Maintenance refers to the combination of all administrative, technical as well as managerial actions during an item’s life cycles with the aim of restoring or retaining it to a state in which it can still do or perform the function required. The primary aim of maintenance is to assist in the maximization of the availability of production system at minimum cost by reducing the system or equipment’s breakdowns probability (Adhikari, and Ozarska 2018).
In order to ensure that operation of the system is at the condition required while at the same time meeting the targets of production optimally in terms of costs, the management responsible for the activities of maintenance must make decisions which are conscious in regard to the objectives of maintenance as well as the strategies which should be pursued. Good practices of maintenance would take into consideration that the strategies and objectives of maintenance are never determined in isolation but are derived from factors in some ways. Some of these factors include manufacturing policy, company policy as well as other potentially conflicting constraints and demands in the company. The utilization of the maintenance resources is done so that its life of the design, standards of safety among others is met. Among the factors targeted include consumptions of raw materials as well as energy use optimization (Aji, Nadhila, and Sanny 2020).
Maintenance management refers to the organization and direction of the resources so as to control the performance of the industrial plant in various aspects including its availability at specific level. Activities like scheduling , planning, controlling, organizing as well as other maintenance related practices constitutes maintenance management. It will therefore be involving a mixture of techniques and policies that vary from one facility to another. As pointed out by various scholars, the strategy of maintenance depends on various factors including the maintenance goals, the facility’s nature or the nature of the equipment to be maintained, the patterns of the workflow and finally, the environment for carrying out the work.
The activities or actions of maintenance can either be preventive maintenance commonly referred to as PM. In the case of PM, it involves components replacements as pre-specified time using diagnostic or prognostic based on the historical data of failure. Also it can be condition based maintenance commonly referred to as CBM which is based on data from monitoring equipment’s conditions through the use of the techniques of condition monitoring (Alfred, and Bett, 2018).
The strategy of maintenance involves or includes researching, identification as well as execution of various repairs, inspection and replacement decisions. It is basically focusing on the formulation of the unit’s best life plan for the plant as well as formulation of the optimal schedule of maintenance of the plant in coordination with the other concerned functions and production. The strategy of maintenance describes the kind of the events like passing of time, failure as well as condition which would determine what kind of maintenance replacement, repair or inspection would be needed. In order to achieve the above objectives, the processes involved would include a mix of techniques/policies that potentially vary from one facility to another depending on various factors like nature of the equipment or facility to be maintained, the patterns of workflow as well as the environment of carrying out the work. The implementation and identification of the appropriate policies on maintenance will basically allow the managers to avoid what is called costs on premature replacements; maintain stable capabilities of production as well as preventing the system and its component parts deterioration.
In consideration of the maintenance management, there are two aspects of performance within the practice. They include maintenance performance and manufacturing performance. In the assessment of manufacturing performance, the concern or the focus is usually on the flexibility, speed, cost as well as quality. In the case of speed, the focus is usually on the on time and fast delivery. In the case of flexibility, consideration would be given to the factors such as adjustments in the design, the broad product line ability to change the products volume and mix rapidly. In the case of costs, consideration is given to the items like low cost of energy, low cost of production among others. In the case of quality, the focus is on the low rates of defects, durability of the products, quality of the performance as well as the environmental aspects (Barman, Das and De 2021).
The performance of maintenance is concerned with the effectiveness of maintenance as well as the results of the efforts of maintenance with the objective of reducing the cost of maintenance as well as enhancing the reliability of production , quality as well as costs. Some of the maintenance performance measures include mean time between failures which is commonly referred to as MTTF, availability, mean time repair(MTTR), production rate index as well as failure breakdown frequency. The productivity indicators of maintenance usually provide measurement on the resource usage like materials, labour, tools, contractors and equipments. These components also form various indicators of costs like manpower efficiency and utilization, work order and material usage. Maintenance productivity (MP) controls would always ensure that the levels budgeted for the efforts of maintenance are sustained and required output of the plant achieved (Cai et al.2018).
Previously, there have been proposals by the scholars on Overall Equipment Effectiveness commonly referred to as OEE as a measurement method which can help in the understanding of the manufacturing area performance. It is also effective in the identification of the limitation possible present. In the context of OEE, there us calculation of the percentage of effectiveness of the processes of manufacturing. It is further a function which is made up of three major factors including efficiency of performance, the availability of the equipments as well as the quality of the products being produced.
The conditions of doing business are rapidly changing and this trend has been continuous. The markets too have been on the receiving ends when it comes to the needs of the customers which basically relates to the higher quality demands, shorter time in terms if delivery as well as higher service level of the customer coupled with lower prices of the products and services. Similarly, the life cycle of the product is becoming shorter and shorter with time. In any competitive context, success would be dependent on having either a value advantage or cost advantage or in ideal conditions having both. Any business survival would be dependent on its ability to effectively compete (Cai, and Choi 2020). As a result, the companies f manufacturing have had their structures changed from the industries which are intensive in terms of labour to technology-intensive that is to say capital intensive set ups.
Majority of the changes in the companies internal environment are currently taking place including increased use of automation as well as mechanization of the operations such as having flexible systems of manufacturing commonly referred to as FMS, automatic warehousing, robots, automatic guided vehicles (AGVs); just-in-time (JIT) increase trends usage among others. Companies within the processes and the chemical industries like in the case of refineries and paper mills are known to be using fully automated and expensive production lines (Cardoso et al.2020). Also there have been increased in the pressure to have the ecological environment protected from the danger of industrial wastes most of which are very harmful and potential sources of pollutants. All the above highlighted changes coupled with capital investments which implies that there is need to use plants effectively as well as efficiently so that the produced products can be those of high quality besides showing concerns for the safety of the environment.
There is interdependence between manufacturing performance and equipment maintenance. Production disturbance is usually associated with the equipment breakdown. Thos in turn affect the operational performance in terms of the quality of the product, speed of production, cost of production, availability of the plant, conditions of the work, safety, and environment among others. It has been argued severally that the practices of maintenance influence the performance of manufacturing through their impacts on the cost, speed, efficiency as well as quality. A change in the practices of maintenance can significantly affect the same outcomes measures as well as performance of manufacturing. The primary driver of the product’s cost is the productivity.
A review of performance in manufacturing for the Tea Processing factories in the UK for maintenance, prices of auction as well as payment to the stakeholders has shown a wide performance range. The range of energy consumption is from 22mj/kgmt to 47mj/kgmt, the consumption of firewood range from as low as 179 kgmt/cum to as high as 380kgmt/cumf, electrical range of consumption from 1kwh/kgmt to 0.6kwh/kgmt, payment range of the bonus being $ 1.00 per kg. Considering that the Tea Processing units in the UK are usually under the management of a common platform, it would be expected that slight variance exist in their performance. Studies have suggested that there is a very strong relationship between the manufacturing performance and productivity. Productivity is therefore very crucial requirement when it comes to the manufacturing success. Low productivity could be associated with the issues such as technology, skills, and effectiveness of management, environmental or planning factors. Some of the above mentioned factors also have impacts on the management of maintenance.
It is important to note that very little research work has been carried out on the manner in which practices of maintenance influence the performance of performance. There are however studies which have only concentrated on the establishment of the performance framework for maintenance. Scholars have evaluated the role being played by the performance measurement and condition monitoring in the enhancement of the asset productivity. The proposal that followed included the function of maintenance needed to integrate at least five factors so as to achieve costs optimally in relation to repair and upkeep as well as providing reliability in the production line. These are policies, people, practices, equipment as well as evaluation of performance. The line according to some studies have been proposed alongside the systems of maintenance management in which logistics, issues related to the people as well as needs of production are the considered input as well as cost effectiveness, reliability and safety are the primary inputs.
The primary aim of this particular study was to assist in the investigation of the impacts of maintenance on the production. This would go alongside determination of six optimal factors of maintenance. I will thus be answering the primary question’’” Can practices of maintenance bring about improved performance in the manufacturing or production?”
The study’s specific objectives include:
This particular study provides some of the important answers for the Tea Processing Factories Management on the factors which affect their performance in the production line. When the identified factors are properly addressed, it will practically result into the effectiveness and efficiency of the Ta factories hence productivity improvement that would translate to higher payment to the farmers and prices. This particular study would also be very useful to the researchers and academics in the sense that it will form the basis of research work further in the practice of maintenance in the UK’s Tea Industries. This particular study would also provide other players in the Tea industry in the UK with a framework which can be used in the development of maintenance management through which proper strategies that contribute to the production improvement hence success in business in wider economic perspectives.
In the literature section, the review is categorized into two sections, first section contains the general discussion of maintenance operations used to comply with the systems of maintenance. The other section involves the study of maintenance control performance measurement and their effects to tea processing effectiveness in UK. The section as well presents a discussion of “Overall Equipment Effectiveness (OEE)”, and how it helps in Maintenance and Processing operations. The last section of the chapter provides a summary of the literature part and outlines the research hypothesis.
A number of approaches have been discussed by several scholars concerning maintenance of factories. Based on their arguments, there is a pair of maintenance management typically employed by most factories, that is, preventive maintenance and corrective maintenance (Chen 2022).
Corrective maintenance is usually implemented once the fault is identified and it is mainly aimed at setting the equipment into the right condition to realize effective performance. The management mechanism operates under the technique “fix it on breakage”. This type of maintenance is considered to be emergent and involves repairs and remedial operations. Despite the fact that this maintenance approach constitutes to greater percentage of maintenance approaches, it is also the most expensive approach following to high machine downtime, high costs of inventing spare parts, high cost of overtime labor and reduced production. According to the conducted analysis, the cost of corrective maintenance technique is approximately 30% above the cost incurred for preventive maintenance approach.
Preventive maintenance is defined as the type of maintenance that involves components replacement or overhauling of items at set intervals to eliminate unnecessary downtime, thus contributing to repair or corrective measures within the system. This particular maintenance management approach highly depends on time, in which activities are carried out to retain the desired and reliable levels of operations (Chen, Pierobon and Ganguly 2019). Conferring to the information provided by some scholars, there are three major categories of preventive maintenance including;
Repairs together with rebuild of machines are categorized under timed preventive maintenances as per the bathtub curve.
Scientists equally presented discussions on numerous approaches to enhance maintenance operations and these included, Reliability Centered Maintenance (RCM), Computerized Maintenance Management Systems (CMMS), Total Productive Maintenance (TPM) and life cycle costing.
Scientists have provided discussions on TPM approaches within processing factories in UK. The definition given bey the scientist concerning TPM was mainly that the approach was designed with the aim pf improving efficiency of the equipment through implementation of advanced productive maintenance system to monitor the life cycle of the equipment as well as other related factors such as maintenance usage and planning. This could as well be achieved through effective engagement of involved parties right from the top most ranking to the least ranking of employees, which also improves productive maintenance by means of voluntary or motivational group activities. The context intensity falls within the integration of maintenance, production and issues surrounding quality into solving the major challenges facing tea processing companies. Such challenges include reduced speed, production and set up rejects, breakdown and little terminations (Chien and Chi 2019).
According to the scientists, maintenance development originates from corrective maintenance and proceeds to preventive maintenance prior to the introduction of relevant comprehensive mechanisms such as TPM, LCC and RCM. These approaches ensured complete integration of the maintenance system followed by involvement of decision support system such as the Computerized Maintenance Management System (CMMS).
According to the arguments made by scholars, maintenance performance measurement (MPM) is considered necessary for estimating the established value of maintenance, calculating the resultant investment and making revision on resources, allocation, while ensuring the safety and better health of customers and also maintaining better conditions of the surrounding while trying to adapt to the changes in the structure, maintenance and operational approaches. Based on their arguments, the definition of MPM is given as “the multi-disciplinary operations involving justification and estimation of resultant maintenance value and considering betterment of health conditions and safety of stakeholders of the company as per the view of the business.”
Other scholars within their documentations outlined the conjunction definition of maintenance performance and should effectively take into consideration the relationship between maintenance activities and other activities carried out within the company, especially the processing operations (Edlund et al.2018). In this particular dissertation, a conceptual system is suggested that guides decision making process on the indicators of maintenance performance of tea processing factories. The study aims at bridging maintenance targets and processing and corporate activities, and offers a correlation between maintenance operations, maintenance attempts alongside maintenance outcomes. By making reference to this conceptual system the indicators of performance maintenance together with the resultant outcomes are allocated for all the categories. Other scholars presented their arguments that maintenance performance can technically be grouped as either leading or lagging indicators and cost performance.
Conferring to the perception of the scientists, the leading indicators of maintenance are to determine the effectiveness of task operations in order to ensure provision of desirable results. The process of maintenance is addressed in a number of ways including; identification of task (as per the objectives and performance gaps of maintenance), planning and scheduling of work and lastly execution of the given task. For the case of work identification, the estimated factors include the reactive and proactive percentages as well as scope of work advancement, and also estimate the response rate of request for work. The factors to be measures under work planning include responsiveness, quality and intensity of planning. For the case of work scheduling, the factors to be estimated include rate of scheduling realization and quality of scheduling, while for execution phase, the measured factors include compliance of the schedule, Mean Time to Repair, efficiency of the employed manpower, turnover of work order and consumption of manpower (Esposito et al.2020).
By making reference to the analysis carried out by scientists, the cost of maintenance is mostly affected by the efficiency and operability approaches of maintenance. Cost performance measurements include direct cost of repairs, damage extent, scope of maintenance, the percentage cost of supply and personnel.
Other scholars within their research works considered “Overall Equipment Effectiveness” to be the most effective processing and maintenance approach for the realized high profit and is defined as a measurement mechanism that improves the level of understanding on processing and performance issues, and as well to allocate possible influences. OEE evaluates the effectiveness scope of the processing activity. The tool is defined as a function comprising of other factors such as performance efficiency, availability and quality and is calculated by the formula below.
In this equation:
The above factors can be calculated using the equations outlined below:
The implementation of OEE approach should cover bottlenecks among other crucial equipment. Under effective implementation of the approach, with the aim of enhancing and controlling OEE, there will be significant positive changes to the general performance of tea processing factories.
Better knowledge on various ways through which maintenance activities contribute to maintenance performance and their influence to processing efficiency is considered effective for successful establishment and employment of maintenance management framework. This concept is as illustrated by the conceptual framework below.
Figure 1: The conceptual framework (García- and Cardona 2019)
The most basic concern of production is the resultant product of interest and the need for maintenance would be the subsequent concern as far as production and processing operations are concerned. Production therefore involves manufacturing and processing of products while maintenance involves maintaining the quality of the resulting product. Quality of the resultant product is influenced by the level of maintenance and production approach implemented for the entire process. The manner in which maintenance is conducted will as well contribute to the quality, operation safety, cost alongside quantity of the resultant product.
To confirm this within tea processing managed companies in UK, a number of hypothesis were suggested as discussed below:
Maintenance impacts or affects the performance of businesses in various aspects including profitability and productivity. In an interesting illustration, a day’s output that is lost due to the plant’s unplanned stoppage can never be recovered without additional costs incurred that is to say working overtime. Also the customers who seek help elsewhere during such incidences are equally lost in businesses. The maintenance function maintenance has increased as a result of its role in the improvement and keeping of availability, efficiency in the performance, product’s quality, safety requirements, on-time deliveries, protection of the environment as well as total plant cost effectiveness particularly at the higher levels. For instance, the implementations of the concept of JIT within any industrial set up would require very effective maintenance which can guarantee smooth production flow and possibly 100% quality cost effectively (Gunathilaka et al.2018). While it is still considered that maintenance is a cost centre, the studies have indicated the impacts of the same activities as a function of the entire performance of the plant that is to say profitability and productivity. Disturbance in the processes of production as a result of maintenance as well as other causes tend to reduce the processes of productivity, increases the cost of production hence reduced profitability. Any facility or equipment failure not only results into the productivity loss but also timely losses of services to the customers. It can potentially results into the problems related to the environment and safety which are known for the destruction of the image of the company. There is therefore need to demonstrate the manner in which maintenance can potentially contribute to the business objective overally and in the case of the manufacturing sectors the contribution of the same towards increased production.
Maintenance has been considered as evil in the ancient times but it is a centre of profit rather than just unavoidable and unpredictable expense. The questions of the manner in which the practices of maintenance can help in the improvement in the productivity of the company as well as the company’s profitability are worth investigations. Using effective practices of maintenances, it will be possible to significantly reduce failures to the least level possible (Guo et al.2018). This will result in savings in greater amount. The practices of maintenance have been gaining more and more popularity as a result of its role in the long-term profitability of corporate. It has impacts on the manufacturability or production and other aspects of operation like quality, capacity, environment, costs and safety. But as a result of the fact that maintenance is regarded as a support function for the production which implies that it has got indirect impacts as opposed to other functions, it would be very difficult to directly footprint is effects.
The performance of maintenance as perceived would be dependent on the applied perspectives. The top management would be interested only in the performance of the budget while accountants will be thinking of maintenance practices in terms of the costs to be incurred. The production department would be seeing the performance in terms of the availability of the equipments as well as responsiveness when it comes to the required support necessary. As a result, it is evident that a common language is lacking in the organization’s various functions. For instance, the top management would be interested in having technical suggestions (Han, Shim and Kim 2019). In order to overcome this particular obstacle, the translation of problem of maintenance especially in matters of economic measures while linking it with capital return and models of cost-revenue. It is therefore important to establish the relationship between the cooperate profitability and maintenance performance.
Several approaches of maintenance i.e the concepts and strategies have been implemented by the intellectuals and practitioners. The strategy of maintenance involves researching, identification as well as execution of replacement, repair as well as inspection of various component arts. It is focusing on the formulation of the unit’s best life plan for the plant while setting the optimal schedule for the plant’s maintenance in co-ordination with the production as well as other concerned functions. The strategies of maintenance would be describing what kind of events in terms of passing of time, failure and condition which will in turn determine the type of maintenance practice to be adopted i.e. whether it should be replacement, repair or just inspection. The strategy of maintenance is made up of a mix of techniques and policies that also vary from one facility to another. It is dependent on various factors including maintenance goals, nature of the equipment or the facility to be maintained, the patterns of the workflow as well as the work environment (Hossain et al.2019).
A concept of maintenance is defined as a set of various interventions of maintenance say preventive, corrective condition based among others as well as the general structure which guides the foreseen interventions. It is from the concept of maintenance that the framework of the strategies of maintenance is developed. In other words it would be regarded as the embodiment in the manner win which the organization is thinking about the maintenance role as a function of the operation. Some of the maintenance concepts which are readily available in the literature include total productive maintenance (TPM), reliability centered maintenance (RCM), terotechnology, business centered maintenance (BCM), integrated logistic support (ILS) as well as capital asset management (Hossain, Zaini, and Mahlia 2019).
In the recent studies, there have been a lot of research work carried out and established the internal resources as one of the primary drivers of the strategic advantages and firm profitability. Profitability is attributed to the interaction of uncontrollable and controllable factors. Among the factors that are uncontrollable include political and economic environment, growth of the market or decline, the cases of inflation among others. These factors which are regarded as uncontrollable do have the potential of imposing negative or positive impacts significantly on the profitability. The measurement of the impacts should be done in such a manner that it enables knowing of the profit change is attributed to the uncontrollable factors changes or as a result of the factors which are controllable (Jin et al.2019).
The case study was carried out at Twinings Tea Company located in London. This particular research work was carried out at the PM2, one of the four machines of the company. The selection was due to the valuable database especially during the period of study.
In this particular study, the primary objective was to establish the impact of maintenance on the production value of the company. In a specific context, the study was limited to case study illustrations on few organizations including Twinings Tea Processing Companies. This provided an opportunity of achieving an in-depth analysis contextually. Although the two cases have been embraced in this particular study, case studies was considered less desirable research form than experimental or survey as a result of lack of rigor. In fact by nature thee is usually very little basis of generalization scientifically. In this particular study, there was mitigation of the same since Twinings Tea Processing company manage their affairs independently and post varying performance on manufacturing hence attracting prices on auction. This study was therefore an empirical and it was carried through across survey of the departments within company.
The study focused on the factories under the management of Twinings. The survey basically focused on the managers of production as well as regional engineers. The choice of this particular rank was based on the fact that it was occupied by high informants of ranking as considered to be reliable information source as confirmed by the scholars previously. Also the choice of the rank was due to the fact that they are the primary users of the services of maintenance. The sampling unit was at the level of the company and therefore one questionnaire was sent to just an individual respondent for every factory and every region.
Both secondary and primary data were collected in this particular study. The focus of the primary data was gathering information on the application level of various practices of maintenance in various factories. The primary data also meant to focus on the informed factors which are known to be affecting performance of maintenance as well as preferred alternatives to handling disruptions in productions. Secondary data on the other hand was obtained from the recent manufacturing, maintenance and marketing reports. The secondary data gave information on the availability of plants, use of energy, and cost of energy as well as average prices of auction.
Figure 2: Conceptual model showing the impacts of maintenance on the profitability of the company (Kaab et al.2019)
The components of the technical data included parameters such as planned production rate; planned operating time; unplanned stoppage time like short stoppage time; failures and unplanned-but-before-failure replacements (UPBFR) as well as bad quality products. The parameters collected covered all the stoppages types like electrical, mechanical, instruments and hydraulic. In the case of the economic data, the information included variable and fixed costs of operations, margin of the profit, working capital, net profit, costs of maintenance; spare parts inventory as well as investments in maintenance.
In order to boost the rate of response, the research questions were made as clear as possible in a manner that was very precise. There was use of closed questions since as asserted by the previous studies they are quicker as well as easier in answering since they need very minimal writing. Kt is also easier to compare responses since they are predetermined. In the case of the closed questions, ratings, lists as well as quantity question types were used in the questionnaire to assist in the data acquisition. In order to increase the rate of response, the respondents were offered anonymity particularly for those who would prefer confidentiality hence increase in the veracity of the responses.
The first instrument of the survey basically focused on the establishment of the practices of maintenance which are utilized in the Twinings Tea factories. In the second instrument, there was establishment of the primary factors which affect the performance of maintenance. In the third instrument, there was collection of the information on various data on performance for the plant and this included availability of the plant, consumption of thermal energy in terms of MJ/KGMT, consumptions of the electrical energy expressed in terms of KWH/KGMT, consumption of fire wood expressed in terms of KGMT/CUMF, total cost of energy among others. In the fourth instrument, the focus was on the establishment on the manner of overcoming disruptions in the productions.
Data Analysis
A total of 26 responses were obtained from the questionnaires dispatched to the production managers which were also 65. The manufacturing and maintenance data for all the factories were obtained from the reports on the current performance in the month of August. There was analysis of data from the survey through the use of quantitative techniques as well as descriptions written to facilitate the construction and comparison of hypotheses. Pie-charts and histograms were used in the illustration of the contrasts between various factors. In order to test whether there was a correlation or an association between the overall equipment effectiveness and practices of maintenance, there was use of Spearman’s rank correlation. The correlation of spearman was also useful in the testing whether there is correlation between the availability of the plant and the practices of maintenance. Also there was testing of the correlation between the availability of the plant as well as various measurement performance which included MJ/KGMT, KWH/KGMT, KWH/KGMT, CUMF/KGMT, Energy cost /KGMT, as well as Auction price /KGMT. The calculation of the rho of the Spearman was achieved using the formula given below:
In this part of the research paper is a summary presentation of data analysis, study results alongside discussion of the obtained results from the research work. The chapter is divided into sections including presentation of data, analysis of study results and discussion of research findings making reference to the study objectives which was to determine the influence of maintenance to performance of tea processing factories in UK.
Considering that the economic data were very confidential, the data used in this particular analysis were basically transformed through the use of various factors suitable for the sake of accurate analysis. It was demonstrated that about 5.7-5.8% of the working time planned, the stoppages of the machine was due to various reasons such as UPBFR, failures, short stoppages and planned stoppages. The distribution of the total stoppage time was as follows:
The above results have been demonstrated in the figure shown below:
Figure 3: The distribution of the total stoppage time (Kithiia, et al.2020)
From the above figure, it was evident that the short stoppage took a better share of the time of stoppage. Averagely, it resulted from about 1600 short stoppages. The planned stoppages which are regular were about 8hrs each which generally was taking place after every week to allow for the performance of certain tasks operationally. This time of the planned stoppages would results into the creation of opportunity that could be utilized in the performance of the pre-planned tasks of maintenance in the case of the implementation of the policy of maintenance. The estimation was that at least 3.6 million US dollars were generated as profit from the maintenance activities. The unplanned stoppages causes and each reasons’ percentage with respect to the total number of the stoppages unplanned are as illustrated in the figure below:
Figure 4: Unplanned stoppages causes at the Tea Processing Factory in the UK
The estimation of the company on the bad quality production as a result of the causes of maintenance was about 7.5% of the total produced bad quality (Kulawik et al.2019).
In this particular verification process, the illustration targeted here is the manner in which the improvement in the effectiveness of maintenance can potentially result into the company’s productivity based on the provided empirical data hence profitability. From the analysis, it became evident that the actual produced quantity annually during the period of case study i.e. from the January 2022 was Q1 = 176 963 tons. The average selling price being about $3o per ton. The total cost of production averagely remained at TC1=$ 1949 per ton and with the fixed cost per ton being $ 724. The quantity of production averagely lost as a result of unavailability as a result of all stoppages unplanned such as electrical, mechanical as well as hydraulic were equivalent to 3775 tons. The bad quantity production averagely lost as a result of causes related to the maintenance problem was again equivalent to 430 tons. The estimation of this particular figure was as per the experience of the personnel of the company and it stood at 7.5% of the total production of bad quality (Li, et al.2018). This implies that in case there is an ideal implementation of the policy of maintenance which has the capacity of getting rid all the stoppages unplanned as well as all productions which are of bad quality in relation to the problems of maintenance then the new quantity output marked as Q2 would be equivalent to 181 170 tons. Therefore, the new fixed cost for every ton would be equivalent to 707. This would again mean TC2 becoming to about 1932 dollars per ton.
Based on the data mentioned above, the index of productivity was calculated as per the equation 1 and the obtained Q1 was about 1.54. Also the productivity index value showed improvement to 1.55 at the point Q2. This implies that the index of productivity of one of the machines at the Tea Processing Plant that is to say PM2 registered an improvement of 0.014 in the case of the implementation of more effective policy of maintenance. Therefore , the ideal net impact on the orofit of the company without cpnsidering the investment cost can be calculated by using the equations below:
F2 = 181 170ð3050 -1932Þ = 202:6 million Pound Sterling,
F1 = 176 963ð3050 -1949Þ = 194:8 million Pound Sterling,
F2 F1 = 7:8 million Pound Sterling,
This implies that in this particular case study, at least 7.8 million Pound Sterling per year which is equivalent to 12.5% of the maintenance budget of the machine would be gained yearly as a result of the improvement of productivity of the machine in case of the implementation of more effective policy of maintenance. This particular value is likely to increase depending on the manner in which activities of maintenance are linked to other elements affecting the overall equipment effectiveness commonly referred to as OEE that is to say planned stoppages and short stoppages. In order to effectively assess te cost effectiveness of new investment as far as maintenance is concerned, the increments of savings as a result of the achieved output through improvement of the effectiveness of maintenance could be subjected to comparison with the actual investment.
The above result is just a representation of the economic result of an effective maintenance as a result of its impacts on the value of the profit margin. Besides, there are other factors like the lost expenses as a result of not utilizing the elements of fixed costs like idle machine, idle labour among others. In the long, there is a possibility on the other hand to reduce the total cost of manufacturing when using the policy of effective maintenance as a result of the maintenance effect of the elements like those elements which are tied less to capital particularly the raw materials, finished goods, WIP among others. In addition, the prices could be increased or improved as a result of value provision benefits to the end customers such as consistent and on tine delivery (Lavrinenko et al.2019).
The analyzed data was provided by around 25 questionnaires received out of the total number of questionnaires sent to respondents which was 65. Other sources of data were obtained from factories for maintenance as well as manufacturing managers reports. Analysis of these data is given in the following sections.
In order to study the maintenance operations practiced within the tea processing factories in UK, the respondents were expected to provide answers on the total percentage of the type of maintenance operations conducted within their companies.
Figure 5: Chart showing the percentage responses given by respondents on maintenance practices within factories in UK(Mila et al.2021)
As can be observed in the chart above, 24 percent of maintenance is for breakdown while other forms of preventive maintenance practices account for the remaining percentage.
In order to determine the impact of maintenance practices on the performance of tea factories in UK, data on breakdown maintenance, preventive maintenance alongside availability was presented in a scatter diagram as shown in the figure below.
Figure 6: Scatter diagram showing the influence of maintenance activities on breakdown maintenance, plant availability and preventive maintenance.
For effective study to further determine the impact of preventive maintenance operations on the general performance of equipment, the use of spearman rank correlation was implemented. From the obtained results, correlation was found to be positive 0.18 with P = 0.34 at . t-Test was calculated to be 0.89 at a critical t value of 1.7.
The spearman’s rank correlation was as well used to determine the impact of breakdown maintenance activities to the operability of the equipment. In this case, the obtained results indicated a negative correlation of -0.18 with P = 0.4 at . t-Test was calculated to be -0.89 at a critical t value of 1.7.
Research question in this section mainly targeted at determining the most crucial factors influencing maintenance performance.
Figure 7: Contributing factor to maintenance performance
From the graphical analysis presented by the above figure, it can be observed that most of the common factors contributing to maintenance performance include technical competence, work design, individual capabilities and allocation of resources.
Secondary data was collected with the aim of evaluating the present scopes of different measurements of performance. The collected data was mainly on the cost management and price of tea within the country and the average value for all the areas under study were presented depending on performance of the industry.
Table 1: The findings outline wider scopes of performance for the seven regions analyzed
|
Region |
Energy consumption Mj/KgMt |
Firewood consumption KgMt/CUM |
Electric power consumption Kwh/KgMt |
Cost intensity £/KgMt |
Availability |
Average auction price |
|
Target |
25 |
350 |
0.55 |
0.10 |
0.85 |
|
|
Region 1 |
39.01 |
222.38 |
0.80 |
0.70 |
||
|
Region 2 |
44.21 |
191.53 |
0.90 |
0.65 |
||
|
Region 3 |
39.31 |
223.14 |
0.69 |
0.66 |
||
|
Region 4 |
30.88 |
282.66 |
0.90 |
0.82 |
||
|
Region 5 |
38.20 |
220.32 |
0.61 |
0.62 |
||
|
Region 6 |
40.61 |
209.12 |
0.70 |
0.66 |
||
|
Region 7 |
36.84 |
232.00 |
0.85 |
0.60 |
In order to determine how maintenance outlined by the availability of plant affects operations of manufacturing, calculations were done including the spearman rank correlation for the seven regions of manufacturing. The obtained results were as given in the table below.
Figure 8: Scatter graph for the performance of study regions alongside plant availability
From the graph, it can be seen that there is an increasing trend in the availability of plant with persistent decrease in cost of energy.
Figure 9: Scatter graph for the average auction price and availability of plant
From the graph, it is clear that there is an increasing trend in availability of plant together with yield of firewood with the average value of auction price retained at constant for the region.
The obtained results were closely in relation to those of spearman’s rank correlation as given below.
Table 2: spearman’s rank correlation
|
OEE against Bonus Payment |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Incorrected correlation |
0.0507 |
(0.69) |
(0.61) |
||
|
Corrected correlation |
0.03031 |
(0.70) |
(0.64) |
0.30 |
0.05 |
|
t-test (n>10) |
0.096121 |
(3.29) |
(2.39) |
0.30 |
0.05 |
|
Scope of freedom |
10 |
10.00 |
10.00 |
10.00 |
10.00 |
|
Critical 2-sided T-value (5%) |
2.198 |
2.20 |
2.20 |
2.20 |
2.20 |
|
Critical 1-sided T-value (5%) |
1.794 |
1.801 |
1.801 |
1.801 |
1.801 |
|
D-square value (estimated) |
272.01 |
487.50 |
450.00 |
207.50 |
299.00 |
|
D-square value (anticipated) |
285 |
285.50 |
285.50 |
285.50 |
285.50 |
|
Standard deviation |
85.992 |
86.01 |
86.01 |
86.01 |
86.01 |
|
z-test |
-0.17024 |
2.29 |
2.00 |
(0.89) |
0.16 |
|
Probability |
0.842 |
0.03 |
0.07 |
0.31 |
0.86 |
|
Observations |
11 |
11.00 |
11.00 |
11.00 |
11.00 |
For the first region, the relation was observed to be positive between auction prices and availability of plant as well as firewood usage. However, the results obtained for the cost of energy and availability of plant showed a negative correlation.
Figure 10: Scatter graph for the relationship between plant availability and cost of energy for region 2
From the figure above, it can be observed that there is an increasing trend in plant availability, decrease in quantity with a constant cost of energy.
Figure 11: Scatter diagram representation for the correlation between average auction price, availability of plant and total firewood yield
The graph depicts an increasing trend in plant availability, firewood yield and average auction price.
The obtained results were then linked to spearman’s rank correlation and the resultant findings were as given in the table below.
|
Statistics |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Incorrected correlation |
0.69 |
0.24 |
-0.27639 |
0.69 |
0.31 |
|
Corrected correlation |
0.684215 |
0.24 |
-0.3923 |
0.69 |
0.32 |
|
t-test (n>10) |
2.299213 |
0.61 |
-1.192453 |
2.69 |
1.03 |
|
Scope of freedom |
7 |
7.00 |
8 |
7.00 |
7.00 |
|
Critical 2-sided T-value (5%) |
2.321 |
2.42 |
2.29 |
2.35 |
2.35 |
|
Critical 1-sided T-value (5%) |
1.901 |
1.89 |
1.85 |
1.89 |
1.89 |
|
D-square value (estimated) |
35 |
146.50 |
215 |
35.00 |
160.50 |
|
D-square value (anticipated) |
120 |
120.00 |
160 |
120.00 |
120.00 |
|
Standard deviation |
42.44126 |
41.97 |
54 |
41.97 |
41.97 |
|
z-test |
-1.9578 |
0.599 |
0.900125 |
1.97 |
0.968 |
|
Probability |
0.0492 |
0.52 |
0.3701 |
0.04 |
0.31 |
|
Observations |
9 |
9.00 |
9.00 |
9.00 |
9.00 |
Spearman’s rank correlation for different performance measurements against availability of plant for region 2
The correlation type observed between plant availability firewood consumption as well as average auction price was positive, while for the correlation between cost of energy, power and availability of the plant, the value was negative.
Figure 12: Scatter diagram for the relationship between plant availability, cost of energy and firewood consumption within the factory for region 3
From the figure above, there is an observed increase in trend for the availability of plant and firewood consumption, while energy usage decreases and the cost of energy is retained at a constant value.
Figure 13: Scatter diagram for the relationship between plant availability, average auction price and firewood consumption for region 3
From the graph, it can be observed that there is a trend in the increase of both plant availability and total production, while the average auction price is retained at a constant value.
Further analysis of the results was conducted with the help of spearman’s rank correlation and the findings tabulated as shown below.
|
Statistic |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Incorrected correlation |
0.60956 |
0.50 |
0.46 |
0.50 |
0.13 |
|
Corrected correlation |
0.60721 |
0.50 |
0.46 |
0.50 |
0.13 |
|
t-test (n>10) |
0.49 |
1.27 |
1.49 |
||
|
Scope of freedom |
6 |
6.00 |
6.00 |
6.00 |
6.00 |
|
Critical 2-sided T-value (5%) |
2.452 |
2.45 |
2.45 |
2.45 |
2.45 |
|
Critical 1-sided T-value (5%) |
1.942 |
1.94 |
1.94 |
1.94 |
1.94 |
|
D-square value (estimated) |
32 |
127.50 |
122.00 |
39.50 |
95.50 |
|
D-square value (anticipated) |
84 |
84.00 |
84.00 |
84.00 |
84.00 |
|
Standard deviation |
31.7526 |
31.75 |
31.75 |
31.75 |
31.75 |
|
z-test |
-1.64267 |
1.42 |
1.19 |
1.42 |
0.37 |
|
Probability |
0.103 |
0.18 |
0.24 |
0.18 |
0.69 |
|
Observations |
8 |
8.00 |
8.00 |
8.00 |
8.00 |
Table showing spearman’s rank correlation for different performance measurement against availability of plant for region 3
The results obtained showed a positive correlation between firewood consumption cost of energy and availability of plant, while for the relationship between cost of energy and plant availability the value was seen to be negative.
Figure 14: Scatter diagram showing the relationship between plant availability, cost of energy and consumption
The findings in this case outlined an increasing trend in plant availability and energy consumption while there was a declining trend in the cost of energy.
Figure 15: Scatter diagram for the auction price plant availability and energy consumption for region 4
From the graphical presentation above, there is an observed increasing trend in firewood yield, plant availability as well as average auction price.
Further analysis of the results was conducted with the help of spearman’s rank correlation and the findings tabulated as shown below.
|
Statistic |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Incorrected correlation |
0.77598 |
0.48 |
0.17 |
0.40 |
0.40 |
|
Corrected correlation |
0.77271 |
0.48 |
0.17 |
0.40 |
0.40 |
|
t-test (n>10) |
2.97163 |
1.59 |
0.46 |
1.19 |
|
|
Scope of freedom |
6 |
6.00 |
6.00 |
6.00 |
6.00 |
|
Critical 2-sided T-value (5%) |
2.449 |
2.45 |
2.45 |
2.45 |
2.45 |
|
Critical 1-sided T-value (5%) |
1.941 |
1.94 |
1.94 |
1.94 |
1.94 |
|
D-square value (estimated) |
18.7 |
129.50 |
99.50 |
47.50 |
120.00 |
|
D-square value (anticipated) |
84 |
84.00 |
84.00 |
84.00 |
84.00 |
|
Standard deviation |
31.74572 |
31.75 |
31.75 |
31.75 |
31.75 |
|
z-test |
-2.05976 |
1.47 |
0.59 |
1.14 |
1.14 |
|
Probability |
0.0393 |
0.17 |
0.59 |
0.27 |
0.27 |
|
Observations |
9 |
9.00 |
9.00 |
9.00 |
9.00 |
Table showing spearman’s rank correlation for different performance measurement against availability of plant for region 4
The results obtained showed a positive correlation between firewood consumption cost of energy and availability of plant, while for the relationship between cost of energy and plant availability the value was seen to be negative.
Figure 16: Scatter diagram for the auction price plant availability and energy consumption for region 5
From the graphical presentation above, there is an observed increasing trend in firewood yield, plant availability as well as average auction price while the cost of energy reduces.
Figure 17: Scatter diagram for the auction price, plant availability and energy consumption for region 5
From the graphical presentation above, there is an observed increasing trend in auction price with plant availability as well as production of firewood.
Further analysis of the results was conducted with the help of spearman’s rank correlation and the findings tabulated as shown below.
|
Statistic |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Non-corrected correlation |
0.279642 |
0.06 |
0.19 |
0.06 |
0.59 |
|
Corrected correlation |
0.277943 |
0.06 |
0.19 |
0.06 |
0.60 |
|
t-test (n>10) |
0.879723 |
0.17 |
0.61 |
0.17 |
2.54 |
|
Scope of freedom |
10 |
10.00 |
10.00 |
10.00 |
10.00 |
|
Critical 2-sided T-value (5%) |
2.199 |
2.20 |
2.20 |
2.20 |
2.20 |
|
Critical 1-sided T-value (5%) |
1.809 |
1.81 |
1.81 |
1.81 |
1.81 |
|
D-square value (estimated) |
203.61 |
299.50 |
341.50 |
271.50 |
461.50 |
|
D-square value (anticipated) |
286 |
286.00 |
286.00 |
286.00 |
286.00 |
|
Standard deviation |
86.19952 |
86.20 |
86.20 |
86.20 |
86.20 |
|
z-test |
-0.94872 |
0.18 |
0.67 |
0.18 |
2.01 |
|
Probability |
0.341 |
0.85 |
0.54 |
0.85 |
0.03 |
|
Observations |
12 |
12.0 |
12.0 |
12.0 |
12.0 |
Table showing spearman’s rank correlation for different performance measurement against availability of plant for region 5
The results obtained for region 5 indicated a positive correlation between firewood consumption cost of energy and availability of plant, while for the relationship between cost of energy and plant availability the value was seen to be negative.
Figure 18: Scatter diagram for the energy price, plant availability and energy consumption for region 6
From the graphical presentation above, there is an observed increasing trend in energy price with plant availability wile production of firewood decreases.
Figure 19: Scatter diagram for the average auction price, plant availability and energy consumption for region 6
From the graphical presentation above, there is an observed increasing trend in average auction price with plant availability and at the same time production of firewood increases.
The obtained results above are supported by spearman’s rank correlation and is summarized in the table below.
|
Statistic |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Non-corrected correlation |
0.270263 |
0.74 |
0.68 |
0.78 |
0.03 |
|
Corrected correlation |
0.257296 |
0.74 |
0.68 |
0.78 |
0.04 |
|
t-test (n>10) |
0.829343 |
3.26 |
2.79 |
3.70 |
0.13 |
|
Scope of freedom |
10 |
10.00 |
10.00 |
10.00 |
10.00 |
|
Critical 2-sided T-value (5%) |
2.199 |
2.20 |
2.20 |
2.20 |
2.20 |
|
Critical 1-sided T-value (5%) |
1.809 |
1.81 |
1.81 |
1.81 |
1.81 |
|
D-square value (estimated) |
207 |
491.50 |
473.50 |
70.00 |
299.00 |
|
D-square value (anticipated) |
286 |
286.00 |
286.00 |
286.00 |
286.00 |
|
Standard deviation |
86.19721 |
86.20 |
86.20 |
86.20 |
86.20 |
|
z-test |
-0.90023 |
2.40 |
2.16 |
2.49 |
0.13 |
|
Probability |
0.37002 |
0.02 |
0.03 |
0.01 |
0.86 |
|
Observations |
12 |
12.0 |
12.0 |
12.0 |
12.0 |
Table showing spearman’s rank correlation for different performance measurement against availability of plant for region 6
The results obtained for region 6 indicated a positive correlation between firewood consumption, cost of energy and availability of plant, while for the relationship between cost of energy and the total amount of energy value was seen to be negative.
Figure 20: Scatter diagram for the cost of energy, plant availability and energy consumption for region 7
From the graphical presentation above, there is an observed increasing trend in average power consumption with plant availability while the cost of energy reduces.
Figure 21: Scatter diagram for the average auction price, plant availability and energy consumption for region 7
From the graphical presentation above, there is an observed increasing trend in average auction price with plant availability and also the firewood yield increases.
|
Statistic |
Auction prices against availability |
MJ against availability |
Cost against availability |
KGMT/CUMF against availability |
KWHT/KGMT against availability |
|
Non-corrected correlation |
-0.7 |
0.7 |
0.30 |
1.00 |
0.20 |
|
Corrected correlation |
-0.7 |
0.7 |
0.30 |
1.00 |
0.20 |
|
t-test (n>10) |
|||||
|
Scope of freedom |
2 |
2.00 |
2.00 |
2.00 |
2.00 |
|
Critical 2-sided T-value (5%) |
4.297 |
4.30 |
4.30 |
4.30 |
4.30 |
|
Critical 1-sided T-value (5%) |
2.892 |
2.90 |
2.90 |
2.90 |
2.90 |
|
D-square value (estimated) |
18 |
18.00 |
14.00 |
– |
8.00 |
|
D-square value (anticipated) |
10 |
10.00 |
10.00 |
10.00 |
10.00 |
|
Standard deviation |
5.76978 |
5.77 |
5.77 |
5.77 |
5.77 |
|
z-test |
1.384956 |
1.37 |
0.67 |
1.71 |
0.38 |
|
Probability |
0.15987 |
0.15 |
0.47 |
0.07 |
0.71 |
|
Observations |
4 |
4.00 |
4.00 |
4.00 |
4.00 |
Table showing spearman’s rank correlation for different performance measurement against availability of plant for region 7
The results obtained for region 7 indicated was quite different from other results. The region showed a negative correlation for the auction price and positive correlation for both energy and firewood consumption.
The research question given above mainly targeted at establishing resolution measures for tea processing companies to the challenges affecting the process of production.
Figure 22: Outlined resolutions for production challenges
According to the preferences of the respondents, large product inventory, buffer stock alongside redundant machines investment were observed to be of greater weights.
By making reference to the above section of data presentation and analysis, various observations were made. It has clearly been presented out by the analysis that plant availability and maintenance operations are in close relationship as determined by the number of equipment taken through preventive maintenance. The critical value t was calculated to be within the range of 0.89 and 1.7 for a single side test at a = 0.05 for the total number of responses received. Therefore, the report by null hypothesis in not entertained and it is realized that maintenance operations play major role as far as maintenance performance is concerned as depicted by plant availability.
Consumption of energy and the general equipment for the study were observed to have a negative correlation for all the regions. The values of t were all calculated to be less than the critical value which was a = 0.05 for different tea processing companies within every region. Therefore, the report by null hypothesis in not entertained and it is realized that maintenance operations play major role as far as maintenance performance is concerned as depicted by plant availability.
Cost of energy and the general equipment are as well shown by the study to bear a negative correlation for six regions with the value t calculated to be less than the critical index value of 0.05. In this case, the report by null hypothesis in not entertained and it is realized that maintenance operations play major role as far as maintenance performance is concerned as depicted by the cost of energy.
For the case of processed tea, the amount for every cubic meter of firewood alongside general effectiveness of the equipment, the analysis results depicted a positive correlation for all the regions and the value t was calculated to be less that the critical value index. In this case as well, the report by null hypothesis in not entertained and it is realized that maintenance operations play major role as far as maintenance performance is concerned as depicted by the amount of tea processed by the factory for every cubic meter of firewood.
The general efficiency of the equipment and average auction price showed a positive correlation for the first six regions with only the last region showing a negative correlation for the two factors. For the companies where correlation was identified to be positive, the value t was calculated to be less than the critical index value of 0.05. And in the similar case, the report by null hypothesis in not entertained and it is realized that maintenance operations play major role as far as processing activities are concerned based on the amount of auction price paid to workers.
Summary, Conclusions and Recommendations
This section mainly provides a summary of the analysis results by making reference to the study objectives. The conclusion made from the research work is as well presented in this chapter alongside limitations, policy recommendations and suggestions for extensive data search.
According to the conducted research work, it was discovered that most of the conducted maintenance operations within tea processing factories in UK were preventive maintenance accounting for 76%, while breakdown maintenance within the factory constituted for the 20% as illustrated by the chart above. According to the analysis, it is found that tea processing companies in UK have wider ranges of plant availability in the order of 46% to 87% for the least and maximum ranges respectively. The research results as well outline that plant availability and conducted breakdown maintenance are in positive correlation.
The study equally outlines that auction prices, consumption of firewood and availability of plant were all in positive correlation for most of the regions. The case was however different for the correlation between cost of energy, quantity of tea processed and energy consumed which was negative.
The study has also outlined some of the major factors affecting maintenance performance and these include technical knowledge, capabilities at personal levels, allocation of resources and work design. According to the respondents, investment in redundant machines and establishment of buffer stocks were the most effective resolutions to challenges facing tea processing activities in UK.
Conclusion
Conclusion was drawn after making thorough analysis of the collected data and its was discovered that there is need for tea processing companies have to enhance plant availability through advancing on maintenance operations in order to help lower the amount of thermal energy consumed by the process, increase the total processing output for every cubic meter of firewood and to lower the amount of electric power required by the entire process.
For this particular objective to be achieved by tea processing industries in UK, it is the role of factory managers to monitor and at the same time regulate preventive maintenance activities. It is also the role of tea processing company to provide solution to the limitations hindering maintenance performance such as technical competence, work design, allocation of resources and capabilities at personal levels.
The responsibility assigned to maintenance in establishing plant availability has been verified by the study to be effective in the processing performance tea factories. Recommendations are however laid down that there is need for further improvement in performance measurement to account for processing efficiency as well as quality of output, the general performance of the equipment is calculated by multiplying the product of availability and production with quality rate. Further recommendations are laid that the parameters below to be used as reference as proposed by some scientists.
Less than 65% regarded to be unacceptable,
Between 65% and 75% considered satisfactory,
Between 75% and 85% is considered to be good,
Between 85% and 90% is termed to be very good, and
90% and above is termed as world class or exceptional performance.
Some of the major limitations which were encountered during the research process included limited number of responses received from the total number of questionnaires released. This was as outlined by the present data mining reports on performance. The research was conducted in a cross-sectional manner and considering the fact that production process of tea is influenced by changes in climatic conditions such as weather, exchange rate etc., it was very difficult for the research to determine the influence of seasonal changes on the maintenance and processing operations of tea. The research was also limited only to tea processing managed companies.
Recommendations are laid down that by use of the present levels of plant availability as the reference point, longitudinal research is conducted to include changes in climatic conditions impacting on the processing activities of Tea alongside maintenance performance in UK. Plant availability enhancement can thus be conducted and regulated by making reference to the present baseline.
Suggestions are also made that various studies with similar objectives be conducted within other factories different from tea processing companies to provide relational performance data for tea processing factories.
It is further suggested that extensive research activities be conducted in sectors different from the common correlation analysis. This was as outlined by the difference posed by region 7. Annual analysis of data was equally recommended for every tea processing company to determine whether the generalized system applies for all the factories or not.
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