CUCKOO-ENTROPY ALGORITHM BASED QUALITY FUNCTION DEPLOYMENT FOR REPLACEMENT ANALYSIS

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 4155-4166 © TJPRC Pvt. Ltd.

CUCKOO-ENTROPY ALGORITHM BASED QUALITY FUNCTION DEPLOYMENT FOR REPLACEMENT ANALYSIS P. VENU1, ARCHANA S. VASUDEV2, FRANCIS THOMAS3 & NOEL JOSEPH GOMEZ4 1

Professor, Department of Mechanical Engineering, SCMS School of Engineering and Technology, Kerala, India 2 3,4

Freelance Programmer-Java & Python, Kerala, India

Assistant Professor, Department of Mechanical Engineering, SCMS School of Engineering and Technology, Kerala, India

ABSTRACT The replacement of machinery is one of the major challenges faced by medium scale industries in developing countries like India. The decision of replacement, if initiated, by aspects like lifetime, major repair, breakdown or similar unforeseen problems, is easy for the management but replacement based on product design, competition or competitor machinery upgradation etc lands as a heavy burden for decision makers to act quickly. This research proposes a Quality framework is built on the strong platform of QFD that captures customer voices and transfers it into product features.

This framework first establishes the relevance of a product design/development change, which is validated through algorithms to prove the necessity to outrun competitors to stay ahead in the market. The core area of this work is competitor analysis of QFD using algorithms that encapsulates cuckoo algorithm ably supported by entropy equations.

The paper concludes with a validation by conventional replacement analysis on a sesame oil mill machine investigating the need of replacing the existing one with a new higher end machine.

Original Article

Function Deployment (QFD) approach for replacement analysis incorporating cuckoo and entropy algorithms. This

KEYWORDS: Quality Function Deployment, Replacement Analysis, Cuckoo, Entropy method & Competitor Analysis

Received: Jun 10, 2020; Accepted: Jun 30, 2020; Published: Jul 20, 2020; Paper Id.: IJMPERDJUN2020395

INTRODUCTION It has been observed that any replacement analysis is based on the expected purchase, operating, maintenance and salvage costs. The utilization of the machine over a constant or predetermined period is marked as the primary parameter before being labelled for replacement. This parameter may not hold good under various conditions like randomness in operations due to customer demand, product improvements or even government policies [1,2]. The small and medium level industries in developing countries like India find it unacceptable to take a decision on replacement of an asset if it is in operating condition. They tend to neglect the fact that if any existing equipment fail to meet the challenges made by the advancements of the technological developments, it must be replaced even though it is in operating conditions [3]. It is very important that timely decision to replace a machine in certain cases even though having physical value, be replaced, if it loses economical value, means fails to withstand competition [4]. It has been found that most of the companies falls in the dilemma during fluctuations in customer requirements and competitor threats. Therefore, developing a concrete method to whether consider a replacement of machinery/equipment in good operating conditions against a modern or economically superior one is important from an industry perspective. Quality Function Deployment (QFD) has been considered as a systematic tool to www.tjprc.org

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gather customer requirements and convert those requirements into product features [5]. QFD was originally proposed, through collecting and analyzing the voice of customers, to develop products with higher quality to meet or surpass customer’s needs. The primary functions of QFD are product development, quality management, and customer need analysis. Later QFD’s functions had been extended to wider field such as design, planning, decision-making, engineering, management, teamwork, timing and costing [7]. The traditional QFD methodology has its results in the form of crisp values which has associated lot of vagueness. This vagueness is initiating many drawbacks to the QFD environment. The application of fuzzy reasoning techniques provides a systematic tool to deal with qualitative and quantitative data in the construction process. The fuzzy set theory is widely applied to solve real life problems that are subjective, vague and imprecise in nature [8,9]. The Fuzzy QFD (FQFD) has been a fancy area of research for many during the last two decades and various models have been published and practiced across the world. All of these models are common in using fuzzy logic. Figure 1 shows the most prominent models that are found it literatures.

Figure 1: Prominent Fuzzy QFD Models The metaheuristic model and the hybrid models are suitable in dynamic conditions and the ability of these models to incorporate various algorithms has enabled these models to solve complex problems arising from the dynamic and uncertain behaviour of various phases of QFD process. The neuro-fuzzy model based on artificial intelligence including fuzzy theory, backpropagation neural network (BPN), and genetic algorithm (GA), along with morphological analysis to synthesize, evaluate, and optimizate of product design. [10, 11]. The data input of the QFD system makes it vulnerable to many situations like error data, missing data, unexpected data etc., hence the use of latest algorithms to optimize data is effective [12]. The genetic algorithm and intelligence algorithms are readily available with its source code. The Java programming code for the algorithms are so effective and easy to adopt because of its portability, robustness and ease of use. The effective use of these Java algorithms not only accelerates the QFD process but also makes it error free. These packages are open source code and readily available in the cyber space [13,14].

Cuckoo search (CS) is an optimization algorithm developed by Xin-she Yang and Suash Deb in 2009 (Wikipedia). It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species). Some host birds can engage direct conflict with the intruding cuckoos. For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Some cuckoo species such as the New World brood-parasitic Tapera have evolved in such a way that female parasitic cuckoos are often very specialized in the mimicry in colours and pattern of the eggs of a few Impact Factor (JCC): 8.8746

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chosen host species.

Cuckoo search idealized such breeding behaviour, and thus can be applied for various optimization problems. It seems that it can outperform other metaheuristic algorithms in applications. The application of cuckoo algorithm to various phases of prioritization is going to be a challenge. The effective protocol for data handling using cuckoo algorithm has been used in various mechanical as well as industrial engineering application involving prediction using neuro-fuzzy system [15-17]. The paper proposes a method to wrap product planning phase of QFD in a Java platform that has packages, classes and functions to invoke cuckoo algorithm and entropy equations to capture customer responses, generate weightages, calculate importance ratings, enumerate technical parameters, decide on competitor ratings and finally provide a fuzzy number based product feature along with its technical parameters in a nut shell. The data at each stage is trained for further deployment and shared with each phase of deployment.

THE CUCKOO-ENTROPY FRAMEWORK FOR QFD The framework used the fundamental fuzzy QFD equations with some improvisation with the help of Java code of the program. The java program is built into a single class for product development phase of QFD. The MS Access database is used to store and retrieve data. The equations used for calculating the critical parameters of fuzzy QFD process is established by various literatures and need no more explanations and scripts. At all stages of development of the program equations to convert basic crisp QFD equation to triangular fuzzy numbers have been used [12, 18]. The major parameters enhanced with the proposed methodology is illustrated in the following sections.

Customer-Competitor Ratings The ultimate aim of the product planning phase of QFD is to fine the most important customer required which can be optimally satisfied by the company during its product enhancement period. This is to be done by overtaking the competitor features and developing features that are not present in the competitor product.

The flexibility of the cuckoo algorithm to eliminate the unmatched data during the generation of the customer competitive comparison matrix is can be eliminated to a large extend by using the cuckoo algorithm. The cuckoo algorithm generated and successfully implemented by using the Java Programming package. This algorithm can be easily coded to program with the help of MATLAB also [12]. The coding once tested in MATLAB can be converted to Java Codes for implementation in the framework. The following java open source code packages helps in developing the program from the algorithm given in the next section •

ulb.infof310.fpicalau.hash

•

ulb.infof310.fpicalau.hash.cuckoo

•

ulb.infof310.fpicalau.hash.hashfunctions`

The cuckoo algorithms developed for the customer competitive rating & analysis is rooting from the primary logic followed.

Initialize Competitive Optimize function f(p), P= (P1,P2 ..., Pc)T Generate initial population of n host nests www.tjprc.org

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Pi (i = 1, 2, ..., n) while (t <MaxGeneration) or (stop criterion) Get a cuckoo randomly by L´evy flights evaluate its quality/fitness Fi Choose a nest among n (say, j) randomly if (Fi > Fj ), replace j by the new solution; end Found fraction (Fa) of worse nests Do abandon and new ones are built; until Preset Best Solutions are reached If stop criterion Keep the best solutions else Nests with quality solutions Sort the solutions and find the current best end while Insert into Customer Comparison Matrix End Final Importance Ratings The Stage consists of calculations mainly consisting of Entropy methods. Entropy is defined as measure of quantity of information that is linked by a discrete probability distribution. The calculation of competitive priority rating is done with the competitive matrix loaded to the database of the framework. The first step in the calculation is finding the total competitive score of the individual WHATS based on the competitor ratings using (1). The total competitive scored is the divided with the individual competitive rating to calculate the probability distribution of the individual WHATS with the aid of (2).

Tn  (T 11  T 12  .......... ..  T 1c) / C

(1)

tn  tij / ci

(2)

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Tn stands for the total competitive score, C stands for the number of competitors and i=1,2,3......n and j=1,2,3....C. This stage is the preceded by the codes generated by using Java.MI.Entropy. The class is extended to the base QFD class and the various methods defined in the class when executed will generate the competitive ratings. The next challenge in front of the GRQFDO framework is the goal performance setting. The goal insertion data is critical to generate the improvement ratio and improvement ratio is that parameter which helps in gaining a competitive advantage to the product against its various competitors in the market. The goal setting is done with aid of cuckoo algorithm. The final importance rating is the final result of this module.

Technical Competitive Priority Rating The final importance rating gives a clear idea of the most prominent technical parameters required to convert the voice of customers to product specifications. This data also helps in the trade-off criteria and also to eliminate the so called ‘ineffective’ technical parameters from further deployment. The result of this analysis is stored in the database and trained into the system for the next deployment processes. The system gets strengthened with each and every deployment process and once the data are completely obtained it can be easily become a part of a continuous non-stop running product development system.

Technical Competitive analysis is a critical part of this stage. It requires minute and factual data from engineering or technical side of the company. The HOWS prioritized will be rated based on the current product level against an expected level referring to the competitor features. These data are taken carefully and inserted into the database. Any variation in this data during the following deployments will be directly given to a manual terminal for thorough verification.

The Innovative Data Regulator This module is built on a higher end package of java that supports Artificial Intelligence and Trained Intelligence Algorithms. The data for competitor analysis and goal setting is verified by the program and then parsed using the cuckoo algorithms. The errors, mismatch, missing, vagueness, out of bound and similar other data problem if found can be rectified and visualised through this page. This page shows the memory of the database that is edited the arc of data that is fixed and count of modifications made in the data through a graphical user interface.

RESULTS AND DISCUSSIONS The proposed method is imposed on data received from a sesame oil manufacturing company as the complexity level of data is minimum and also the product sesame oil is a fast-moving consumable good with definite number of quality parameter. The table 1 shows the quality variables.

Table 1: Sesame Oil Quality Variables Primary Property Secondary Properties Physical Colour, Smell, Solubility, Touch and Feel Cooking Taste, Health, Speed of cooking, Solubility, Smoke Index Body Use Hair oil, Massage oil Handling Bottling, Quantity, Material, Re-usability, Colour

From these quality variables the following are finalised based on the technical expert opinion obtained from the

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company.

•

Taste (R1)

•

Quantity (R2)

•

Touch and Feel (R3)

•

Colour (R4)

•

Massage Oil (R5)

•

Speed of cooking (R6)

•

Smell (R7)

•

Hair Oil (R8)

•

Bottling (R9)

•

Health (R10)

The technical descriptors or HOWS needed to satisfy the basic customer requirements is listed out taking direct opinion from the production department. The list show first fourteen among the eighteen technical descriptors under deployment.

•

Additive Hair Oil (H1)

•

Additive Colour (H2)

•

Additive Taste (H3)

•

Anti Oxidants (H4)

•

Additive Smell (H5)

•

Yeast (H6)

•

Viscosity (H7)

•

Filter 1 (H8)

•

Expeller (H9)

•

Multi Packaging Unit (H10)

•

Filter 2 (H11)

•

Packing Material (H12)

•

Packet/Bottle Shape (H13)

•

External Agents (H14)

The deployment process using the cuckoo-entropy framework program is initiated and the results of major QFD parameters are critically examined. The relative importance ratings result studied and plotted on a fuzzy scale graph is

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shown in figure 2.

Figure 2: Average Fuzzy Relative Importance Ratings

The competitor priority rating derived from the Java.MI Entropy class program is shown in figure 3. The interface proved to be effective in giving a very clear result as visible from the chart. A total of 9 sets of trial runs are shown and it could be seen that the results of all the runs are pretty closer showing the proper implementation of the algorithm through programming.

Figure 3: Competitive Priority Ratings

The final technical ratings obtained after traversing through all modules of product planning phase is shown in table 2. The table shows the floating-point results obtained using crisp values of traditional QFD process and the fuzzy number results generated using the cuckoo-entropy framework. The comparison of these two values shows that the proposed method is not having erratic values but fall in the range of values obtained using traditional equations. The fuzzy presentation wipes out the vagueness and more effective comparison of the results may be interpreted.

Table 2: Final Technical Rating www.tjprc.org

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Sorted Hows

Floating FTR

Scaled STFN FTR

H9 H4

13.28 12.21

0.5012,0.1000 0.4415,0.9048

H2

9.69

0.4013,0.8678

H8

7.72

0.4083,0.8326

H1

5.27

0.3445,0.7551

H10

5.22

0.2602,0.6762

H3

4.96

0.2456,0.6443

H5

2.98

0.1768,0.5333

H7

2.53

0.1585,0.5069

H6

2.37

0.1529,0.4972

H11

2.28

0.1319,0.4027

H14

2.22

0.1319,0.4027

H12

1.53

0.1029,0.3595

H13

0.76

0.0947,0.2974

The results show that for improving the color, health and hair-oil properties of the sesame oil, a cold pressed expeller and an external agent that improves the hair growth are the best technical measures. It is also evident that the cold pressed expeller can also improve three more customer requirements; hence it is a critical technical parameter that has to be implemented to improve the existing oil properties.

REPLACEMENT ANALYSIS The figure 4 shows the sesame oil production process of the company under deployment. It can be seen that the expeller is a critical machinery that almost dictates the quality of oil extracted from the sesame seeds.

Figure 4: Sesame oil Production Process The company is having an expeller in its production line with a roaster attached to it for heating the sesame seeds.

The heating is done so to extract more oil as the existing expeller does not have the capability to extract the optimum oil from cold sesame seed. It has been also observed that most of the market leader in sesame oil production uses most

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advanced cold pressed expellers for production. There replacement analysis is done by challenging the existing expeller machine against a new cold pressed expeller machine. The new machine proposed is called the Challenger and the existing machine is called Defender. The data for challenger and defender is inserted in the Table 3.

Table 3: Replacement Analysis Data Parameters Defender

Challenger

PURCHASE PRICE (P)

800000

2470000

PRESENT VALUE (P)

300000

2470000

SALVAGE VALUE (F)

100000

400000

ANNUAL MAINTENANCE COST (A)

239000

98000

LIFE (n)

6 years

20 years

15%

15%

0.2642

0.1598

INTEREST RATE (i) (A/P, i, n) From Interest table based on 'I'

Defender – Annual Equivalent Cost (AE) AE = (300000 – 100000) × 0.2642 + (100000 × 0.15) + 239500 = Rs 307340 Challenger – Annual Equivalent Cost (AE) AE= (2470000 - 400000) × 0.1598 + (400000 ×0.15) + 98000 = Rs 487544 Advantage of Defender over Challenger is Annual-Equivalent-Cost = 487544 –307340=Rs 180204 The EAC of challenger considering the data shown in table is less than defender. This means that defender machine is not in a position to be replaced. The actual EAC of defender suffers some major setbacks which are shown in the next topic.

Cost Advantages of Cold Pressed Expeller Annual production in MTs = 2400/year and Production in kg/h = 363.63 = 364 kg/h Existing machine has a rated capacity of 500 kg/h. But the company reports indicate that the capacity has been only 273 kg/h for the last 18 months. The company usually produces 394 kg/h (target set to meet floating demands). So the existing machine is in deficit of 121 kg/h during normal shift of 8 hours. This extra required quantity is obtained by doing an overtime of 3.5 hours every day.

Cost of producing 121 kg/h per year and Workers overtime wages = 300 per worker/shift Total cost spent for workers alone= 300 × 3 × 225= Rs 202500 It can be seen that the above amount also get added in the annual equivalent cost of the Defender (existing machine).

The Challenger (Present value – 247000) which is the new machine has a rated production of 600 kg/h and a guarantee of 515 kg/h for its life of 20 years. Therefore, it can be concluded that Challenger (new machine) is more economical than Defender (existing machine) using Replacement Analysis and cost advantage. The advantages of the www.tjprc.org

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challenger over the defender are listed: •

AE of Defender > AE of Challenger

•

Challenger has an annual cost gain of Rs.22296 compared to Defender

•

The challenger can provide up to 500 kg/hr of Sesame oil if needed

•

Improved Quality of Sesame oil will result in improved sales

The replacement analysis result shows that the QFD process done under the roof of cuckoo-entropy framework yield positive results. The result appears to be an easy catch which again proves the validity of the framework.

CONCLUSIONS The necessity of a replacement analysis may arise in an industry or factories due to fluctuating customer requirements, arrival of latest technologies or due to many unforeseen parameters. This might force a company to decide on replacing or scrapping an existing machine under good operating condition. This decision making has to be prompt and precise as this decision many a times involves huge investments. The paper proposes a methodology to trigger this decision process with the help of Quality Functional Deployment process. The framework thus developed is a modified QFD program to incorporate the uncertainty in customer requirements and also to effectively calculate the competitive rating analysis. The java packages and classes used to code the program provides an automated environment for decision makers to compare and contrast the critical parameters of deployment to adjudge a replacement process.

ACKNOWLEDGMENTS This work was supported by Pavithram Sesame Oil Company by providing necessary data to initiate the Quality Function Deployment process. The results shown in this paper are not yet endorsed by the company as it has not been disclosed to the company. The authors thank the management of the company for the support extended for the research work. The authors are also indebted to the web-based platform GitHub for its repositories that had highly influenced the programming of the proposed framework.

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Bean, J., Lohmann, J. and Smith, R. (1994) Equipment replacement under technological change. Naval Research Logistics, 41, 117-128.

2.

Bethuyne, G. (1998) Optimal replacement under variable intenstity of utilization and technological progress. The Engineering Economist, 43(2), 85-106.

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Harman, J., & Tan, C. H., (2014), Equipment replacement analysis: A literature review and directions for future research. The Engineering Economic, 59, 136-153.

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NAIK, NORA, et al. "LEUKEMIA PREDICTION USING RANDOM FOREST ALGORITHM." International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) 8.3, Aug 2018, 1-8

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Nguyen, T.P.K., Yeng, T.G., & Castainer, B. (2013), Optimal maintenance and replacement decisions under technological change with consideration of spare parts inventories, International Journal of Production Economics, 143, 472-477.

6.

Besterfield, D. H., Besterfield-Mivhna, C., Besterfield, G. H., and Besterfield-Sacre M. (2003). Total Quality Management, Book, Third Edition, Pearson Prentice Hall, New Jersey, USA.

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