Development of a QFD-based expert system for CNC turning centre selection econstor Make Your Publications Visible. A Service of zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics Prasad, Kanika; Chakraborty, Shankar Article Development of a QFD-based expert system for CNC turning centre selection Journal of Industrial Engineering International Provided in Cooperation with: Islamic Azad University (IAU), Tehran Suggested Citation: Prasad, Kanika; Chakraborty, Shankar (2015) : Development of a QFD- based expert system for CNC turning centre selection, Journal of Industrial Engineering International, ISSN 2251-712X, Springer, Heidelberg, Vol. 11, pp. 575-594, http://dx.doi.org/10.1007/s40092-015-0122-x This Version is available at: http://hdl.handle.net/10419/157461 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. http://creativecommons.org/licenses/by/4.0/ www.econstor.eu ORIGINAL RESEARCH Development of a QFD-based expert system for CNC turning centre selection Kanika Prasad1 • Shankar Chakraborty1 Received: 9 March 2015 / Accepted: 17 August 2015 / Published online: 20 October 2015 � The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Computer numerical control (CNC) machine tools are automated devices capable of generating com- plicated and intricate product shapes in shorter time. Selection of the best CNC machine tool is a critical, complex and time-consuming task due to availability of a wide range of alternatives and conflicting nature of several evaluation criteria. Although, the past researchers had attempted to select the appropriate machining centres using different knowledge-based systems, mathematical models and multi-criteria decision-making methods, none of those approaches has given due importance to the voice of cus- tomers. The aforesaid limitation can be overcome using quality function deployment (QFD) technique, which is a systematic approach for integrating customers’ needs and designing the product to meet those needs first time and every time. In this paper, the adopted QFD-based methodology helps in selecting CNC turning centres for a manufacturing organization, providing due importance to the voice of customers to meet their requirements. An expert system based on QFD technique is developed in Visual BASIC 6.0 to automate the CNC turning centre selection procedure for different production plans. Three illustrative examples are demonstrated to explain the real- time applicability of the developed expert system. Keywords CNC turning centre � Expert system � Multi- criteria decision-making � Quality function deployment Introduction Machine tools have been around since the industrial rev- olution, extensively used to manufacture parts/components of machines, which is a process of selectively removing material to create a desired shape. They are capable of producing parts/components of different shapes and sizes, having simple to complex contours. These days’ products are becoming much more complex, and difficult to design and manufacture. Hence, the manufacturing organizations are forced to develop and adopt new technologies to avoid long design and machining time for complex products. So, machine tools have been gradually evolved out over the past few decades to meet the increasing demand of man- ufacturing complicated components with high degree of accuracy in large volume. The computer numerical control (CNC) machine tools are now being extensively applied for automated machining operations to help in achieving faster production rate with decreased human involvement and effort. Development of CNC machine tools is a great contribution to the manufacturing domain as automation of the machining process with flexibility to handle small to medium batch quantities in part production now becomes possible. This CNC technology can be applied to milling, turning, grinding, boring, drilling machines, flame cutters, etc. The CNC as the name suggests is equipped with computers that help in organizing and restoring informa- tion to attain high accuracy and speed in part production. Its basic aim is to achieve the desired objectives of the manufacturing organizations within the limited available budget. In an industrial setting, CNC machine tools can be combined into entire cells of tooling machines that can operate independently of each other. They are often driven by completely digital designs, which eliminate the need for design blueprints to be physically drawn up. All the CNC & Shankar Chakraborty s_chakraborty00@yahoo.co.in Kanika Prasad pdkanika@gmail.com 1 Department of Production Engineering, Jadavpur University, Kolkata, West Bengal 700 032, India 123 J Ind Eng Int (2015) 11:575–594 DOI 10.1007/s40092-015-0122-x http://crossmark.crossref.org/dialog/?doi=10.1007/s40092-015-0122-x&domain=pdf http://crossmark.crossref.org/dialog/?doi=10.1007/s40092-015-0122-x&domain=pdf machines are able to accurately control motions in multiple directions, and hence, can generate complex contours and shapes on the workpieces with higher dimensional accu- racy and precision. Many of them are capable of running for several days without human intervention. These auto- mated features of CNC machine tools make it possible to produce thousands of identical parts/components with minimal supervision, and allow the operators to perform other tasks thus saving a lot of time. Besides this, they can also produce parts/components with a level of accuracy that can be nearly impossible to attain using the older tools. This improved accuracy can help eliminate waste due to production of less defective parts. Features, like automatic tool and job change, etc. substantially reduce the machin- ing time, thereby trimming down the total production cost at the end. The CNC machine tools, capable of performing repetitive complicated and unsafe machining operations, become highly productive and cost effective, thus gaining wide acceptance in manufacturing industries. A manufac- turing organizations’ productivity is directly related to the proper choice of its CNC machine tools as their proper use can increase the overall production while effectively uti- lizing the available resources and reducing the chance of human injury. Although CNC machine tools are highly productive and flexible, they are also quite expensive to procure, install and maintain. However, their ability to enhance productivity can easily offset the huge initial investment if they are properly evaluated and selected. Hence, it is an important decision for the production planners to choose the most appropriate CNC machine tool among various available alternatives to fulfil the organi- zational requirements. The CNC machine tool selection process is focussed on fulfilment of two basic requirements, i.e. (a) boundary (fixed) conditions, and (b) performance expectations (de- sired results). For a CNC machine tool, the boundary conditions include spatial constraints, range of spindle speed, etc. whereas, performance expectations comprise positional accuracy, repeatability, capability to generate complicated parts, etc. Once these conditions and expec- tations are identified and prioritized, the most appropriate CNC machine tool that meets the basic application requirements can be easily selected. Choosing the best suited CNC machine tool from a wide range of similar alternatives is a complex and time-consuming task as it involves consideration of a large number of qualitative and quantitative factors, such as capital cost, table area, three axes movement, power, spindle speed range, machining diameter and length, tool capacity, flexibility, safety, etc. which are sometimes interrelated to each other. Technical brochures can effectively convey various machine features/ specifications and some level of technical performance data, but they do not provide true comparisons with the other competing machines. So, lack of accurate informa- tion is another problem faced by the production planners while selecting a CNC machine tool for a specific appli- cation. The production planners also need to analyse huge amount of raw data consisting of many interrelated factors for proper and effective evaluation of available CNC machine tool alternatives, which involves human expertise in a particular domain. But, human expertise is scarce, and it may not be always possible to analyse a large amount of data and crucial details of a problem. Moreover, it has limited working memory, and hence, cannot comprehend the data quickly. These shortcomings can be overcome while developing a database containing the technical specifications of various CNC machine tools, which can be updated from time to time. The database can then be integrated with an expert system to ease out and automate the CNC machine tool selection process. A selection pro- cedure is not a simple task, but rather a sequence of interdependent activities that must take into consideration the customers’ requirements, manufacturing economics, design expectations and above all, human safety. Thus, there is a need for a systematic and rational approach that can aid in solving the CNC machine tool selection prob- lem, avoiding human intervention and expertise. In this paper, an expert system based on quality function deployment (QFD) technique is developed in Visual BASIC 6.0 to automate the CNC turning centre selection procedure for three different production plans, i.e. flexible, mass and tailor made (customer specific). The database containing the technical specifications of more than 200 CNC turning centres is developed in MS-Access. The QFD technique is augmented to systematically integrate the needs of customers with various engineering or technical characteristics and derive the priority weights for different technical requirements while evaluating the feasible CNC turning centres. Three numerical examples are illustrated to demonstrate the applicability of the developed QFD-based expert system for CNC turning centre selection. The rest of the paper is organized as follows. A literature review on the past researches is provided in Sect. 2 and QFD methodology is explained in Sect. 3. Section 4 describes the development procedure of QFD-based expert system for CNC turning centre selection. Three illustrative examples are provided in Sect. 5, and in Sect. 6, final conclusions are drawn. Literature review in machine tool selection Till date, many studies have been reported in the literature on solving machine tool selection problems using diverse multi-criteria decision-making (MCDM) methods, mathe- matical models and knowledge-based systems. Sun (2002) 576 J Ind Eng Int (2015) 11:575–594 123 applied data envelopment analysis (DEA) to assess 21 CNC machines with respect to their technical and cost factors. Sensitivity analysis with variable variation and weight restrictions identified six CNC lathes as ‘good buys’ to be subsequently recommended for further consideration. Yurdakul (2004) presented a new strategic justification tool employing analytic hierarchy process (AHP) and analytic network process (ANP) which were applied to calculate the contribution of machine tool alternatives to manufacturing strategy and rank them based on the developed hierarchical structures. Ayağ and Özdemir (2006) introduced triangular fuzzy numbers in pair-wise comparison of the AHP matrix to overcome the vagueness and uncertainties in judgments of the decision makers in the conventional AHP method while evaluating machine tool alternatives. Ayağ (2007) integrated AHP method with simulation technique to determine the best machine tool satisfying the needs and expectations of a manufacturing organization. The AHP method was adopted to narrow down the list of feasible machine tool alternatives and a simulation generator was then used to automatically model the manufacturing organization. Durán and Aguilo (2008) developed a fuzzy AHP-based software for evaluation and justification of machine tools. Önüt et al. (2008) proposed a combined fuzzy AHP and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) for machine tool selection, while incorporating triangular fuzzy numbers in the traditional AHP and TOPSIS methods. Dağdeviren (2008) integrated AHP and preference ranking organization method for enrichment evaluation (PROMETHEE) for equipment selection. The AHP method was applied to analyse the structure of the selection problem and deter- mine the criteria weights, whereas, PROMETHEE method was employed to obtain the final ranking of the alterna- tives. Yurdakul and İç (2009) discussed about the benefits of using fuzzy numbers instead of crisp numbers in a TOPSIS method-based machine tool selection model. İç and Yurdakul (2009) developed a decision support system (DSS) using extended versions of MCDM approaches, like fuzzy AHP and fuzzy TOPSIS to help the decision makers in machining centre selection decisions. Qi (2010) devel- oped a comprehensive evaluation model for machine tool selection and applied fuzzy integral approach for aggre- gation of performance scores of the alternatives with respect to different criteria. Ayağ and Özdemir (2011) proposed an intelligent approach for machine tool selection using fuzzy ANP to consider the vagueness and uncertainty existing in the importance attributed to judgment of the decision maker. Özgen et al. (2011) presented a combined application of modified DELPHI method, AHP and PRO- METHEE approaches with fuzzy set theory for solving machine tool selection problems. İç (2012) applied an integrated TOPSIS and design of experiments approach to solve a CNC machine tool selection problem in a real-time industrial environment. İç et al. (2012) developed a com- ponent-based machining centre selection model based on AHP, which would use only the technical specifications while evaluating the machining centre components. Ilangkumaran et al. (2012) developed an evaluation model based on AHP and VIKOR (VlseKriterijumska Opti- mizacija I Kompromisno Resenje) methods under fuzzy environment for selection of the best machine tool among various alternatives. Taha and Rostam (2012) developed a DSS to select the best alternative machine tool using a hybrid approach of fuzzy AHP and PROMETHEE meth- ods. Ayağ and Özdemir (2012) applied ANP together with the modified TOPSIS method for performance analysis on machine tools. Furthermore, a fuzzy ANP approach was adopted to deal with the imprecise and uncertain human comparison judgments. Samvedi et al. (2012) integrated fuzzy AHP and grey relational analysis (GRA) approaches for selection of a machine tool from a given set of candi- date alternatives. Fuzzy AHP was applied to calculate the criteria weights followed by GRA method to rank the alternatives. Aghdaie et al. (2013) applied step-wise weight assessment ratio analysis (SWARA) and complex propor- tional assessment of alternatives with grey relations (COPRAS-G) methods for machine tool evaluation and selection. Dawal et al. (2013) presented a simple approach for multi-attribute-based selection of machine tools using fuzzy AHP and TOPSIS methods. Tho et al. (2013) inte- grated intuitionistic fuzzy entropy and TOPSIS method to deal with the vague information in the decision-making process for machine tool selection. Nguyen et al. (2014) presented a hybrid approach integrating fuzzy ANP and COPRAS-G methods for evaluating machine tools with consideration of interactions between the considered attri- butes. Xin et al. (2014) proposed an optimal machine tool selection approach based on interval-valued fuzzy C-means clustering algorithm. Sahu et al. (2015) applied VIKOR method for determining a compromise ranking list of five alternative CNC machine tools while considering 21 sub- jective evaluation criteria. A comparative study between various MCDM tech- niques that had been used in the past and the developed QFD-based model to solve the machine tool selection problems is provided in Table 1. It can be observed from the literature that the earlier MCDM methods as applied for machine tool selection are unable to take into account the voice of customers in the evaluation process. But, the customers’ requirements have an immense importance in the present-day manufacturing scenario where there is an enormous competition to capture every single percentage of market share available, which is primarily driven by an organization’s ability to satisfy its customers. Further, it is also realized that till date, no J Ind Eng Int (2015) 11:575–594 577 123 attempt has been made to interrelate the technical requirements of various machine tools with the corre- sponding customers’ requirements. Moreover, the MCDM methods that had been previously applied to select machine tools have one or more inherent draw- backs. Like, in AHP and ANP methods, huge mathe- matical calculations are involved, and if there is any ambiguity or uncertainty in the pair-wise comparison matrix, the reliability of the derived solutions is itself questionable. Similarly, TOPSIS method introduces two reference points, i.e. positive ideal and negative ideal solutions, but it does not consider the relative importance of the distances of the alternatives from those two points. It signifies that the selected alternative may not always be the best solution (i.e. closest to the ideal solution). PROMETHEE method is based on some preference functions and in most of the real-time situations, the decision maker faces a problem in selecting the most appropriate preference function. VIKOR method is more time consuming as the final decision is to be compro- mised taking into consideration two other factors as acceptable advantage and acceptable stability of the decision. On the other hand, DEA comprises of too many lengthy computations and cannot often be solved manually. The decision maker needs to have some soft skills in computer programming for performing such calculations. These drawbacks of MCDM methods pre- viously adopted for machine tools selection can be effectively addressed while developing a QFD-based expert system, which can not only incorporate the cus- tomers’ requirements into the selection process but also interrelate them with the technical requirements. It is also supposed to be superior to other MCDM methods on its ability to deal with the dynamic nature of the decision-making problems. Hence, in this paper, a user- friendly software prototype with graphical interface in Visual BASIC 6.0 is developed to help the production planners in selecting the best CNC turning centre to meet the dynamic requirements of a specific production system and accomplish the managerial benefits of an automated selection procedure. QFD methodology In this era of global competition, the success of any organization depends on its ability to understand and meet the ever changing needs of the customers. QFD method- ology is now being recognized as an efficient technique to deal with the voice of customers which includes the cus- tomers’ needs for a product, customers’ perceptions on the relative importance of those needs, and the relative per- formance of the manufacturing organization and its main competitors on those needs. QFD basically consists of two components, i.e. quality and function, which are deployed in the design process. The quality deployment brings cus- tomer’s voice into the design phase, whereas, function deployment links different organizational functions and technical requirements in the design to manufacturing. It is a focused methodology for carefully listening to the voice of customers, and then effectively responding to those needs and expectations, thereby attaining the highest cus- tomer satisfaction. It is employed to translate the cus- tomers’ requirements, in terms of engineering or technical Table 1 Comparative study between different MCDM methods used for machine tool selection Method Flexibility Operational approach Computational time Complexity Decision maker’s involvement Compensatory nature Type of data AHP High Pair-wise comparison Very high Very high Very high Yes Ordinal ANP High Pair-wise comparison Very high Very high Very high Yes Ordinal PROMETHEE Moderate Pair-wise comparison High High Very high No Mixed GRA High Grey system Moderate Moderate Moderate Yes Mixed DEA Very low Efficiency measurement Very high High No No Cardinal TOPSIS Low Euclidean distance Moderate Moderate Moderate No Cardinal VIKOR Low Euclidean distance High Moderate Moderate No Cardinal QFD-based expert system High Pair-wise comparison Very low Moderate Low Yes Mixed 578 J Ind Eng Int (2015) 11:575–594 123 characteristics, that can be deployed through product planning, process planning, service design and part development. This quality improvement tool was developed in late 1960s in Japan by Akao who is regarded as the father of QFD and was first implemented at the Mitsubishi Heavy Industries Kobe Shipyard in 1972 under the guidance of Shigeru Mizuno and Yasushi Furukawa (Akao 1990). Ford Motor Company, Toyota, Procter and Gamble, Mitsubishi, Campbell’s soup, Hewlett-Packard, Kodak, IBM, Xerox and 3M Corporation were among the early adopters of QFD methodology. Chan and Wu (2002) reviewed the implementation of QFD technique in different organiza- tions, such as shipbuilding, automobiles, electronics, soft- ware, banking and accounting, health care, education and research, retail outlets, apartment layouts, airline services, office equipment, consumer products, financial services, telephone services, gas and electrical services, distribution networks, traffic management, food industry, fishing industry, chocolate industry, online gaming, hot bar sol- dering, etc. It is observed that the above-mentioned appli- cations are for product and process development, but lately, QFD has also been applied for selection of suppliers (Bevilacquaa et al. 2006; Bhattacharya et al. 2010; Shad et al. 2014), non-traditional machining processes (Chak- roborty and Dey 2007), industrial robots (Karsak 2008), product design and development (Liu 2011; Soota et al. 2011) and materials (Mayyas et al. 2011; Prasad and Chakraborty 2013). QFD is such a systematic, robust and practical quality improvement tool that apart from the above-cited domains, it has also been applied in some unconventional fields, like game of soccer (Partovi and Corredoira 2002). QFD understands how the customers or users become interested and satisfied with the end products. Customers’ requirements and their relationships with the design char- acteristics are the driving force behind QFD methodology (Dursan and Karsak 2013). It can be used to translate subjective quality criteria into objective ones that can be quantified and measured. It is a complimentary method for determining how and where priorities are to be assigned in product or process development, and intelligently links the needs of the customers with the design and development of a product. There are three basic steps in implementing QFD methodology. At first, the spoken and unspoken wants or needs of the customers are prioritized. In the next step, those needs are translated into technical characteris- tics and specifications, and in the final step, a quality product or service is developed and delivered focusing everybody towards customer satisfaction. QFD methodology can be adopted to process both qualitative and quantitative data. Its main merit over the other MCDM approaches is that it provides flexibility to the decision makers to correlate both customer needs and engineering metrics through assigning scores and weights to them, and at the same time, it defines the direction of improvement for each metric which may be directly or inversely proportional to each other (Mayyas et al. 2011). QFD for a product is developed through brainstorming of a team, which comprises of six to eight persons, consisting of representatives from various cross-functional departments, like marketing, design, production, quality assurance, testing, purchasing, vendor, etc. The representatives from those cross-functional departments are collectively known as the QFD team, as shown in Fig. 1. The main advantage of this cross-functional group decision-making approach is that it takes opinions from the representatives of various departments and incorporates them into the product, lead- ing to a better quality product and higher customer satis- faction. This also avoids any biasness and partiality in the decision-making process. QFD employs a matrix format to capture a number of issues vital for the planning process. According to Dai and Blackhurst (2012), the overall process of QFD is based on its core matrix framework, called house of quality (HoQ), which is used to intertwine customers’ needs, service design or management requirements, target design goals and competitive product or service evaluations (Sharma and Rawani 2008). Although, HoQ is the primary tool in QFD method, the statements or demands of the customers may not always be clear or comprehensible, therefore, some other tools are also required which can interpret and explain the voice of customers clearly. Thus, along with HoQ, seven other management and planning tools are used to identify and prioritize customers’ expectations quickly Marketing/ Sales Quality assurance Design Purchase Vendor Production Testing QFD team Fig. 1 A QFD team J Ind Eng Int (2015) 11:575–594 579 123 and effectively. Figure 2 presents those seven management and planning tools, while the basic structure of HoQ matrix is shown in Fig. 3. HoQ translates the customers’ requirements, based on marketing research and benchmarking data, into an appropriate number of engi- neering targets to be met by a new product design. Basi- cally, HoQ is the nerve centre and the engine that drives the entire QFD process (Raissi et al. 2012). The procedural steps involved in the development of HoQ matrix are described as below: Step I In the first step, various market segments are determined and subsequently analysed to identify the potential customers. The QFD team then conducts customer surveys to accumulate the relevant information about customers’ requirements or expectations from the product/service. The seven management and quality tools, i.e. affinity diagrams, tree diagrams, relations diagrams, matrices and tables, process decision program charts, AHP, and blueprinting are employed to analyse and categorise those information as primary, sec- ondary and tertiary. The customers’ require- ments are then prioritized based on customers’ choice and its relative importance to them using a 1–5 rating scale, where 1 having the least priority and 5 having the maximum priority. These customers’ Affinity diagrams Matrices and tables Relations diagrams AHP Process decision program chart Hierarchy trees Blueprinting Management and planning tools Fig. 2 Management and planning tools C us to m er s’ r eq ui re m en ts Technical requirements Primary Secondary T er tia ry S ec on da ry Tertiary P rim ar y Interrelationship matrix Technical correlation matrix Planning matrix Prioritized technical requirements Fig. 3 House of quality matrix 580 J Ind Eng Int (2015) 11:575–594 123 requirements are placed on the left wall of HoQ matrix along the rows. Step II In this step, the key performance indicators in an organization to achieve customer satisfac- tion are recognized. Additionally, regulatory standards and technical requirements dictated by the management are also identified. Fur- ther, these technical requirements are orga- nized as primary, secondary and tertiary using different management and quality tools, and are arranged at the top of HoQ matrix along the columns. Step III The interrelationship matrix, which shows the relationship between customers’ requirements and technical requirements is now established and positioned at the centre of HoQ matrix. The relationship between pairs of customers’ requirement and technical requirement is portrayed using symbols or numbers, termed as correlation index, as shown in Table 2. Step IV The performance measures in the existing designs usually conflict with each other. The technical correlation matrix, which is more often referred to as roof matrix, shows the relationship between various technical requirements. This roof matrix also helps in establishing the interrelationship matrix, and identifies where the technical requirements must work together to avoid any design conflict. Step V Once the roof matrix is constructed, the planning matrix measuring the performance of the organization with respect to its bench- marked competitive organization is devel- oped. The performance of the organization is then ranked on a scale of 1–5, 1 being the least satisfying and 5 being the excellent performance. This matrix is set on the right side of the interrelationship matrix. Step VI Finally, the priorities assigned to the technical requirements are recorded and compared to those of the benchmarked competitor accord- ing to the relative weight of each relationship. Then, these values are linked back to the customers’ requirements to meet the new design requirements and are positioned at the bottom of HoQ matrix as prioritized technical requirements. Development of a QFD-based expert system for CNC turning centre selection The developed QFD-based expert system relates the dynamic requirements of the customers with technical specifications of CNC turning centres and then selects the most suitable machine based on the considered evaluation criteria. The basic framework for design and development of the QFD- based expert system is exhibited in Fig. 4. It has five basic modules, e.g. recognition of customers’ requirements, iden- tification of technical requirements, creation of the database, development of the expert system and evaluation of the alternatives to select the best CNC turning centre. Based on a market survey using questionnaires and customers’ feedback, the wants and needs of the customers related to CNC turning centres are first accumulated. These customers’ voices are then prioritized using different management and planning tools. The 12 most important customers’ requirements associated with the selection of CNC turning centres are detailed out as follows: (a) Allocated fund—It is associated with the initial acquisition cost and investment needed to procure and set up a CNC turning centre in a manufacturing organization. It also includes the expenditure made on installation of the said machine tool. An organi- zation’s objective is always to reduce the allocated fund and keep it as minimum as possible. (b) Availability of space—It is related to the total space occupied by a CNC turning centre with respect to its length, width and height dimensions. There are always some spatial constraints within the shop floor because of which machine tools having smaller overall dimensions are always preferred. (c) Capacity—This characteristic of a CNC turning centre deals with the maximum dimension, i.e. length, diameter and weight of the workpiece that can be machined. It is always better to have a CNC machining centre with higher capacity. (d) Productivity—It can be defined as the volume of workpieces machined by a CNC turning centre per Table 2 Correlation index for interrelationship matrix Number Symbol Relation 1 Very weak relation 3 Weak relation 5 Moderate relation 7 Strong relation 9 Very strong relation J Ind Eng Int (2015) 11:575–594 581 123 unit time. As productivity is directly proportional to a manufacturing organization’s profit, it is always preferred to have highly productive CNC turning centres. (e) Machining time—It is the time taken by a CNC turning centre to machine a workpiece having the desired dimensions and shape. Less machining time reduces the lead time to market a product. So, a CNC turning centre which has less machining time is more beneficial for a manufacturing organization. (f) Power requirement—It deals with the power rating of a CNC turning centre, i.e. amount of power required to operate it. It is always beneficial to have machines which consume less power. (g) Flexibility—Flexibility of a CNC turning centre relates to its ability to deal with the changing part configurations to allow variations in part assembly and process sequence, and changes in production volume and product design. A manufacturing orga- nization must be proficient to produce reasonably priced customized parts/components of higher dimensional accuracy that can be quickly delivered to the customers. (h) Ease of machine tool handling—The CNC machine tool handling and changing task should be easy as it requires frequent human intervention. Easy machine tool handling makes it simple to respond to any change in the production plan. (i) Auxiliary attachments—These are the additional attachments provided with CNC turning centres to enhance their overall machining performance. For example, guards are added to increase operator safety, automatic tool and job changers are often provided to reduce human interference and total machining time, and turret may be equipped with multiple tool holders so that a range of different machining operations can be performed simultaneously. (j) Surface finish—It relates to the dimensional accu- racy and smoothness of the workpiece surface machined by a CNC turning centre. This is also crucial from the aesthetics viewpoint of the product. QFD team discussion table Quality assurance Design Vendor Purchase Production Testing Marketing/ Sales QFD-BASED EXPERT SYSTEMWebsite Database Collection of data Identification of technical requireemnts Obtain customers’ requirements Retrieval of alternatives Evaluation of alternatives Best CNC turning centre Customers Decision maker Fig. 4 Basic framework for development of QFD-based expert system 582 J Ind Eng Int (2015) 11:575–594 123 (k) Generation of complicated parts—The CNC turning centres are able to accurately control motions in multiple directions, and hence, can generate complex shape contours on the workpieces with higher precision and accuracy. Therefore, a CNC turning centre which can easily machine complicated part geometries has an added advantage over the others. (l) Adaptability—It is associated with a CNC turning centre’s ability to adapt to new machining condi- tions. The CNC machines having this feature are favoured over the others to satisfy diverse customer requirements. Next, an expert panel comprising of members from various departments of a manufacturing organization, like purchasing, production, testing, quality assurance, mar- keting, design, etc. is interviewed to obtain their opinions regarding the technical specifications of CNC turning centres. After critically analysing those specifications, 13 technical requirements are finally shortlisted based on their impact on the selection procedure, as enlisted below: (a) Area (mm 2 )—This specification of a CNC turning centre denotes the total space occupied by it on the shop floor. It is an important consideration in case of any spatial constraint within the shop floor. (b) Cost—It relates to the capital investment required to procure and install a CNC turning centre. A CNC turning centre having less initial capital investment is always preferred to that requiring higher capital outflow. (c) Height (mm)—It is one of the dimensions of a CNC turning centre. It actually measures the spread of the machine in vertical direction. (d) Maximum length (mm)—It refers to the maximum length of a workpiece that can be accommodated and machined on a CNC turning centre. This specifica- tion is related to the capacity of a CNC turning centre. (e) Maximum diameter (mm)—It is related to the maximum diameter of a workpiece that can be machined on a CNC turning centre. Like maximum length, it is also related to the capacity of the machine. (f) Maximum spindle speed (min -1 )—Rotations of the spindle of a CNC turning centre per minute is its spindle speed. It can be correlated to overall productivity and attainable surface finish. (g) Travel X-axis (mm)—It signifies the maximum length that the tool can move in X-direction. (h) Travel Z-axis (mm)—It indicates the maximum length that the tool can travel in Z-direction. (i) Rapid traverse X-axis (m/min)—It expresses the movement of the tool turret at the fastest rate in X- axis, requiring only an end point for this movement. (j) Rapid traverse Z-axis (m/min)—Similarly, rapid traverse Z-axis is the movement of the tool turret at the fastest rate in Z-axis direction. (k) Spindle motor power (kW)—It is the power rating of a CNC turning centre. It is directly related to the consumption of electrical power while running the machine. (l) Number of tools—It is the maximum number of tools that can be accommodated in a tool holding device of a CNC turning centre. More number of tools facilitates in generating complicated shapes on the workpieces and also reduces the total machining time. (m) Weight (kg)—It is another specification of a CNC turning centre indicating its overall weight. Machine weight is quite critical when a load limit exists on the shop floor. Among all these technical specifications, cost of a CNC turning centre is expressed using a qualitative scale of 1–9. The actual range of cost (in USD) along with its scale values and interpretations is given in Table 3. The detailed information and relevant data regarding the technical specifications of various CNC turning centres are accumulated from the brochures of different manufacturers available online. Then, these collected data for CNC turning centres are stored into MS-Access option of Visual BASIC 6.0. An exhaustive database containing technical specifica- tions of more than 200 CNC turning centres is thus created. The next stage involves in the development of the QFD- based expert system, which can be broadly divided into two phases. At first, the related HoQ matrix is constructed and the feasible alternatives satisfying the set criteria values are extracted from the database. The HoQ matrix developed here is a simplified one where technical correlation and Table 3 Scale indicating range of cost for CNC turning centres Cost (in USD) Scale Interpretation 25,000–30,000 1 Lowest 30,001–45,000 2 Very very low 45,001–60,000 3 Very low 60,001–70,000 4 Low 70,001–85,000 5 Medium 85,001–105,000 6 High 105,001–130,000 7 Very high 130,001–155,000 8 Very very high 155,001–180,000 9 Highest J Ind Eng Int (2015) 11:575–594 583 123 planning matrices are not considered. Only the prioritized technical requirements are taken into account at the bottom of HoQ matrix. The customers’ requirements and technical requirements are already identified, and they are placed on the left wall of HoQ matrix along the rows and at the top of HoQ matrix along the columns, respectively. Once the customers’ requirements and technical requirements are placed in HoQ matrix, the corresponding interrelationship matrix is developed. Three production plans, i.e. ‘Flexible Production’, ‘Mass Production’ and ‘Custom’ are provided to the production planners to choose the kind of interrelationship matrix to be developed to accommodate the dynamic demands of the customers. Flexibility is the measure of how fast a setup can convert its process(es) from making an old line of products to a new set of items. A flexible production plan is often required to permit low cost switching from one product line to another. This type of production plan can efficiently produce highly customized and unique products in varying volumes while utilizing CNC technology. Its use can reduce the need for human intervention and provide an infrastructure which can react quickly to deviations in the production plan. Consistent and better quality products, with lower cost can be produced using the same manpower while adopting a flexible production plan. While, mass production is the manufacturing of large volumes of stan- dardized products, making many copies of the products very quickly, using assembly line techniques. It is often characterized by mechanization to achieve high volume output, efficient flow of materials through various stages of manufacturing, careful supervision of quality standards and minute division of labour. As there is a continuous flow of materials, there is no queuing at any stage of the production process. Supervision is also easy in case of mass produc- tion because only few instructions are necessary. If any of the first two options is chosen, i.e. ‘Flexible Production’ and ‘Mass Production’, an automatically filled up interrelationship matrix with default values appears. On the other hand, if ‘Custom’ option is selected, the end user needs to fill up the interrelationship matrix based on the subjective judgments. The customers’ requirements can be either beneficial (higher the better) or non-beneficial (lower the better), and are attributed by the value of the corre- sponding improvement driver (?1 for beneficial criteria and -1 for non-beneficial criteria). The next stage com- prises of assigning priority weights to the requirements of the customers. For assigning priority values to customers’ requirements, a scale of 1–5 is set, where 1—not important, 2—important, 3—much more important, 4—very impor- tant and 5—most important. After critically analyzing the relationship between customers’ requirements and techni- cal specifications of CNC turning centres, it is observed that productivity is highly correlated to spindle speed, motor power and rapid traverse speed; whereas, area, height and weight of CNC turning centre have the least relationship with it. Similarly, it is also found that flexi- bility is strongly related to spindle speed and number of tools, while availability of space is highly interrelated with area and height. Furthermore, it is revealed that the allo- cated fund is greatly associated with cost of the CNC turning centre; whereas, machining time is strongly related to spindle speed, and rapid traverse in X- and Z-axes. Moreover, it is also noticed that power requirement is positively related to spindle motor power, but it is least influenced by maximum length and diameter of the work- piece, and travel in X- and Z-axes. Additionally, it is observed that number of tools considerably influences the capability of a CNC turning centre to generate complicated parts. The interrelationships between the remaining cus- tomers’ requirements and technical requirements are sub- sequently developed similarly with values from an appropriate scale of 1–9, where 1—very very weak, 2— very weak 3—weaker, 4—weak, 5—moderate, 6—strong, 7—stronger, 8—very strong and 9—very very strong. Once the HoQ matrix is filled up with all the necessary data, the ‘Weight’ functional key is pressed to obtain the priority weights of all the technical requirements, using the following equation: wj ¼ Xn i ¼ 1 Pri � IDi � correlation index ð1Þ where wj is the weight for jth technical requirement, n is the number of customers’ requirements, IDi is the value of improvement driver for ith customer requirement, Pri is the priority assigned to ith customer requirement and correla- tion index is the relative importance of jth technical requirement with respect to ith customer requirement. These weights are subsequently used for calculation of the performance scores of the feasible CNC turning centres. The second phase of QFD-based expert system embarks with identifying the most important technical requirements based on which the end user wants to evaluate the candi- date CNC turning centres. Once the desirable technical requirements are shortlisted by the end user, the range for each selected technical requirement needs to be specified. Depending on the given ranges of values for the shortlisted technical requirements, a list of feasible CNC turning centres is then extracted. The final stage of the expert system consists of evalu- ating the feasible candidate alternatives to select the best CNC turning centre. In this stage, a final set of CNC turning centres that needs to be evaluated based on the set criteria is chosen from the list of feasible alternatives. A decision matrix comprising of the selected technical 584 J Ind Eng Int (2015) 11:575–594 123 specifications with respect to each chosen CNC turning centre is then developed. This decision matrix now needs to be normalized to make it dimensionless so that the performance of all the alternatives can be compared with respect to the set criteria. The following linear normaliza- tion procedure is adopted here. For beneficial criteria (technical requirements for which weights are positive) Normalized value ¼ Property value � Smallest value Highest value � Smallest value ð2Þ For non-beneficial criteria (technical requirements for which weights are negative) Normalized value ¼ Smallest value � Property value Highest value � Smallest value þ 1 ð3Þ The performance score for each CNC turning centre is now computed using the following equation: Performance score PSið Þ ¼ Xn j¼1 wj � Normalized valueð Þij i ¼ 1; 2; . . .; m; j ¼ 1; 2; . . .; nð Þ ð4Þ where m is the number of alternatives and n is the number of technical requirements. The weights for the selected technical requirements are automatically retrieved from the HoQ matrix. Based on these performance scores, the fea- sible CNC turning centres are ranked and a graphical representation showing the performance score of each CNC turning centre is automatically generated. The best per- forming CNC turning centre is finally identified and its retailed technical specifications are displayed along with its actual photograph. Figure 5 exhibits a flowchart for the developed QFD- based expert system to help the end user to navigate through it properly. The procedural steps for running this software prototype are enlisted as below: Step I An opening window containing the guidelines to be followed by the end user for running the QFD-based expert system appears at first. Step II Once the guidelines are understood, the end user selects an option from the type of production plans and presses the ‘Next’ key. A HoQ matrix depending on the type of the production plan appears in a new window. Step III The customers’ requirements are identified as beneficial or non-beneficial while assign- ing appropriate improvement driver values. Step IV A priority value between 1 and 5 is assigned to each customer requirement. Step V Based on the type of production plan, an interrelationship matrix, either filled up or blank, appears. If it is blank, it needs to be filled up with necessary values, else proceed to the next step. Step VI The ‘Weight’ functional key is pressed to obtain the priority weights of all the technical requirements in the HoQ matrix. Step VII A set of evaluation criteria is chosen from the list of available technical requirements to finalize the selection decision. Step VIII The ‘Input Range’ functional key is pressed to generate empty cells to capture ranges of the selected criteria values within which the spec- ifications of CNC turning centres should lie. Step IX All the feasible alternatives satisfying the given ranges of specifications are extracted by pressing ‘Feasible Alternatives’ key. Step X The end user then shortlists the final set of alternatives to be evaluated. Step XI The ‘Next’ key is pressed to automatically develop the corresponding decision matrix with the technical specifications of the finally selected alternatives with respect to the set evaluation criteria in a new window. Step XII The performance scores and ranks of the finally selected alternatives are computed after pressing the ‘Calculate Rank’ key. Step XIII The ‘Rank Analysis’ key is pressed to graph- ically display the performance score of each alternative. Step XIV The ‘Machine Details’ key is pressed to display the detailed technical specifications of the most appropriate CNC turning centre along with its actual photograph. The interrelationship matrices for flexible and mass production plans are slightly different from each other. For example, presence of auxiliary attachments in a CNC turning centre makes it more flexible. This affects the overall cost of the machine more strongly in flexible pro- duction than mass production. In flexible production plan, the correlation indices between flexibility and all other technical requirements are high due to greater impact of these requirements on flexibility. Moreover, a large volume of finished products is required in mass production and hence, if the number of tools is more in a CNC turning centre, its productivity will also be high. Therefore, there exists a stronger relationship between productivity and number of tools in mass production than flexible J Ind Eng Int (2015) 11:575–594 585 123 Start the selection process Run the application Go through the guidelines in opening window Select the type of production plan from ‘Type of production system’ module and press ‘Next’ key Assign improvement driver to each customer’s requirement Enter priority values for customers’ requirements Press ‘Weight’ key to obtain the weights of technical requirements Choose the selection criteria from the list of technical requirements Press ‘Input Range’ key and enter ranges for the selected criteria Fill up the interrelationship matrix as per the guidelines Is interrelationship matrix is filled? Press ‘Feasible Alternatives’ key to list all the feasible alternatives Choose final set of alternatives after comparing all the feasible alternatives Press ‘Next’ key to develop decision matrix with technical specifications of the finally selected alternatives Press ‘Calculate Rank’ key to compute performance scores and ranks of the alternatives Press ‘Rank Analysis’ key to display the graph with performance scores Press ‘Machine Details’ key to get details of the best alternative with a photograph End of the selection process Yes No Fig. 5 Flowchart of the QFD-based expert system 586 J Ind Eng Int (2015) 11:575–594 123 production. Similarly, the capacity of a CNC turning centre for mass production should be such that it can accommo- date more number of tools so that several machining operations can be performed simultaneously. Hence, capacity of the machine and number of tools are more strongly related in mass production than flexible production. Illustrative examples An Intel � Core TM i5-2034M CPU with 2.50 GHz, 4.00 GB RAM operating platform is required to run this QFD-based expert system. Three illustrative examples are provided to demonstrate its applicability. Figure 6 displays the instruction sheet, which first appears on the screen when this software prototype is run to assist the end user to get acquainted with it. Example 1 In this example, the end user desires to select an appro- priate CNC turning centre for a manufacturing system which is subjected to change in the type and volume of parts produced. A flexible production plan can only offer a variety of parts/products with rapidly changing production level as demanded by the customers. Taking this feature of flexible production into account, ‘Flexible Production’ option is chosen from ‘Type of Production System’ mod- ule. On pressing the ‘Next’ key, the corresponding HoQ matrix with the filled up interrelationship matrix according to flexible production plan now appears in a new window, as shown in Fig. 7. The improvement driver and priority values for each customer’s requirement are then entered in their respective columns. The improvement driver value of -1 for ‘Allocated fund’, ‘Availability of space’, ‘Ma- chining time’, ‘Power requirement’ and ‘Surface finish’ reveal that they are the non-beneficial attributes requiring minimum values. A priority value of 5 assigned to ‘Allo- cated fund’, ‘Availability of space’ and ‘Flexibility’ sig- nifies that these customers’ requirements have the highest importance. On the other hand, a priority value of 1 for ‘Ease of machine tool handling’ and ‘Auxiliary attach- ments’ shows that they are not so much important to the customers in this case. The ‘Weight’ key is then pressed to derive the priority weight of each technical requirement present in the HoQ matrix. Next, depending on the end requirements of the final product, cost, maximum diameter, maximum length, spindle motor power and weight are shortlisted to finalize the CNC turning centre selection decision. In the HoQ matrix, among the five criteria chosen for evaluation of CNC turning centres, negative priority weights for cost, spindle motor power and weight identify Fig. 6 Opening window having guidelines for the end user J Ind Eng Int (2015) 11:575–594 587 123 them as non-beneficial technical requirements. Now, pressing of the ‘Input Range’ key generates the necessary empty cells, where the ranges of values for the selected technical requirements are entered. Then, the functional key ‘Feasible Alternatives’ is then pressed to display a list of feasible CNC turning centres satisfying all the set cri- teria values. A final set of 12 alternatives to be evaluated is chosen from this list of feasible alternatives for ultimate selection of the most appropriate CNC turning centre for the given application. In Fig. 8, the final decision matrix for the CNC turning centre selection problem is developed from the database on pressing the ‘Next’ functional key. Pressing of the ‘Cal- culate Rank’ key then normalizes the decision matrix, computes the performance scores and displays the ranking preorder of the considered CNC turning centres. Addi- tionally, their relative performances are graphically dis- played when the end user presses the ‘Rank Analysis’ button. Finally, the ‘Machine Details’ key is pressed to retrieve the detailed technical specifications and an actual photograph of the best suited CNC turning centre. Here, model ST20 manufactured by Haas Automation Inc. is identified as the best CNC turning centre, followed by GENOS L 300E-MY. On the other hand, LOC-500 is indicated as the least preferable choice for the given application. It can be noticed from the specifications of model ST20 that it can simultaneously satisfy all the shortlisted technical requirements, i.e. its cost, weight and spindle motor power are low; and maximum diameter and maximum length are high. Example 2 In this example, a CNC turning centre for producing a large volume of standardized parts/components needs to be selected from a wide range of available alternatives. Mass production is the suitable approach for producing parts in large volume at low unit cost. Keeping this in mind, the end user selects ‘Mass Production’ option from ‘Type of Pro- duction System’ module. A HoQ matrix with already filled up interrelationship data for mass production plan now appears, as shown in Fig. 9. Similar to the earlier example, after identifying the beneficial and non-beneficial attri- butes, and entering the priority for each customer’s requirement, the ‘Weight’ button is pressed to obtain the priority weights for the technical requirements. A priority Fig. 7 HoQ matrix for flexible production plan 588 J Ind Eng Int (2015) 11:575–594 123 value of 5 assigned to ‘Allocated fund’ shows that it is the most important criterion for the customers. ‘Productivity’ and ‘Generation of complicated parts’ are also important for this particular CNC turning centre selection problem and hence, a priority value of 4 is assigned to them. In this case, ‘Availability of space’ and ‘Power requirement’ is not the constraints for the customers; therefore, they are allotted a priority value of 1 signifying their least impor- tance. Four technical requirements, i.e. cost, rapid traverse X-axis, travel X-axis and maximum spindle speed are identified here as the governing requirements. A negative priority weight of cost implies that it is a non-beneficial criterion, whereas, for the other three criteria, it is always better to have their higher values. Then, the ranges of values for these selected criteria are entered in their respective cells and the functional key ‘Feasible Alterna- tives’ is pressed to retrieve all the feasible alternatives fulfilling the set criteria values. From the list of feasible alternatives, the end user selects a set of nine alternatives for final assessment. The decision matrix for this problem with all the selected technical specifications for the finally chosen alternatives is shown in Fig. 10. In the next step, their performance scores and ranking preorder are determined after pressing the ‘Calculate Rank’ key. MACTURN 350-W manufactured by Okuma Corporation is identified as the most appropriate choice for this application, while LB-35II (M) 850-C emerges out as the least suitable CNC turning centre under the given conditions. To retrieve all the technical details of MACTURN 350-W along with its photograph, the ‘Machine Details’ key is then pressed. A close review of the details of MACTURN 350-W reveals that although its cost is slightly high (85001-105000 USD), but it has comparatively higher values for the other three technical parameters, i.e. maximum spindle speed, rapid traverse X-axis and travel X-axis. Hence, its selection can be justified with respect to the considered criteria. Example 3 This example illustrates the selection of a CNC turning centre when the end user has the flexibility to fill up the interrelationship matrix based on the association between customers’ requirements and technical requirements. After selecting the ‘Custom’ option, when the end user presses the ‘Next’ key, a blank HoQ matrix is automatically Fig. 8 Output of the expert system for flexible production plan J Ind Eng Int (2015) 11:575–594 589 123 generated. After identifying the appropriate beneficial and non-beneficial attributes, ?1 or -1 value is assigned to them. For this example, ‘Allocated fund’ and ‘Capacity’ are the most important customers’ requirements, having a priority value of 5 assigned to them. ‘Availability of space’ and ‘Productivity’ have been assigned a priority value of 4. On the other hand, a priority value of 1 implying least importance to the customers is allotted to ‘Ease of machine tool handling’ and ‘Adaptability’. Figure 11 exhibits the HoQ matrix filled up based on the opinions and judgments of the end user showing the relationship between cus- tomers’ requirements and technical requirements. The weight of each technical requirement is then derived. The end user now shortlists area, cost, height, maximum diameter and maximum spindle speed from the technical requirements list, based on which the CNC turning centres are to be evaluated. In the next step, the ‘Feasible Alter- natives’ key is pressed to obtain a list of candidate alter- natives satisfying all the set criteria values. A decision matrix containing the selected technical specifications for the final set of alternatives is developed in a new window, as displayed in Fig. 12, when the user presses the ‘Next’ key. This QFD-based expert system identifies MACTURN 250 manufactured by Okuma Corp. as the best CNC turning centre, whereas, GENOS L 300-W is the least suitable choice for the given application. It is observed that the performance scores of MACTURN 250 and MACTURN 550-W are almost same. A closer look at the technical specifications of these two machines reveals that the former has lower values for area, cost and height which are the non-beneficial attributes. The maximum spindle speed for MACTURN 250 is also higher, which is a beneficial criterion. Therefore, its selection as the best turning centre is justified. Real-time implementation of the developed expert system A specific machine shop of a manufacturing organization is considered here for real-time application of the developed expert system. The identity of this organization is not disclosed for confidentiality and anonymity purpose, and hereafter, it is referred to as XYZ Limited. It is noticed that the said organization used to practice the conventional (manual) technique for selection of CNC turning centres for various machining applications. This manual approach of short listing the appropriate CNC turning centres for a specific application is often a costly and time-consuming task due to various activities involved, like creating and receiving records, record keeping and maintenance, retrieving data for comparison and identification of a suitable CNC turning centre, and disaster prevention and recovery planning of vital records. Those activities require scarce resources, like manpower, office space, operating Fig. 9 HoQ matrix for mass production plan 590 J Ind Eng Int (2015) 11:575–594 123 Fig. 10 Output of the expert system for mass production plan Fig. 11 HoQ matrix for custom option plan J Ind Eng Int (2015) 11:575–594 591 123 supplies, etc. But, it is observed that once this expert sys- tem is implemented in the said organization for the selec- tion of CNC turning centre, many of the activities utilized earlier in the manual method are either removed from the selection process or required in meagre amount. This implies that with the implementation of the developed expert system, there is a considerable reduction in man- power and time for upkeepng of records, and smaller office space is required due to comparatively less number of documents. Additionally, in the current automated envi- ronment, fewer operating supplies are consumed because of computerization of the selection process. A cost-benefit analysis is carried out to find out the monetary benefits of implementing this expert system in the considered orga- nization, which is estimated to be in the range of 3600–4000 USD per month. This derived cost-benefit can be attributed to savings under various cost heads due to automation of the selection procedure, like 3000–3100 USD per month is spared because of reduction in man- power requirement, 400–500 USD per month is saved due to reduced office space utilization, 100–200 USD per month is cut down on account of decreased consumption of operating supplies and 100–200 USD per month is cur- tailed owing to cloud computing-based disaster planning. Although the development and subsequent implementation of the expert system in the said organization require an initial investment, it is often offset by the derived saving. Moreover, once it is successfully installed, there is no recurring maintenance cost. It is also noticed that managers of the said organization can derive a plethora of benefits from application of this expert system, such as (a) reduc- tion in costs related to the selection process, (b) better utilization of workforce associated with decreased number of employees and increased personnel efficiency, (c) en- hancement of managerial efficiency with better decision- making, (d) effective utilization of information in the organizational environment, (e) strengthening of manage- ment information system for timeliness of CNC turning centre selection decision, and (f) accuracy in the decision- making process leading to higher production rate. Conclusions In today’s competitive manufacturing environment, the need for quality products is of utmost importance. There- fore, achieving cost-efficient, consistent machining results through automation is attractive to the manufacturing Fig. 12 Output of the expert system for ‘Custom’ option 592 J Ind Eng Int (2015) 11:575–594 123 organizations. In this paper, an expert system based on QFD methodology is designed and developed to help the process planners in CNC turning centre selection, where a large number of available alternatives need to be evaluated based on several conflicting criteria. It eases out and automates the entire selection procedure, while eliminating rigorous calculations involved, thereby reducing the time taken to arrive at the best decisive action. The level of human intervention is also minimized and the end users do not need to have an in-depth technical knowledge regard- ing various CNC machine tools. It ensures an error-free computation of CNC turning centre selection decision. Moreover, as discussed earlier, it encompasses both cus- tomers’ requirements and technical requirements in the selection procedure, thus providing the end users with a competitive edge over the previously adopted methodolo- gies. It can also work in a group decision-making envi- ronment where opinions from different individuals can be sought. It can be employed for selection of CNC turning centre for any type of production system depending on the requirements of the end users. The database for CNC turning centres can be upgraded from time to time to make it more dynamic. It can also be applied for selection of various other CNC machine tools, such as milling, grind- ing, etc. while creating a new module and database within the same system. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://crea tivecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References Aghdaie MH, Zolfani SH, Zavadskas EK (2013) Decision making in machine tool selection: an integrated approach with SWARA and COPRAS-G methods. Inz Ekon Eng Econ 4(1):5–17 Akao Y (1990) Quality function deployment. Productivity Press, Cambridge Ayağ Z (2007) A hybrid approach to machine-tool selection through AHP and simulation. Int J Prod Res 45(9):2029–2050 Ayağ Z, Özdemir RG (2006) A fuzzy AHP approach to evaluating machine tool alternatives. J Intell Manuf 17(2):179–190 Ayağ Z, Özdemir RG (2011) An intelligent approach to machining center selection through fuzzy analytic network process. J Intell Manuf 22(2):163–177 Ayağ Z, Özdemir RG (2012) Evaluating machine tool alternatives through modified TOPSIS and alpha-cut based fuzzy ANP. Int J Prod Econ 140(2):630–636 Bevilacquaa M, Ciarapicab FE, Giacchetta G (2006) A fuzzy-QFD approach to supplier selection. J Purch Supply Manag 12(1):14–27 Bhattacharya A, Geraghty J, Young P (2010) Supplier selection paradigm: an integrated hierarchical QFD methodology under multiple-criteria environment. Appl Soft Comput 10(4):1013–1027 Chakroborty S, Dey S (2007) QFD-based expert system for non- traditional machining process selection. Expert Syst Appl 32(4):1208–1217 Chan L-K, Wu M-L (2002) Quality function deployment: a literature review. Eur J Oper Res 143(3):463–497 Dağdeviren M (2008) Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. J Intell Manuf 19(4):397–406 Dai J, Blackhurst J (2012) A four-phase AHP-QFD approach for supplier assessment: a sustainability perspective. Int J Prod Res 50(19):5474–5490 Dawal SZM, Yusoff N, Nguyen H-T, Aoyama H (2013) Multi- attribute decision-making for CNC machine tool selection in FMC based on the integration of the improved consistent fuzzy AHP and TOPSIS. ASEAN Eng J Part A 3(2):15–31 Durán O, Aguilo J (2008) Computer-aided machine tool selection based on a fuzzy-AHP approach. Expert Syst Appl 34(3):1787–1794 Dursan M, Karsak EE (2013) A QFD-based fuzzy MCDM approach for supplier selection. Appl Math Model 37(8):5864–5875 İç YT (2012) An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robot Comput-Integr Manuf 28(2):245–256 İç YT, Yurdakul M (2009) Development of a decision support system for machining center selection. Expert SystAppl 36(2):3505–3513 İç YT, Yurdakul M, Eraslan E (2012) Development of a component- based machining centre selection model using AHP. Int J Prod Res 50(22):6489–6498 Ilangkumaran M, Sasirekha V, Anojkumar L, Boopathi Raja M (2012) Machine tool selection using AHP and VIKOR method- ologies under fuzzy environment. Int J Model Oper Manag 2(4):409–436 Karsak EE (2008) Robot selection using an integrated approach based on quality function deployment and fuzzy regression. Int J Prod Res 46(3):723–738 Liu H-T (2011) Product design and selection using fuzzy QFD and fuzzy MCDM approaches. Appl Math Model 35(1):482–496 Mayyas A, Shen Q, Mayyas A, Abdelhamid M, Shan D, Qattawi A, Omar M (2011) Using quality function deployment and analyt- ical hierarchy process for material selection of body-in-white. Mater Des 32(5):2771–2782 Nguyen H-T, Dawal SZM, Nukman Y, Aoyama H (2014) A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes. Expert Syst Appl 41(6):3078–3090 Önüt S, Kara SS, Efendigil T (2008) A hybrid fuzzy MCDM approach to machine tool selection. J Intell Manuf 19(4):443–453 Özgen A, Tuzkaya G, Tuzkaya UR, Özgen D (2011) A multi-criteria decision making approach for machine tool selection problem in a fuzzy environment. Int J Comput Intell Syst 4(4):431–445 Partovi FY, Corredoira RA (2002) Quality function deployment for the good of soccer. Eur J Oper Res 137(3):642–656 Prasad K, Chakraborty S (2013) A quality function deployment-based model for materials selection. Mater Des 49:525–535 Qi J (2010) Machine tool selection model based on fuzzy MCDM approach. In: Proceedings of International conference on intel- ligent control and information processing, Dalian, China, pp 282–285 Raissi S, Izadi M, Saati S (2012) Prioritizing engineering character- istic in QFD using fuzzy common set of weight. Am J Sci Res 49:34–49 Sahu AK, Datta S, Mahapatra SS (2015) GDMP for CNC machine tool selection with a compromise ranking method using J Ind Eng Int (2015) 11:575–594 593 123 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ generalised fuzzy circumstances. Int J Comput Aided Eng Technol 7(1):92–108 Samvedi A, Jain V, Chan FTS (2012) An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. Int J Prod Res 50(12):3211–3221 Shad Z, Roghanian E, Mojibian F (2014) Integration of QFD, AHP, and LPP methods in supplier development problems under uncertainty. J Ind Eng Int 10(1):9. doi:10.1007/s40092-014- 0051-0 Sharma JR, Rawani AM (2008) Quality function deployment: integrating comprehensive matrix and SWOT analysis for effective decision making. J Ind Eng Int 4(6):19–31 Soota T, Singh H, Mishra RC (2011) Fostering product development using combination of QFD and ANP: a case study. J Ind Eng Int 7(14):29–40 Sun S (2002) Assessing computer numerical control machines using data envelopment analysis. Int J Prod Res 40(9):2011–2039 Taha Z, Rostam S (2012) A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell. J Intell Manuf 23(6):2037–2049 Tho NH, Dawal SZM, Yusoff N, Tahriri Aoyama FH (2013) Selecting a CNC machine tool using the intuitionistic fuzzy TOPSIS approach for FMC. Appl Mech Mater 315:196–205 Xin Y, Tian X, Huang L (2014) Optimal machine tools selection using interval-valued data FCM clustering algorithm. Math Probl Eng. doi:10.1155/2014/921647 (Article ID 921647, 10 pages) Yurdakul M (2004) AHP as a strategic decision-making tool to justify machining center selection. J Mater Process Technol 146(3):365–376 Yurdakul M, İç YT (2009) Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. J Mater Process Technol 209(1):310–317 594 J Ind Eng Int (2015) 11:575–594 123 http://dx.doi.org/10.1007/s40092-014-0051-0 http://dx.doi.org/10.1007/s40092-014-0051-0 http://dx.doi.org/10.1155/2014/921647 Development of a QFD-based expert system for CNC turning centre selection Abstract Introduction Literature review in machine tool selection QFD methodology Development of a QFD-based expert system for CNC turning centre selection Illustrative examples Example 1 Example 2 Example 3 Real-time implementation of the developed expert system Conclusions Open Access References