Performance status has to do with profitability of manufacturing industries. Good performance brings about increase in productivity. There are in existence different models and software, but none has been able to develop software based on Am erican Productivity Center model (APC). The strategic decisions required were identified as: Factor Productivity, Price Recovery and Cost Effectiveness indices, while the parameters used are: quantity produced, price per unit, labour input time, cost per hour of labour as well as the period. These were used to develop the models for the strategic decisions mentioned and software (PPE-INDICES, 2016) for implementation of the models using Java programming language. Olam cocoa processing company was used as the case study and the software was able to report the performance of the company thus: 24%, 51% and 87% increase in Factor Productivity, Price Recovery and Cost Effectiveness indices respectively for period 2013/2014, 29%, 20% and 55% increase in Factor Productivity, Price Recovery and Cost Effectiveness indices respectively for period 2014/2015, and 23%, 13% and 39% increase in Factor Productivity, Price Recovery and Cost Effectiveness indices respectively for period 2015/2016. The model and its software will find its application in all manufacturing industries of developing and developed countries.
In the development literature, industrialization has been accepted as the major driving force of the modern economy. In most modern economies, industrial sector serves as the vehicle for the production of goods and services, the generation of employment and the enhancement of incomes. Hence, [
In the light of the above, Nigeria has employed several strategies which were aimed at enhancing the productivity of the sector in order to bring about economic growth and development. For instance, the country adopted the import substitution industrialization strategy during the First National Development Plan (1962-1968) which aimed at reducing the volume of imports of finished goods and encouraging foreign exchange savings by producing locally some of the imported consumer goods, Central bank of Nigeria [
There are many definitions attached to productivity by many authors like: [
Although the definition of productivity appears straight forward, for three major reasons it is difficult to deal with [
Productivity combines the concepts of effectiveness and efficiency, where effectiveness is the degree to which end results are achieved to the required standard [
Conceptually, output embodies both quality and quantity and this creates sometimes confusion that the productivity measure is unfounded in the sense that they do not take quality into consideration. Such arguments may be true in case of very simple productivity ratios. In those ratios, the quality of the output or input is often ignored. But, when the output is measured in deflated net sales, for example, the quality of the products or services is included in the function. However, quantifying quality changes in productivity measurement is always a measurement problem, not a conceptual problem. At the conceptual level quality of the output and the input are very much included in the productivity ratio.
The following are the methods used to achieve the objectives in this research which are: ascertain the required parameters for the model development, mathematical models used for the required computation of each parameter, development of algorithm and its software for implementing the mathematical models ascertained, application of the models and the developed software named (PPE-INDICES 2016).
The following parameters for strategic decision taken were identified in this research for required model development:
1) Quantity produced in year: This is the total unit or quantity of product produced in either the base year or current year. It is an output function.
2) Price per Unit for product: This function is used to convert the output in to monetary form. It is the cost of each unit of the product.
3) Labour input time: This is an input function that describes the time input of labour to produce the given output quantity of the product.
4) Cost per hour of labour: This is the amount paid per hour for the labour used to produce the given quantity of product.
5) Period of time: This is the time under consideration which could be either a base year or a current year. The base year is the period used as a comparison time to determine the economic productivity of a current year relative to the base year.
6) Factor Productivity Index (FPI): This defined as the ratio of the value of current level output to base level output, divided by the ratio of the value of current-period inputs to base level inputs. The productivity change ratio measures the technical efficiency of firms.
7) Price recovery index: This defined as the ratio of the value of outputs at current period prices to the value at base level prices, divided by the value of inputs at current period prices to the value at base level prices. The price recovery ratio helps measure the abilities of firms to be price or allocative efficient.
8) Cost Effectiveness index: This is the ratio the value change of input to the value change of total output.
Data used for this study were extracted from accounting/inventory departments of Olam Cocoa Processing Company Akure. Olam is a leading global integrated supply chain manager and processor of agricultural products and food ingredients. Agricultural products processed by Olam include Cocoa, Cashew, Sesame and Cotton. It also has a cashew processing plant at Ogbondoroko, a suburb of Ilorin, Kwara state. According to the statement, working closely with the group, Olam’s corporate responsibility and sustainability team had delivered farm management training and GPS mapped 5000 hectares of farmland. “In so doing, Olam equipped Nigerian cocoa farmers with accurate information about the size of their farms”, it added. The company also distributed higher yielding planting materials and successfully prepared the farmers for Rainforest Alliance audits in 2012 and 2013 which made the N26 million premium payments possible [
The company is located besides Ondo State Cooperative building, Akure-Owo express road. The Company has over 50 workers both skilled and unskilled.
1) Q1 = Quantity produced in year 1
2) Q2 = Quantity produced in year 2
3) P1 = price per units for product in year 1
4) P2 = price per units for product in year 2
5) I1 = Labour (input) time in year 1
6) I2 = Labour (input) time in year 2
7) C1 = Cost per hour of Labour (input) time in year 1
8) C2 = Cost per hour of Labour (input) time in year 2
9) T1 = Period of time in year 1
10) T2 = Period of time in year 2
This software was developed using the java application, and intended to determine the production status of manufacturing industries regardless of the product type. Once the data is extracted in this manner, the cumbersomeness of manual calculation is eliminated by the use of this software and the production status is determined in matter of a second. This makes this software a handy to for manager and business owners.
Below are conditions that must be met or processed by a system to satisfy the specification for this software.
1) Microsoft window 98 or above (Operating System Platform)
2) Browser
3) Java Compiler (JDK 1.5 above)
The hardware requirements are:
1) System with minimum of Pentium II motherboard
2) Minimum of 5 gigabyte of Hard disk drive
3) A good VGA or SVGA monitor
4) Printer.
The software interface designed for the determination of economic productivity of a firm of product is as shown in figure 1. Loading this option and keying in the appropriate data will perform the desired computation and generate the result in no time at all. This interface is designed to display input data such as quantity produced, price per unit, labour time, labour time 2, Time of production, cost per hour of labour and cost 2 per hour of labour and when these data is inputted it yields an output displayed as factor productivity price recovery index cost effectiveness index and then a decision which is either increase in productivity, decrease in productivity or a static productivity.
For the purpose of application of the developed software, the necessary data was collected from Olam Cocoa Processing Company Akure.
The data for applying the APC model was obtained from the accounting/ inventory departments of Olam Cocoa Processing Company Akure (2013/2014) fiscal year. This data made it possible to calculate the deflated values, change ratios, performance ratios and performance contributions. Quantities; prices and/or values of both input and output were obtained. The period selected was 2013 to 2016 fiscal year.
The software interface designed for the determination of economic productivity of a firm of product is as shown in Figures 2-4. When the data in
This interface accepts all required data from
OUTPUT | |||
---|---|---|---|
Basic period (year 1) - 2013 | Current period (year 2) - 2014 | ||
Q1 | 100,000 Kg | Q2 | 100,000 Kg |
P1 | N500 | P2 | N600 |
INPUTS | ||||
---|---|---|---|---|
Labour a | Basic period (T1) - 2013 | Current period (T2) - 2014 | ||
I1 | 2300 Hrs | I2 | 3000 Hrs | |
C1 | N200 per Hour | C2 | N160 per Hour | |
Labour b | I1 | 2500 Hrs | I2 | 2800 Hrs |
C1 | N190 per Hour | C2 | N150 per Hour |
OUTPUT | |||
---|---|---|---|
Basic period (year 1) - 2014 | Current period (year 2) - 2015 | ||
Q1 | 150,000 Kg | Q2 | 200,000 Kg |
P1 | N600 | P2 | N800 |
INPUTS | ||||
---|---|---|---|---|
Labour a | Basic period (T1) - 2014 | Current period (T2) - 2015 | ||
I1 | 3000 Hrs | I2 | 3000 Hrs | |
C1 | N160 per Hour | C2 | N180 per Hour | |
Labour b | I1 | 2800 Hrs | I2 | 3000 Hrs |
C1 | N150 per Hour | C2 | N165 per Hour |
OUTPUT | |||
---|---|---|---|
Basic period (year 1) - 2015 | Current period (year 2) - 2016 | ||
Q1 | 200,000 Kg | Q2 | 250,000 Kg |
P1 | N800 | P2 | N1000 |
INPUTS | ||||
---|---|---|---|---|
Labour a | Basic period (T1) - 2015 | Current period (T2) - 2016 | ||
I1 | 3000 Hrs | I2 | 3100 Hrs | |
C1 | N180 per Hour | C2 | N200 per Hour | |
Labour b | I1 | 3000 Hrs | I2 | 3000 Hrs |
C1 | N165 per Hour | C2 | N180 per Hour |
(PPE-INDICES 2016) was able to indicate an increase in productivity bases in the data inputted.
This interface accepts all required data from
The cost implication for PPE-INDICES, 2016 development is as shown in
From the above cost implication for (PPE-INDICES 2016), a unit cost for the software disk is N100,000.00. Similar software like the SANDVIK CORAMANT-PAYBACK CALCULATOR cost $10. By using (PPE-INDICES 2016) the establishment would have added a 50% economic value.
For the preservation of this software, it is important to:
1) Keep it away from direct sun light.
2) Keep it away from dust.
3) Store in a dry environment.
4) Avoid exposure to magnetic fields.
5) Avoid using on virus infested system.
Results identifies APC model as a suitable model for making strategic decision required for measuring the productivity in manufacturing industries in that it can make use of both financial and non-financial data as input data. The algorithm now was used to develop software (PPE- INDICES 2016) with the capability of calculating the economic productivity of any manufacturing firm. Case study data was used to run PPE-INDICES 2016 and the results gotten were as desired.
The software was able to compute the three performance measuring indicators which are the factor productivity index, price recovery index and cost effectiveness index for the various period under consideration. The results are displayed on a printable software interface as shown in figure 2 to figure 4 in chapter three.
PPE-INDICES 2016 was verified by comparison of manual application result of the model and the results from PPE-INDICES 2016 and were tabulated in
S/n | Description | Estimated Cost (N) |
---|---|---|
1 | Coding, Debugging and test running | 30,000.00 |
2 | Program compilation | 5,000.00 |
3 | Production of installable program | 1,000.00 |
4 | Miscellaneous | 3,000.00 |
TOTAL | 39,000.00 |
recommended handy tool for managers and any other performance decision maker in the manufacturing industry.
From the result as determined with the software, it is easy to see the performance of the company within the selected period. The result for (2013/2014), with 2013 as base year shows that CEI = FPI × PRI i.e. 1.87 = 1.24 × 1.51. This index indicates also that sales revenue is increasing faster than the cost and that the product price increase is more significant than productivity. So there is low productivity.
The indices for (2014/2015) CEI = FPI × PRI i.e. 1.55 = 1.29 × 1.20 indices also indicates that sales revenue is increasing faster than the cost and that the productivity is more significant than the product price increase So there is increase in productivity. The indices for (2015/2016) which is CEI = FPI × PRI i.e. 1.39 = 1.23 × 1.13 also indicate that sales revenue is increasing faster than the cost and that the productivity is more significant than the product price increase so there is increase in productivity (
The graph shows the performance indicators for the various years compared. From the graph it can be seen that period 2013/2014 recorded a low productivity level that is FPI PRI, these shows increase an productivity of the company.
2013/2014 | |||
---|---|---|---|
Performance Indicators | Manual Result | Software Result | Deviation |
FPI | 1.24 | 1.24 | Nil |
PRI | 1.51 | 1.51 | Nil |
CEI | 1.87 | 1.87 | Nil |
2014/2015 | |||
---|---|---|---|
Performance Indicators | Manual Result | Software Result | Deviation |
FPI | 1.29 | 1.29 | Nil |
PRI | 1.30 | 1.20 | Nil |
CEI | 1.55 | 1.55 | Nil |
2015/2016 | |||
---|---|---|---|
Performance Indicators | Manual Result | Software Result | Deviation |
FPI | 1.23 | 1.23 | Nil |
PRI | 1.13 | 1.51 | Nil |
CEI | 1.39 | 1.87 | Nil |
S/n | Year | FPI | PRI | CEI | DECISION | REMARK STATUS |
---|---|---|---|---|---|---|
1 | 2013/2014 | 1.24 | 1.51 | 1.87 | EPI < Pri | Low Productivity |
2 | 2014/2015 | 129 | 1.20 | 1.55 | EPI > Pri | Increases Productivity |
3 | 2015/2016 | 1.23 | 1.13 | 1.39 | EPI >pri | Increases Productivity |
The impetus for this study arose from the review of the literature on importance of productivity measurement in the manufacturing industries. This literature review revealed that productivity can be measured by a profit-linked model, and this model can be set up and computed using this software. American Productivity Center (APC) Model was found to be the most suitable; it allows for measure of both non-financial (indexes) and financial (Naira) during computation. The non-financial and financial measurement allows both line manager and financial manager to use the model for measuring productivity and profitability in the company.
The computation with the model software makes it possible for the manager to get further insight on the performance of the firm in no time at all.
In addition, the literature revealed productivity which is regarded as value addition and quality, which is value enhancement, is the main determinant of competitiveness. To remain competitive, companies need to integrate and synergize both productivity and quality.
Based on the review of literature, the main objectives of this study were formulated as follows: to identify the strategic decision required for measuring the productivity in manufacturing industries, identify and adopting a suitable model for measuring economic productivity in manufacturing industries, develop an algorithm and software that will calculate the economic productivity of any manufacturing industry based on the mathematical relations of the identified model, and then validate the software (PPE IDICES 2016).
In accordance with the aforementioned objectives, a literature study APC model was set up and productivity, price recovery and profitability were computed with the software.
This study could be classified as being successful because a suitable productivity measurement was computed and contribution of profitability were decomposed to productivity and price recover to determine the economic productivity of Olam cocoa processing company Akure.
From the discussion, the result indicates that Olam cocoa processing company Akure experienced a low productivity in 2014 compared to 2013 as the base year. Subsequently, 2015 and 2016 were better years for her as productivity improved.
Akinnuli, B.O., Kalu-Imo, E.C. and Yakubu, A.J. (2018) Computer Aided System for Manufacturing Industries Economic Production Status Determination. Open Access Library Journal, 5: e4133. https://doi.org/10.4236/oalib.1104133