Lean Performance Evaluation System Based on Extrapolated Financial data, Case Study

Abstract

The primary sector, especially the operation of dairy farms, plays a prominent role in the food supply chain. In this research, a controlling model was presented, which is able to evaluate the performance of companies based on financial data related to lean management. The innovation of our research is that it provides a unified framework for the identification and interpretation of financial indicators supporting lean management. Within the framework of the study, we paid particular attention to identifying financial indicators that are easily accessible, understandable and applicable for dairy farms. During the development of the model, simple implementation and practical usability were key aspects, so that the model provides real value to dairy farms. Among our results is the development of a linear extrapolation model, which enables forecasts of the company’s financial performance. A further research opportunity is the exploration of direct relationships between lean management and financial effectiveness. Our research contributes to studies on the financial indicators of lean management. Due to its simple implementation and practical applicability, the presented controlling model is a valuable tool for dairy farms. The application of the model can help further increase the efficiency of the sector and support the continuous improvement of the financial performance of companies.

Introduction

Intense global competition in the 21st century places significant pressure on agricultural organizations to maximize the sources of competitive advantage available to them. Increasing the efficiency of the organizational processes of agricultural activities can be one of the most important tools for improving competitiveness. The lean philosophy appeared a few decades ago as a kind of universal success formula, but the 2019 COVID-19 crisis also brought to the surface some of the disadvantages of the lean philosophy. Among these disadvantages, the most critical are the minimization of stocks and the pulling system, which during the crisis posed serious challenges during the disruption of global supply and transport chains. As a result, the procurement of raw materials and semi-finished agricultural products generated significant additional costs and in some cases became impossible. After the crisis, the increase in global demand further aggravated this situation, which resulted in the cessation of production in some agricultural sectors due to the limited availability of raw materials (McMaster et al., 2020).

The lean philosophy, like other business and management principles and philosophies, has many advantages and disadvantages. In order for an agricultural organization to achieve a long-term sustainable competitive advantage, it may be necessary to selectively apply the tools and methods of different concepts and philosophies. However, the combination of these tools and methods can only result in a long-term competitive advantage if they are able to adapt to changes in the macroeconomic environment. In order to monitor the insular application of lean management, controlling systems are needed that can be integrated into modern agricultural company management systems and can provide aggregated feedback. In recent years, IT and economic IT development, especially the use of Big Data and digitization, created new databases and information opportunities for agricultural organizations, which fundamentally changed traditional controlling systems (Hazen et al., 2014). In this way, agricultural enterprises can adapt more effectively to macroeconomic changes.

Data mining methods for the analysis of large amounts of data, as well as the application of various mathematical-statistical models, enable the transformation of data into relevant information and the extraction of relevant information (Tabesh et al., 2019). Controlling systems must form a bridge between strategic, functional and operational goals, as well as the mentioned mathematical-statistical and data mining methods (Otley, 1999). The effective definition and structuring of Key Performance Indicators (KPI) can help establish a connection between strategic goals and data sets (Fanning, 2016). The correct definition of lean KPIs and their efficient structure can thus serve as an essential element of a modern lean controlling system in the agricultural context, since lean methods and processes appear as islands in the operation and organizational processes of agricultural organizations. Consequently, measuring the effectiveness of lean management tools (kaizen, JIT, KANBAN, VSM, etc.) is a challenge for agricultural organizations. However, the lean controlling models derived by the innovative economic informatics methods and the correct definition of lean KPIs enable the evaluation of the efficiency of lean processes operating in an island-like manner, as well as the efficient creation of an agricultural lean index (Tekez – Taşdeviren, 2020). This adaptation of lean controlling systems can thus support the sustainable competitiveness of agricultural enterprises, their ability to respond quickly, increase operational efficiency and reduce costs.

However, these innovations do not stop at the technological development. In order for agricultural enterprises to get the most out of lean controlling systems, it is important to improve the quality of data, further develop decision-making processes and train employees. It is important to keep in mind that lean controlling systems are only tools, and their use will only bring the expected results if they are used correctly. It is especially important for agricultural enterprises to develop continuously by applying lean controlling, by experimenting and constantly correcting errors. Lean processes and tools, such as Kaizen, JIT, KANBAN, VSM, and others, alone are not sufficient for lean controlling success (Venkat Jayantha et al., 2020). In order for agricultural enterprises to gain a sustainable competitive advantage in the long term, it is important to continuously adapt and refine lean management, according to the specific needs of the enterprise.

So it can be stated that the application of lean controlling in agricultural enterprises is not only important in terms of improving efficiency and reducing costs, but can also play a key role in increasing the long-term competitiveness and sustainability of enterprises (Zhang et al., 2021). The application of innovative economic informatics methods and lean KPIs, as well as the development of lean controlling models to evaluate the efficiency of island-like lean processes and to demonstrate the agricultural lean index, can promote the success of the agricultural sector in the global competitive market of the 21st century. The present research pays particular attention to a specific agricultural sector: dairy farms. The focus of our research is a dairy farm that actively uses lean management methods and mainly measures lean performance using financial Key Performance Indicators (KPIs).

Our primary goal in the research is to discover which financial KPIs are best suited to effectively measure and evaluate lean performance in this specific area. For this purpose, we analyze in detail the financial KPIs used by the examined company. During our research, however, we do not only examine KPIs in themselves, but also discuss how they are related to lean management and to what extent they affect lean performance. At the end of the research, we will evaluate the financial KPIs that contribute the most to the measurement and evaluation of lean performance. The secondary goal of the research is to create a model based on financial KPIs and to help measure and evaluate lean performance. Such a model can help not only the examined company, but also other dairy farms to evaluate and optimize the application of lean management more effectively. In addition, the model can contribute to the spread and effective application of lean management in the agricultural sector in general, and especially in dairy farms.

Materials and methods

We conducted our research based on qualitative interviews and case study methodology. Qualitative methodology is a frequently used tool in organizational research (Bryman, 1992), but this does not mean that this methodology is generally or uniformly applied. It is a fact that several researchers, such as Eisenhardt (1989) and Yin (1994), emphasize theory building as the main goal of the case study methodology. However, according to Bryman (1992), case studies used in organizational research should focus on the detailed exploration and understanding of the local context. Stake (1994) separates qualitative and non-qualitative case studies and emphasizes that the purpose of a qualitative case study is to understand a given case as much as possible. At the heart of this is the question „What can we learn from a single case?” According to this approach, generalization is not the primary goal, but if it becomes necessary, generalization based on a detailed analysis of a single case is considered more reliable than one based on many cases. We built our research along these principles, in which the case study methodology provided an opportunity to gain an in-depth understanding of the lean performance measurement of the investigated dairy company and the role of financial KPIs in this process. During the research, we carried out a series of qualitative interviews with managers and specialists, which allowed us to gain an in-depth understanding of the company’s internal processes and measurement practices. Through these interviews and further data collection, we gained a thorough insight into the evaluation of the company’s lean performance and the use of financial KPIs, which allowed us to apply our results in the model developed during the research.

When interpreting a case study, it is crucial to have a clear understanding of what the term „case” means. According to Stake (1994), a case is a „system with boundaries”, which can be an organization, a group, or even an individual. Essentially, in his definition, the case is a specific, unique, delimitable and integrated system. However, according to Bryman (1992), the concept of the case allows for a much broader interpretation. By „case” or examined unit, he also understood given locations, events and actions, as well as persons. It is important to mention that in the case of qualitative research, the investigation is often directed at several levels, which are not significantly separated from each other. During our research, when we used the concept of „case”, we took into account the approach of both Stake (1994) and Bryman (1992). On the one hand, within the framework of the case study, the case is a dairy company, which is a system with boundaries. On the other hand, following the broader interpretation of Bryman (1992), we considered not only the company as the „case”, but also its specific events and persons. This allowed us to gain in-depth insight into the company’s internal processes, and within these processes to examine in detail the practice of lean performance measurement and the application of financial KPIs.

The sampling principles of qualitative research differ significantly from those of quantitative research, and this difference can be observed primarily in the research objectives. While the goal of generalization in quantitative research refers to a predetermined population, in the case of qualitative research, the generalization is aimed at a concretely observed phenomenon, context, or a theoretical-conceptual framework (Bokor, 1999). During qualitative research, sampling is not tied to pre-formulated expectations. Most of the time, the theoretical aspects designate only the initial phase of the research, by examining the first or second case (Gelei, 2002). As the research progresses, the process is focused on the research objectives and the first analysis results.

During sample selection, the researcher has the freedom to rely on his intuitions, implicit knowledge, and personal expertise (Kvale, 1996). Applying this principle to our own research, the selection of the dairy farm that formed the basis of the case study was not only based on predetermined criteria, but also on our personal experience and expertise. Based on the phenomena we investigated, the application of lean performance measurement and financial KPIs, we developed our sampling strategy and used these analysis results to advance the research.

The company we examined was a dairy farm with 520 Holstein Friesians operating in Csongrád County in Hungary. The main interviewees were the company’s senior managers and financial-controlling specialists.

Results

The examined enterprise does not define lean goals and does not apply classic lean management tools or methods, yet in many cases the manifestation of the lean philosophy can be observed in its activities. The main objective of the company is profitable operation, which cannot be considered a lean goal in itself, however, in order to achieve it, several lean goals must be met. Among these sub-goals are, for example, the establishment of the appropriate farm size, the production of a high-quality product, the optimization of the feed stock and the efficient utilization of by-products and twin products. These factors all mean lean principles such as continuous improvement, loss reduction and focusing on value creation. All of this indicates that the company’s activities, although not directly, are indirectly in line with the lean philosophy.

The company’s corporate management system typically focuses on financial indicators, which are entered into the management system in aggregated form. The measurement of indicators and the content of information are determined by the company using simpler mathematical and reporting operations. The data required for these measurements are obtained from the farm management system and from the accounting ledger analytics. The finance department processes this data and inserts it into the management system. The KPI indicators defined in the management system and their calculation methods are based on predefined calculation methods. However, it is important to note that the information content of the aggregate indicators is not necessarily complete, and the logical relationships of the various KPIs are not always displayed. This is important because in order to measure and evaluate the effectiveness of lean management, it is essential to accurately define and evaluate the relationships between financial and lean indicators. In our research, we therefore placed a lot of emphasis on revealing these relationships and identifying financial indicators that can effectively measure the effectiveness of lean management in the company.

To understand the connections, we analyzed the operation of the enterprise in detail, including production processes, feed stock management, product quality control, and the utilization of by-products and co-products. The purpose of our analyzes was to determine, from the available data, which financial indicators best reflect the effectiveness of lean management in these areas.

Based on all of this, we came to the conclusion that lean indicators are not directly applied in the company’s management system, yet the effectiveness of lean management can be effectively measured through financial indicators. On the one hand, this suggests that the relationships between lean management and financial indicators are much deeper and more complex than it appears at first. On the other hand, it also indicates that the measurement and evaluation of the effectiveness of lean management is not limited to the indicators often mentioned in the lean literature, but also covers financial indicators. This recognition opens up new perspectives in measuring and evaluating the effectiveness of lean management. By including financial indicators in the measurement of lean performance, we can get a much more comprehensive view of the impact of the company’s lean activities. This enables us to verify whether the lean activity really contributes to the company’s financial results and thereby supports the objective of the company’s profitable operation. In addition, this approach can bring additional benefits to the business. The use of financial indicators in measuring lean performance can enable the enterprise to more effectively monitor the financial effects of lean activities and thereby make more accurate estimates of future financial results. In addition, the use of financial indicators in the measurement of lean performance can contribute to improving the internal communication of the enterprise, since financial indicators are generally easier to interpret and understand at different levels of the enterprise than classical lean indicators.

Corporate controlling system

Using general KPI tables in the controlling system, the financial indicators are systematically presented. Tables can be used to illustrate the pre-defined indicators of each financial function, and also to give a comprehensive view of the company’s financial performance. The controlling experts continuously fill this tables with data and calculate the values, which are summarized in monthly and quarterly reports and the trends of the most important indicators are reported to the management.
The specific indicators of the various financial functions and units are regularly analyzed and evaluated by the controlling and financial managers. This analysis occurs periodically, usually on a monthly or quarterly basis, and plays a central role in evaluating a company’s financial performance and strategic decision-making. By monitoring the development of indicators, the company is able to identify potential intervention points and proactively react to problems, thus contributing to the company’s financial effectiveness. Two reports are used in the company’s controlling system, along which company processes can be monitored. The first table refers to those responsible and to the analysis of longer-term strategic periods (table 1), while the second (table 2) refers to the plan-actual analysis and the calculation of values for each given business year.

In the KPI table (Table 1), there is a responsible people or group identified by the controlling system for each indicator. Its task is to monitor the achievement of goals, evaluate the results and provide feedback. The opinion of the people in charge play an important role in determining periodic and annual goals for the financial indicators they supervise.

The data of the different periods must be interpreted as a cumulative value, that is, each period’s data also includes the results of the previous periods. During the creation of periodic reports, the periods can change dynamically and contain current financial data.

KPIs are calculated based on a plan-fact analysis. In our research, we developed and revealed an applied KPI data set (Table 2), along which the company’s lean performance can be evaluated using financial indicators.
In each case, the KPI indicators contain a forecasted – extrapolated value, which gives an estimate of the expected annual results based on the current performance trend. These predicted values are compared with the set plan values, on the basis of which the development of plan-fact deviations becomes predictable. This makes it possible to effectively determine timely intervention points and take appropriate corrective measures in time.

The examined organization uses a linear extrapolation method to predict plan-fact deviations. This method starts from current trends and uses linear regression to estimate future trends. The data on the rating scale reflect the values of the extrapolated plan-fact deviations.

The linear extrapolation mathematical method used by the organization is structured according to the following steps:

1. Data sets analysis: In the first step, the method analyzes the data sets to determine the current trends.
2. Trend modeling: During the analysis, the method calculates the linearity of the data sets and creates a model of the data trend based on this.
3. Extrapolation: The method predicts future values using an equation that models the trend of the data.
4. Analysis of deviations: Finally, the method compares the fact data with the predicted data and determines the plan-fact deviations, which are evaluated along three different categories. These categories were defined according to the „intervention point”, „unfavorable” and „favorable” linguistics terms.

This method is able to make predictions in a mathematically objective manner, thereby promoting effective decision-making and continuous improvement of organizational performance.

The method is structured as follows:
Z = A–Tp Tt
A = Fact value in the given period
Tp = Number of elapsed days in the given period
Tt = Total number of days in the planning period (quarter, year)
Z = Extrapolated estimated value
Y = Z–P 100
Z = Extrapolated estimated value
P = Plan value/target cost value
Y = Plan-fact deviation value (%)
X = Y – 100
Y = Plan-fact deviation value (%)
X = Value deviation (difference between the extrapolated fact value and the planned value)

During the analysis of deviations, the system takes into account the evaluation of the degree of deviation from the plan-fact analysis. Deviations compared to the plan values of given KPIs examined with the extrapolation method belonging to the given period analysis are classified by the algorithm according to a predefined set of rules.

A negative change in income, revenue and processes means that the plan has not been fulfilled, while positive deviations mean that the plan values have been exceeded. In the case of costs, the differences are in the opposite direction.
If the measured value deviates more than 5% in the negative direction, the KPI must be classified as an „intervention point”.
μintervention point(x) = If the deviation in the negative direction is 5% ≤ X, then the examined KPI is an element of the set
μintervention point(cost)(x) = If there is a positive deviation of 5% ≤ X, then the examined KPI is an element of the set
X = Difference between the extrapolated fact value and the plan value in (%)
If the amount of the deviation falls between the negative 5% or, in some cases, the pre-defined negative deviation limit and the probable result of the plan value, the KPI is classified as „unfavorable”.
μunfavorable(x) = If the negative deviation is 0% < X < 5%, then the examined KPI is an element of the set
μunfavorable (cost)(x) = If the deviation in a positive direction is 0% < X < 5%, then the examined KPI is an element of the set
X = Difference between the extrapolated fact value and the plan value in (%)
If the value of the KPI is 0%, or above the plan values, or below, in the case of costs, the system classifies the analyzed KPI in the „favorable” category.
μfavorable(x) = If 0% ≤ X, then the examined KPI is an element of the set
μfavorable (cost)(x) = If X ≤ 0%, then the examined KPI is an element of the set
X = Difference between the extrapolated fact value and the plan value in (%)
By applying the extrapolated fact values, the company can therefore forecast an expected lean performance that meets the basic principles of controlling. In this way, it is able to reveal intervention points and predict results that do not meet the goals.

Conclusions

One of the main advantages of the organization’s controlling system is that it primarily focuses on monitoring financial performance. Finance can evaluate performance primarily through financial indicators. The controlling system used by the organization enables the effective analysis of various processes and activities with the help of financial indicators. Another advantage of the controlling system is that it is predictive, so it is future-oriented in accordance with the basic principles of controlling. The linear extrapolation used by the organization makes it easier to evaluate the expected performance and thus to explore possible intervention points. One of the main advantages of linear extrapolation is that it is a simple and easy-to-use method, but at the same time, it also has several shortcomings, one of the most significant of which is limited sensitivity to standard deviation. Linear extrapolation with respect to standard deviation has basically two problems. One is that the linear extrapolation method does not take into account the standard deviation of the available data, it only follows the trend. So, if the data have a wide spread, linear extrapolation will not provide an accurate estimate of the unknown values. Ignoring the standard deviation can lead to inaccuracies in the estimate, especially in the case of large standard deviations. Another application problem is the neglect of variation in standard deviation. Linear extrapolation cannot accommodate changes in standard deviation. If the standard deviation of the data changes, the linear extrapolation cannot follow this change, which can lead to further inaccuracy in the estimate. Therefore, the effects of standard deviation must be taken into account when using linear extrapolation. Alternative methods may be used, such as polynomial extrapolation or more complex statistical methods such as confidence intervals, if the data are widely spread or the standard deviation varies.

Prediction is therefore a highly emphasized element of the organization’s controlling system, which enables the organization to change the processes in such a way as to ensure that the planned state is reached after the intervention points have been identified. Therefore, the plan values have a decisive role in the operation of the controlling system. The predictive fact values are compared to the plan values and thus the expected performance is determined. If the expected performance differs from the pre-defined plan values, then intervention is required.

One of the most significant shortcomings of the controlling system is the low level of exploration of correlations between financial indicators and processes. Therefore, the controlling system cannot dynamically and accurately determine the definition of intervention points – process development. The controlling system can only provide feedback on the possible need for intervention. If this shortcoming can be addressed during the development of the controlling system, the controlling system can provide even more accurate feedback on performance. During the development of the controlling system, the process monitoring system must also be integrated into the controlling system and operated as a unified system. Another shortcoming of the controlling system is that statements based on financial indicators do not always reflect strategic goals, and therefore can only be used to evaluate financial results. If the strategic goal differs from the financial goal, or if the strategic goal cannot be monitored with financial indicators, then the controlling system is not suitable for effective monitoring. Currently, the organization’s controlling system only works effectively if the strategic goals can be properly monitored by evaluating financial indicators, thus the controlling system can be considered a performance evaluation system based primarily on financial indicators. The controlling system can also be further developed by integrating non-financial data, thus the system would become more suitable for monitoring the organization’s processes more effectively and for dynamically and more accurately predicting possible intervention and development points.

Supported by the ÚNKP-22-4-I New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.”

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Dr. Gergő Thalmeiner, Assistant professor,
Hungarian University of Agriculture and Life Sciences,
Institute of Rural Development and Sustainable Economy

Dr. Sándor Gáspár, Assistant professor,
Hungarian University of Agriculture and Life Sciences,
Institute of Rural Development and Sustainable Economy

Laura Gergely, Director,
Dömsödi Zöldségfeldogzó Ltd.

Nurbek Kobilov Lecturer,
Karshi Engineering-Economics Institute,
Uzbekistan