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     . In other to evaluate machine performance, a holistic approach was suggested by several studies     and  . By collecting data from the operations of processing machines, operator behaviour and skill level  , and economic factors related to processing industries  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  . 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  . 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  . 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.  and  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.  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   . 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”  .
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.
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  as well as its logic. The computer algorithm and its software were developed, application of the developed software using data of  , 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
This is the financial economic consideration required to run the processing machine throughout the year. The model was as shown in Equation (1).
2.1.2. Overall Machine Effectiveness
This is the capability of producing a desired result. The three major attributes for its determination are machine availability, performance efficiency and rate of quality product . The mathematical model required is shown in Equation (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:   .
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.
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
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
If (SD2 < 0.85) Then
If (A_bar < 0.9) Then
Rt = 0.9 * opt
A_bar = Rt / opt
If (efficiency < 0.95) Then
Dim n_constant As Double = 0.8
efficiency = speed * (0.95 / n_constant)
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)
SD2 = A_bar * efficiency * epsilon
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
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"
fina = "Decrease in productivity"
Result3.Text = "(FPI)= " & FormatNumber(fpi, 2).ToString & vbCrLf & "(PRI) =" & FormatNumber(pri, 2).ToString & vbCrLf & "(CEI) = " & FormatNumber(cei, 2).ToString & vbCrLf & fina
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.
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.
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