Back
 OALibJ  Vol.7 No.11 , November 2020
Multi-Criteria Computer Aided System for Industrial Machines' Performance Assessment
Abstract: Efforts have been made by some researchers to determine machines' economic performance, some considered engineering features, some supply conditions while some look into the productivity as well as profitability of the machine separately. Recently, [1] saw the performance assessment of machine as surrogate problem and they deviate from single strategic decision common in past researches to multi-criteria approach in their research. Considerations were given to: annual operation cost, machine effectiveness and cost effective index as strategic decisions for machine performance evaluation. The model was robust, well integrated but its application is time consuming for decision making. There is no software to address this multi-criteria surrogate problem yet. Available single strategic decision software was of high cost, hence the development of this software that is flexible and novel to proffer solution to this problem using JAVA programming language. The software performance was evaluated using the data gotten from [1]. The summary of each year performance of a case study of cocoa winnowing machine on each of the selected strategic decision from 2008 to 2017, as it affects the machine annual operating cost (MAOC), overall machine effectiveness (MEFF) and cost effective index (CEI), was shown in Table 2. That of the year 2008 was 226,061.365; 0.97; and 0.99 for AOC; MEFF and CEI respectively. These results were statistically analysed and the results’ graphs were shown in Figures 2-5, and Figure 6, respectively. Their results were compared with the results of the software developed and the results were 100% accurate since there was no deviation from the results. Availability of this software makes the developed multi-criteria machine performance assessment model useable anywhere in the world.

1. Introduction

1.1. Background

The need to explore information and computer technology to solve agricultural problem most especially in agro-oil machines in industries become so important when the trend in computer world is increasing. The development in Information Computer Technology (ICT) can be applied to material processing industries in other to improve the effectiveness, quality and productivity of the processing machinery. This study was motivated by the recent emergence and growth of the computer in our society into which our processing industries must integrate.

1.2. Literature Review

When evaluating the performance of processing machine, two separate approaches can be taken: processing machines follow established principles that describe their operating characteristics on a generic basis and calculations of power, efficiencies, etc. are easily calculated using simple equations governing those properties [2] [3] [4] [5] . In other to evaluate machine performance, a holistic approach was suggested by several studies [6] [7] [8] [9] and [10] . By collecting data from the operations of processing machines, operator behaviour and skill level [9] , and economic factors related to processing industries [6] were determined on a much broader scale than with traditional theory based research.

Effective optimization model development is essential for processing equipment. This enhances the repair and maintenance of equipment at the most appropriate time [11] . It is being used in the developed world to know the salvage life of the machine, when you need to change the machine, the time to carry out certain periodic maintenance and repair. This also helps in determining the degree of utility of the equipment [12] . Important factors including types of equipment and operations are to be considered.

Optimization techniques such as linear or non-linear programming that minimize cost subject to reasonable constraints (e.g., labour availability, frost dates) can help improve profitability [12] . Over the decades, industries and their organization concentrated most of their attention upon products production thereby ignoring the “Overall Machine Effectiveness” (OEE) factors, viewing it as a necessary evil. [13] and [14] said, today, with the general operating cost rising each year, there is the potential of realizing significant savings if industrial optimization managers adhere strictly to proper OEE analysis practices. [15] said a well-structured OEE metrics practice plays a vital role in the efficiency, development and progress of manufacturing/processing industries.

A computer program is an instance, or concrete representation, for an algorithm in some programming language [16] [17] . Once we have a correct algorithm for a problem, we have to determine the efficiency of that algorithm. This view is stated very succinctly in the well-known slogan “algorithm = data structure + control” [18] .

Some of the related works done so far in this area of study are hereby summarized in Table 1. Hundreds of high level programming languages have been developed, the most common ones were shown in Table 2 for good comparison under ten criteria.

2. Methodology

The method applied to achieve the set objectives of this research involves: identification of the strategic decisions and their attributes; adopting the model developed by [1] as well as its logic. The computer algorithm and its software were developed, application of the developed software using data of [1] , on winnowing machine of cocoa industry as case study, results of the developed software were evaluated by comparing it with the manually generated results for its performance evaluation.

2.1. Models Development for Machine Annual Operating Cost

The models developed for the strategic decisions were: machine annual operating/running cost, overall machine effectiveness and machine cost effective index.

2.1.1. Machine Annual Operating/Running Cost [ O c ]

This is the financial economic consideration required to run the processing machine throughout the year. The model was as shown in Equation (1).

[ O c ] = F e % P + H ( R M c + L + O + F + T ) (1)

2.1.2. Overall Machine Effectiveness [ A ¨ ]

This is the capability of producing a desired result. The three major attributes for its determination are machine availability, [ A ˜ ] performance efficiency [ η ˙ ] and rate of quality product [ ϕ r ] . The mathematical model required is shown in Equation (2),

[ A ¨ ] = A ˜ × η ¨ × ϕ r (2)

2.1.3. Cost Effectiveness Index (Wc)

It also shows machine’s ability to fight inflation.

Table 1. Previous works on cocoa processing and machinery and software development.

*From literature there is a gap of multi-criteria model and its software to be developed for decision making on processing machines’ economic, engineering and productivity performance assessment. This led to this research.

Table 2. Commonly used programming language and their comparison for selection.

Source: [34] [35] .

C o s t E f f e c t i v e I n d e x = P r o d u c t i v i t y f o r C u r r e n t P e r i o d P r o d u c t i v i t y f o r t h e b a s e P e r i o d

W c = Q 2 P 2 / Q 1 P 1 I 2 C 2 / I 1 C 1 (3)

It is to be noted that cost effectiveness index is product of factor productivity index and price recovery index. The developed software interface is as shown in Figure 1.

3. Results and Discussions

The data collected for the running of the models and the software developed ran through period of ten (10) years from 2008 to 2017. Application of the software using the three selected strategic decisions on windowing machine, for each year 2008 to 2017 gave the results shown in Table 3.

Table 3 shows summary of each year performance of winnowing machine on each of the selected strategic decisions from 2008 to 2017. The performance as it affects the machine annual operating cost (MAOC), overall machine effectiveness (MEFF) and cost effective index (CEI) were shown in Table 3. These results were statistically analysed and the results’ graphs were shown in Figures 2-5, and Figure 6, respectively.

Figure 2 and Figure 3 show the mathematical model of the MAOC over the period of 10 years on the MAOC. The plot was modeled using a polynomial equation of one degree which gave us

f ( x ) = p 1 x + p 2

where: P1 = 0.056 and P2 = −112.2. This example shows how to fit polynomials up to one degree to the available MAOC data for the period of 10 years on the Windowing machine figures that correspond to the error (SSE) and the adjusted R-square statistics to help determine the best fit.cross zero on the p1 and p2 coefficients for the first-degree polynomial.

Figure 1. Interface for data collection, analysis and results generation.

Figure 2. Statistical analysis of windowing machine performance on Machine Annual Operating Cost (MAOC) from 2008 till 2017.

Figure 3. Determination of the model fittings Using R-Square Test.

Table 3. Yearly windowing machine’s performance on each strategic decision (AOC, MEFF and CEI) from 2008 to 2017. The bolded 2008 results were seen on the computer interface developed.

Figure 4. Statistical analysis of windowing machine performance on Machine Effectiveness (MEFF) from 2008 till 2017.

Figure 5. Statistical analysis of windowing machine performance on machine Cost Effective Index (CEI) from 2008 till 2017.

Figure 6. The 3D bar chart of MAOC, CEI and MEFF over the period of 10 years (2008-2017).

The model has a fitting of 97.76% according to R-Square test and the Sum of square error was given as 0.006 which is approximately 0. With this model we can actually predict the following year MAOC if all necessary factors are constant.

Figure 4 represents the machine operating effectiveness of the windowing machine over the period of 2008 to 2009 it has a very good flow with average effectiveness of 92.6% with variance of 0.0018. The average effectiveness of the machine varies over the years however the minimal effectiveness which is occurred in 2017 still has a very good effectiveness of 86% this is above the acceptable low limit of 85%.

Figure 5 represents the area chart of the cost effective index. Initially in 2008 the CEI is very close to 1 which means the operating cost of the windowing machine was performing well on budget. In 2009 the operating cost was performing well against budget. But 2010, 2011 and 2016 the windowing machine was over budgeted

Figure 6 represents the 3D bar chart of MAOC, CEI and Meff over the period of 10 years (2008-2017) to show the exact value of MAOC, CEI and Meff because area chart are known to show a trend over a particular period and not the exact value

Source Code For The Software Development.

Public Class Form1

Private Sub Analyse_Click(sender As Object, e As EventArgs) Handles Analyse.Click

'calculation for SD1

'FC cal

Dim FC, Um, RMs, Lr, Oc, Tfc, Tc, Ec, SD1 As Double

FC = Val(sd1_pc.Text) * 0.0275

Um = Val(sd1_pc.Text) / Val(sd1_ls.Text)

RMs = 0.06 * Val(sd1_pc.Text) '* Val(sd1_hp.Text)

Lr = Val(sd1_so.Text) / Val(sd1_toh.Text)

Oc = Val(sd1_tov.Text) * Val(sd1_ocl.Text) * 12

Tfc = Val(sd1_tfv.Text) * Val(sd1_fcl.Text) * 12

Tc = Val(sd1_ac.Text) / Val(sd1_po.Text)

Ec = Tfc + Oc

SD1 = FC + (Um * (RMs + Lr + Ec + Tc))

Console.WriteLine("FC%= " & FC.ToString)

Result1.Text = "(Umc)= " & FormatNumber(Um, 2).ToString & vbCrLf & "(Lr) =" & FormatNumber(Lr, 2).ToString & vbCrLf & "(Oc) = " & FormatNumber(Oc, 2).ToString & vbCrLf & "(Tfc)= " & FormatNumber(Tfc, 2).ToString & vbCrLf & "(Tc) = " & FormatNumber(Tc, 2).ToString & vbCrLf & "(Rmc)= " & FormatNumber(RMs, 2).ToString & vbCrLf & "(Aoc)= " & FormatNumber(SD1, 2).ToString & vbCrLf & "(Fc) = " & FormatNumber(FC, 2).ToString & vbCrLf & "(Ec) = " & FormatNumber(Ec, 2).ToString

'calculation for SD2

Dim A_bar, opt, speed, efficiency, N_bar, epsilon, SD2 As Double

Dim Rt As Double = Val(sd2_rt.Text)

Dim Lt = Rt - Val(sd2_lt.Text)

Dim Qo As Double = (Val(sd2_tp.Text) - Val(sd2_qd.Text))

opt = Val(sd2_tp.Text) - Val(sd2_st.Text)

A_bar = Val(sd2_rt.Text) / opt

speed = Val(sd2_act.Text) / Val(sd2_lct.Text)

N_bar = Val(sd2_ap.Text) / opt

efficiency = speed * N_bar

epsilon = (Val(sd2_tp.Text) - Val(sd2_qd.Text)) / Val(sd2_rt.Text)

SD2 = A_bar * efficiency * epsilon

Console.WriteLine(SD2.ToString)

If (SD2 < 0.85) Then

If (A_bar < 0.9) Then

Rt = 0.9 * opt

A_bar = Rt / opt

End If

If (efficiency < 0.95) Then

Dim n_constant As Double = 0.8

efficiency = speed * (0.95 / n_constant)

End If

efficiency = speed * N_bar

If (epsilon < 0.89) Then

epsilon = (Val(sd2_tp.Text) - Val(0.89 * Val(sd2_rt.Text))) / Val(sd2_rt.Text)

End If

SD2 = A_bar * efficiency * epsilon

End If

Result2.Text = "(Rt)= " & FormatNumber(Rt, 2).ToString & vbCrLf & "(Lt) =" & FormatNumber(Lt, 2).ToString & vbCrLf & "(Op) = " & FormatNumber(opt, 2).ToString & vbCrLf & "(Qo)= " & FormatNumber(Qo, 2).ToString & vbCrLf & "(Qr) = " & FormatNumber(epsilon, 2).ToString & vbCrLf & "(Os)= " & FormatNumber(speed, 2).ToString & vbCrLf & "(A)= " & FormatNumber(A_bar, 2).ToString & vbCrLf & "(Nr) = " & FormatNumber(N_bar, 2).ToString & vbCrLf & "(Peff) = " & FormatNumber(efficiency, 2).ToString & vbCrLf & "(Meff) = " & FormatNumber(SD2, 2).ToString

'SD3 computation

Dim mpi, pri, cei, pf, fpi As Double

Dim fina As String

'mpi = (Val(sd3_a1.Text) / Val(sd3_a2.Text))

pri = ((Val(sd3_Q2.Text) * Val(sd3_p2.Text)) / (Val(sd3_Q2.Text) * Val(sd3_p1.Text))) / ((Val(sd3_I2.Text) * Val(sd3_c2.Text)) / (Val(sd3_I2.Text) * Val(sd3_c1.Text)))

cei = ((Val(sd3_Q2.Text) * Val(sd3_p1.Text)) / (Val(sd3_Q1.Text) * Val(sd3_p1.Text))) / ((Val(sd3_I2.Text) * Val(sd3_c2.Text)) / (Val(sd3_I1.Text) * Val(sd3_c1.Text)))

fpi = ((Val(sd3_Q2.Text) * Val(sd3_p1.Text)) / (Val(sd3_Q1.Text) * Val(sd3_p1.Text))) / ((Val(sd3_I2.Text) * Val(sd3_c1.Text)) / (Val(sd3_I1.Text) * Val(sd3_c1.Text)))

pf = cei / pri

If (pf = pri) Then

fina = "Static productivity"

ElseIf pf > pri Then

fina = "increase In productivity"

Else

fina = "Decrease in productivity"

End If

Result3.Text = "(FPI)= " & FormatNumber(fpi, 2).ToString & vbCrLf & "(PRI) =" & FormatNumber(pri, 2).ToString & vbCrLf & "(CEI) = " & FormatNumber(cei, 2).ToString & vbCrLf & fina

End Sub

End Class

4. Conclusion

The objectives which are computer algorithm and software development for the models’ implementation were achieved and source code written for the model’s ease of application using JAVA programming language due to its flexibility and friendliness was also achieved. The cost benefit, was successfully determined by comparing the cost of foreign software of nearly similar functions with limitation of single criterion with this of multi-criteria cost and the software was able to make a saving cost of 40% based on the average cost of the six software collected from the internet. The tool was able to consider arms of Economic, Engineering and Productivity features, of production processes, in an attempt to reduce/eliminate all barriers that could hinder optimal performance. The outcome contributed to the existing knowledge in the field of Industrial Engineering and in particular decision making in machine operating cost, overall machine effectiveness for productivity enhancement and machine operations’ cost effective index to determine the machine’s ability to fight inflation.

Acknowledgements

The research team hereby acknowledged the Managing Director of Hurlag Technologies limited, 32A Ladipo Oluwole Street, Ikeja, Lagos for both their technical and financial support that made this research a success.

Cite this paper: Olagunju, O.R., Akinnuli, B.O., Mogaji, P.B. and Awopetu, O.O. (2020) Multi-Criteria Computer Aided System for Industrial Machines' Performance Assessment. Open Access Library Journal, 7, 1-15. doi: 10.4236/oalib.1106862.
References

[1]   Mogaji, P.B., Akinnuli, B.O., Awopetu, O.O. and Olagunju, O.R. (2020) Modeling Economic Dominant Feature for Agro-Oil Seeds Processing Machinery Performance Evaluation. International Journal of Industrial Engineering & Technology, 10, 11-20.

[2]   Akinnuli, B.O. and Oluwadare, S.A. (2011) Computer Aided System for Modelling Machinery Procurement due Date Prediction in Production Industries. Journal of Information Computer Technology, 10, 99-115. https://doi.org/10.32890/jict.10.2011.8111

[3]   Goering, C., Stone, M., Smith, D. and Turnquist, P. (2006) Off-Road Vehicle Engineering Principles. American Society of Agricultural Engineers, St. Joseph.

[4]   Srivastava, A., Goering, C., Rohrbach, R. and Buckmaster, D. (2006) Engineering Principles of Agricultural Machinery. American Society of Agricultural and Biological Engineers, St. Joseph.

[5]   Goering, C. and Hansen, A. (2008) Engine and Tractor Power. American Society of Agricultural and Biological Engineers, St. Joseph.

[6]   Yule, I.J., Kohnen, G. and Nowak, M. (1999) A Tractor Performance Monitor with DGPS Capability. Computers and Electronics in Agriculture, 23, 155-174.
https://doi.org/10.1016/S0168-1699(99)00029-0

[7]   Schmidt, J.P., Taylor, R.K. and Gehl, R.J. (2003) Developing Topographic Maps Using a Sub-Meter Accuracy Global Positioning Receiver. Applied Engineering in Agriculture, 19, 291-300.
https://doi.org/10.13031/2013.13661

[8]   Boon, N.E., Yahya, A., Kheiralla, A.F., Wee, B.S. and Gew, S.K. (2005) A Tractor-Mounted, Automated Soil Penetrometer-Shearometer Unit for Mapping Soil Mechanical Properties. Biosystems Engineering, 90, 381-396. https://doi.org/10.1016/j.biosystemseng.2004.12.004

[9]   Yahya, A., Zohadie, M., Kheiralla, A.F., Giew, S.K. and Boon, N.E. (2009) Mapping System for Tractor-Implement Performance. Computers and Electronics in Agriculture, 69, 2-11.
https://doi.org/10.1016/j.compag.2009.06.010

[10]   Singh, C.D. and Singh, R.C. (2011) Computerized Instrumentation System for Monitoring the Tractor Performance in the Field. Journal of Terramechanics, 48, 333-338.
https://doi.org/10.1016/j.jterra.2011.06.007

[11]   Akinnuli, B.O. and Babalola, S.A. (2013) Computer-Aided System for Determining Industrial Machinery Optimal Replacement Period. Journal of Information and Communication Technology, 12, 175-188. https://doi.org/10.32890/jict.12.2013.8143

[12]   Agritrade, Executive Brief: The Cocoa Sector in ACP-EU Trade, October 2009. 6.

[13]   Bruce, C.H. (2006) Best Practices in Maintenance.
http://www.tpmonline.com/articles_on_total_productive_maintenance/management

[14]   Diwlworth, J.B. (2013) Production and Operations Management. McGraw-Hill, New York.

[15]   Kadiri, M.A. (2000) Scheduling of Preventive Maintenance in a Manufacturing Company: A Computer Model Approach. Unpublished M.Sc. Thesis, Department of Industrial and Production Engineering, University of Ibadan, Ibadan.

[16]   Oluwadare, S.A. and Akinnuli, B.O. (2012) A Mixed Linear Programming Model for Real-Time Task Scheduling in Multiprocessor Computer System. Journal of Information and Communication Technology, 11, 17-36.

[17]   Cerpa, H.C. and Verner, F.T. (1996) Making Chocolate from Scratch. FSandT-33. CTAHR, University of Hawai‘i, Honolulu.

[18]   Hamundu, F.M., Wibowo, S. and Budiarto (2012) A Hybrid Fuzzy-Monte Carlo Simulation Approach for Economical Assessment of the Impact of ERP Technology. Journal of Information and Communication Technology, 12, 93-111.

[19]   Akinnuli, B.O., Ayodeji, S.P. and Omeiza, A.J. (2014) Computer Aided Design for Cocoa Beans Processing Yield Prediction. International Journal of Applies Science and Technology, 4, 8-9l.

[20]   Akinnuli, B.O., Bekunmi, O.S. and Osueke, C.O. (2015) Design Concept towards Cocoa Winnowing Mechanization for Nibs Production in Manufacturing Industries. British Journal of Applied Science and Technology, 161, 35-45. https://doi.org/10.9734/BJAST/2015/16161

[21]   Adzimah, S.K. and Asiam, E.K. (2010) Design of a Cocoa Pod Splitting Machine. Research Journal of Applied Sciences, Engineering and Technology, 2, 622-634.

[22]   Arai, N. and Iwata, S.T. (1997) Cocoa Crop Protection: An Expert Forecast on Future Progress, Research Priorities and Policy with the Help of the Delphi Survey. Crop Protection, 16, 227-233.
https://doi.org/10.1016/S0261-2194(96)00099-3

[23]   Audu, I., Oloso, A.O. and Umar, B. (2004) Development of a Concentric Cylinder Locust Bean Dehuller. Agricultural Engineering International, 6, 14-19.

[24]   Awua, P.K. (2002) Cocoa Processing and Chocolate Manufacture in Ghana. David Jamieson and Associates Press Inc., Essex, 12-14.

[25]   Bjarnemo, B.O. and Hansen, R.C. (1998) Overall Equipment Effectiveness (OEE). Industrial Press, Italy.

[26]   Bozzo, F.T. and Harrison, J.R. (1998) Dominant Coalition Dynamics, the Politics of Organizational Adaptation and Failure. International Conference on Computer Simulation and the Social Science, Cortona.

[27]   EEC (1973) Directive 73/241/EEC by European Parliament and the European Council Relating to Cocoa and Chocolate Products Intended for Human Consumption. Official Journal of the European Communities, L228, 23-35.

[28]   Faborode, M.O. and Oladosun, G.A. (1991) Development of a Cocoa Pod-Processing Machine. Nigerian Engineers, 26, 26-31.

[29]   Harrington, S.F. (1998) Cultivating Cacao: Implications of Sun-Grown Cacao on Local Food Security and Environmental Sustainability. Agriculture and Human Values, 20, 277-285.

[30]   World Cocoa Foundation Scientific Research and Website Library.
http://www.worldcocoafoundation.org

[31]   Jurgen, F. and Buhler, B. (2009) The Manufacturing Confectioner September, Cocoa Processing. Cleaning through Roasting.

[32]   Lipp, M. and Anklam, E. (1998) Review of Cocoa Butter and Alternative Fats for Use in Chocolate—Part A. Compositional Data. Food Chemistry, 62, 73-79.
https://doi.org/10.1016/S0308-8146(97)00160-X

[33]   Whitefield, R. (2005) Making Chocolates in the Factory. Kenedy’s Publications Ltd., London.

[34]   Oluwadare, S.A. and Akinnuli, B.O. (2012) A Mixed Integer Linear Programming Model for Real-Time Task Scheduling in Multiprocessor Computer System. Journal of Information and Communication Technology, 12, 17-36.

[35]   Weipedia Scala. http://en.wikipedia.org/wiki/Scala_(programming_language)

 
 
Top