-35 TONG UNLOADER VALVE MISC. 411 4 25 50 75563 SEAT, A-35 TONG UNLOADER VALVE MISC. 412 5 25 50 Table 7. Sample reorder table -129- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 Record# Part# Description Employee Department Quantity Price 68496 60246 INSERT, CASING SLIP - STRAIGHT TOOTH 7400 HAWKJAW 1 $39.50 68498 30034 BOLT, U – NC 6392 MANUF USE 4 $0.65 68499 45301 O-RING, BUNA N-90A 7440 PIPE & FAB 12 $8.25 68501 77179 PIN, MASTER CYLINDER FRAMEMOUNTING COMPONENT 6165 CAM 2 $6.52 68502 45303 O-RING, BUNA N-70A 7440 PIPE & FAB 12 $8.11 68505 45989 O-RING, BUNA N-90A 5394 MANUF USE 4 $0.03 68507 45761 FITTING, TBG ADAPTER-NPT X TUBE X 90 DEGREE 5394 MANUF USE 12 $3.44 68508 46059 FITTING, ADAPTER-NPT RED BUSH-FLUSH 5394 MANUF USE 4 $0.96 68509 45996 FITTING, ADAPTER-NPT X TUBE X 45 DEGREE 5394 MANUF USE 4 $5.44 68510 45936 FITTING, ADAPTER-NPT X NPTF X 90 DEGREE 5394 MANUF USE 6 $1.35 68511 45528 FITTING, TBG ADAPTER-NPT X TUBE X 90 DEGREE 5394 MANUF USE 4 $6.77 68512 45615 FITTING, ADAPTER-NPT X TUBE 5394 MANUF USE 4 $1.51 68513 45937 FITTING, ADAPTER-NPT X NPTF X NPTF X TEE 5394 MANUF USE 2 $4.80 Table 8. Transaction table The steps that were followed to use IRSIM for performing analysis are as follows: 1) Select Parts – By clicking on the Select Parts function on the main menu of IRSIM, the transaction table was selected for parts having part numbers ranging between 75000 and 80000. This selection automatically created the summary table and performed default classifications for ABC and FSN analysis. 2) Perform Classifications a) VED Classification – Selection of the file and range led to the parts information page, where information about VED Classification was entered for each part. After entering information about the VED classification, the Perform ABC and FSN Classification function was chosen. b) ABC Classification – The ABC classification was performed by taking the % of A parts as 20% and % of B parts as 30% with the rest of the parts classified as C. The result of this classification was as follows: ▪Class A → 16.89% of the inventory accounts for 66.33% of the value ▪Class B → 22.12% of the inventory accounts for 8.47% of the value ▪Class C → 60.99% of the inventory accounts for 25.2% of the value -130- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 c) FSN Classification –The FSN classification was performed by taking the % of F parts as 25% and % of S parts as 50% with the rest of the parts classified as N. The result of this classification was as follows: ▪Fast moving parts have a demand greater than or equal to 46 ▪Slow moving parts have a demand of less then 46 and greater than 5 ▪Non moving parts have a demand less than or equal to 5 3) Assign Weights – Once all the classifications were performed, they were assigned weights using the Assign Weights function on the main menu of IRSIM. The management of the oil equipment company believes that the costly parts should be made in-house, so ABC is given the highest weights at (10, 5, 1). The FSN is considered as more important than the VED, so a (5, 3, 1) is used for FSN and (3, 2,1) is used for VED. 4) Generate Reorder Lists – To generate the reorder lists, the Reorder Lists function was selected from the main menu. A sample output is displayed in Figure 3 with the respective classifications and the priority scores. Figure 3. Sample output report -131- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 The parts are arranged in descending order of their priority scores. A ranked list is given in Table 9. Ranking Part # Part Description 1 75557 Body, Tong Door Interlock Valve 2 75014 Stud, Cam – 14” Tong 3 75065 Roller, Cam – 14” Tong 4 75012 Housing, Mub – 5206 Bearing -7 5/8”, 10 ¾” & 14” Tong 5 75561 Housing, A-35 Tong Unloader Valve 6 75558 Spool. Tong Door Interlock Valve 7 75200 Rod, Lock for BJ 350 & 175 8 75220 Rod, Lock – 500 Ton BJ 9 75037 Roller Jaw – 13 3/8” & 13 3/8” HT & 10 ¾” 10 75035 Pin, Jaw Hnge – 7 5/8”, 10 ¾” & 14” Tong 11 75195 Pin, Yoke – 175, 350 & 500 Ton BJ Tools 12 75318 Stud, Cam – 36” Tong / 12” Casino Tong 13 75015 Roller, Cam – 14” Tong 14 75319 Roller, Cam – 36” Tong / 12” Casino Tong 15 75191 Pin, Slip Hinge; BJ 175 Ton or B & V 250 MT 16 75007 Lug, Backing – 7 5/8”, 10 ¾”, 13 3/8”, 21” Casino Tong 17 75562 Poppet, A-35 Tong Unloader Valve 18 75206 Plunger, Overload For BJ Tools 19 75158 Roller, Door – 7 5/8” Tong 20 75563 Seat, A-35 Tong Unloader Valve 21 75036 Pin, Jaw Roller – 4 ½” & 5 ½” – 10 ¾” Tong Table 9. Ranking table Our system suggests that those parts with higher priority scores are those which would be of more importance to the company and therefore should be produced in-house so as to have better control of it based on the production capacities and due dates. While those with lower priority scores are of lesser importance and may be considered for outsourcing within the allowable order constraints. 6. Conclusions The case study is performed to develop an integrated inventory ranking procedure to determine make-or-buy decisions on the parts of an oilfield equipment company. A DSS called IRSIM is created to implement the procedure using MS Access database software. Three different inventory classification techniques including ABC classification, FSN classification, and VED classification are integrated into IRSIM to combine both quantitative factors and qualitative factors to generate a ranked list. -132- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.511 The integration of all three inventory classification techniques into a single system is a unique feature of this research. This is important as it provides a more inclusive, big picture view of the DSS for management’s use in making business decisions. The inventory ranking depends heavily on the weights given to the three classification methods. The VED ranking should have the most weight if vital parts can be easily identified. If the shortage of a part will shut down the entire process, then it should be given the highest priority over value and demand. The part’s cost, or ABC classification, should have the second rank in the weight system. Lastly, the FSN classification can be used as a tie-breaker for highcost, vital parts in the process. Our DSS is tested and verified by utilizing real world data, and the company has produced a list of parts to be made in-house. While we feel that the VED ranking should have the most weight, the management of the oil equipment company believes that the costly parts should be made in-house, so ABC is given the highest weights. The decision reveals that the management may not feel very comfortable about their VED assignment. Other industries will generate different rankings if different priorities are used, indicating the need for a flexible DSS. This is very common in DSS applications. Future enhancements to this research can be done in two areas. The inventory ranking procedure is based on three quantitative and qualitative factors as suggested by the company in our case study. Future research can consider other classification methods and other factors such as due dates and similar setups. The integration mechanism can also be enhanced to implement procedure to assign weights for the other factors. The IRSIM developed in this case study focuses on the make-or-buy decisions for one industry. The scope of IRSIM can be further extended to deal with other inventory management problem areas such as reorder strategies. References Atamtürk, A., & Hochbaum, D.S. (2001). Capacity acquisition, subcontracting, and lot sizing. 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