Analitical Algoritms for Environment Monitoring in the Sales Management System of an Organization
【Abstract】As a rule, traditional factor models in economic science focus on assessing changes in financial results during reporting periods. The authors emphasize the significant potential of factor analysis models in obtaining financial results, as they enable the identification of reasons for profit decline and facilitate effective administrative and managerial decisions. However, the classic method of measuring factor influence appears ineffective in the context of current monitoring and business administration tasks due to its lack of timeliness compared to short-term analysis. This paper proposes an approach for the current assessment of financial results using algorithms capable of monitoring the dynamics of factor characteristics' influence. When analyzing operational effectiveness, it is advisable to study the dynamics of resulting indicators using various methodological tools for economic analysis. In this regard, the paper substantiates the feasibility of using modernized ABC-XYZ analysis in evaluating factor modeling results. The integration of these methods within the management decision-making system necessitates proper documentation. Therefore, a management report structure is proposed, integrating factor model results and ABC-XYZ analysis for a productive assessment of ongoing changes. The authors believe that this operational management report form will enhance operational monitoring efficiency, as it allows for evaluating the priority level of specific products based on sales dynamics studied using factor models.
【Key Words】ABC-XYZ analysis, analysis of financial results, analysis of marginal profit, factor analysis of marginal profit.
1.Introduction
The need for research arises from the limitations of traditional factor analysis in evaluating a company's financial results. When assessing factors indicating emerging problems, it is crucial to rapidly evaluate the situation, monitor the dynamics, track significant changes, present data effectively for managerial monitoring, and integrate methodological techniques for a comprehensive analysis of factor impacts on key performance indicators. To prioritize changes among numerous product items, we propose combining the deterministic factor analysis model with the augmented ABC-XYZ method. The focus is not on the factor model itself, but on a compilation of methods aimed at real-time operational monitoring. The analysis targets a hypothetical retail organization selling a wide range of goods, making the proposed model universally applicable.
2.Method
The shift to modern market practices and economic instability significantly impact internal management processes. Management decisions in such environments rely on operational analysis of short-term economic activity data and results. Researchers suggest using multiplicative factor models for operational analysis and management reporting, identifying vulnerable financial responsibility centers and enabling timely measures and decisions. Spencer and Stradling emphasize the importance of realistic model compilation and application in managerial problem-solving (2005). Melicher and Norton view factor-based assessments of cost, profit, and profitability dynamics as crucial operational management tools (2013). Scott and Lazov delve into operational analysis of small business financial results (Lazov, 2017; Scott and Scott, 2016). Nain and Battacharyya indirectly address operational management reporting in horizontal mergers (Bhattacharyya and Nain, 2011).
Foreign authors like Siegel advocate for "instantaneous" factor analysis to guide product positioning decisions, noting that the financial result, as a model output, is an indicative tool rather than an action call, highlighting flaws in commodity item structure analysis (Siegel and Shim, 2006). In contrast, Alexander considers margin analysis for medium-term financial planning (2018). Berman's manager's manual and Martins' paper mention factor analysis as a financial planning tool (Berman, 2006; Martins, 2017). Bragg discusses the significant impact of total marginal income on prices, variable costs, and financial results (Bragg, 2009).
Among various factors, short-term changes in total marginal income most affect the operating financial result. This study focuses on identifying the influence of volume, structure, and marginal income factors of individual products using two approaches: considering marginal income per unit or per dollar of sale price, and using volume factors in value or quantity terms. These approaches are illustrated in Fig. 1.
Marginal income, the volume of products sold in physical terms, and the structure of products sold in terms of sales were selected as the components of the factor model. In our opinion, the natural model wins compared to the value model, since its factor attributes are better interpreted, and the natural volume is more visual for the manager’s perception. It gives an idea of the dynamics of sales for specific positions without regard to price fluctuations that may occur due to a change in pricing approach. In our opinion, it is advisable to consider the proposed factor model of marginal analysis as a tool for managerial monitoring, while it is advisable to use the weekly measurement interval and to present the results of the analysis in the form of a management report that will create an information basis for determining specific methods of operational sales management.
The use of the factor model for the operational analysis of the financial results of the company and the formation of a management report involved several stages. During these stages, the influence of factors that affected the change in the resulting indicator, marginal income, was determined and reflected in the indicators of the management report. The changes that are taking place are not considered separately, but in conjunction with previous periods. Therefore, the reporting period turns into the base period for the second and third week during factor analysis of the first and second weeks. When using this model, an important aspect is the allocation of volume and structure factors; otherwise, it is likely to draw erroneous conclusions as a result of the analysis due to the fact that replacing one product with another may not affect the change in total marginal income. As a result, the total structural shift according to the model does not demonstrate a significant impact force, since the positive and negative effects of volume and structure indicators cancel each other out. In reality, the structural shift in stock keeping units is significant. In this case, focusing on the totals is incorrect, because it is stock keeping units that are important for managerial analysis.
Fig. 1. Approaches to factor modelling of total marginal income
The study examined various approaches to the development of management reporting forms on financial results, which are determined by the company's direction, management needs, and the importance of choosing alternative management decisions. Since the management report should reflect the dynamics of adjacent monitoring periods, the paper proposes a tabular presentation variant to more clearly identify the factors that most strongly affect the financial result.
3.Results:
Based on the factor analysis results, the following impact of factors on the resulting indicator was obtained (Table 1). Calculations revealed an increase in total marginal income by US $972,754 in the second week, primarily due to growth in total marginal income for commodities 1, 3, and 4. The factor analysis results indicate that the most significant reason for the positive change in the sought-for indicator was the influence of a quantitative factor associated with an increase in total sales in physical terms.
Structural shifts in the composition of sales are observed, but their cumulative negative impact on total marginal income is insignificant compared to its growth due to the total sales factor (+972,754 against -11,222). In this situation, it should be noted that the virtually absent influence of the structure factor in the model is due to the availability of a structurally equivalent but reverse shift in dynamics in positions of the same margin (item 3: -98,551.97, item 4: 95,886.55). Thus, the structural shift is significant but not diagnosed in the model used, imposing objective limitations on its practical application (Table 2).
Table 1. Results of factor analysis of total sales margin income (1st – 2nd weeks)
Table 2. The results of the factor analysis of the total marginal revenue of sales (2nd -3rd weeks)
According to the results of the 3rd week, an increase in total marginal income by US $436,520 occurred mainly due to structural changes in the composition of sales for commodities 1 and 4. Reducing the specific weight in sales of commodity 1, which has a high margin income per unit, can significantly negatively impact the company's performance. Additionally, a sharp increase in the specific weight of commodity 4 caused a strong structural shift, changing the factor of change in total marginal income from negative (-52,200) to positive (+69,450).
A factor analysis for marginal profit, even in the format of specialized management reporting, may not fully assess the company's financial results. Comparing the developed dynamics with respect to factors and the priority of analyzed product items in the management report may be most effective from a sales management perspective due to the large assortment. To improve the proposed methods, the criteria of the traditional ABC-XYZ analysis approach should be supplemented with the "share of total marginal income" indicator, providing a three-dimensional estimate alongside the factor of regularity of sales. This allows for a more productive comparison of factor analysis and determining the significance of changes in marginal income for certain positions, facilitating the selection of financial monitoring methods.
During ABC analysis, commodities are sorted and grouped according to their contribution to sales: A (most valuable, 20% assortment = 80% sales), B (intermediate, 30% = 15%), and C (less valuable, 50% = 5%). Adding MNP grouping introduces a new factor characterizing the share of a product in total marginal income, while maintaining similar percentage values for attributing commodities to a particular group. This evaluates the volumetric indicator of financial activity by margin, adapting ABC-XYZ analysis directly to the study object and forming a three-dimensional matrix assessment.
In XYZ analysis, commodities are grouped by demand stability and forecast accuracy: X (stable demand and high forecast accuracy, coefficient of variation 0-10%), Y (volatile demand but forecast possible, 10-25%), and Z (random demand, forecast impossible, 25% and above). Regular updates on ABC-MNP-XYZ sales analysis results and timely actions based on these results increase operational management efficiency. This information acts as an independent component of the information array and the generalized structure of the management report.
Dynamic analysis indicates a significant drop in the sales volume factor at the end of 3 weeks of observations (-23,000), with particular attention needed for commodities 1 and 3, leaders of the AMY group, whose margin income factor has decreased (-6 and -2, respectively). The most rational approach to assessing the dynamics of total marginal income in the context of individual stock keeping units is studying factorial features reflecting the causes of changes related to the priority level of analyzed commodity positions. Practical implementation of the proposed approach requires justifying the format for presenting operational monitoring indicators in a management report, enabling managers to focus on key aspects of changes and timely implement appropriate business administration tools.
Table 3. Results of ABC-MNP-XYZ analysis
Table 4. Management report form for operational monitoring of the company's financial results
4.Conclusions
The result of the study devoted to the features of the application of marginal analysis tools is the proposed approach based on the justification of the composition of factor characteristics and the form of functional connection in the deterministic model of marginal income analysis. Regulation of the observation period, integration of the results with the modified ABC-XYZ analysis algorithm, and presentation of the analytical indicator system in the management report format corresponding to the purpose of disclosing the operational dynamics of marginal revenue from sales in general and in the context of stock keeping units sold are also key components of this approach.
Acknowledgements
The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.
References
Alexander J. (2018). Objectives of financial analysis and performance management. Financial Planning & Analysis and Performance Management (Wiley Finance), 134-142.
Berman K. (2006). Profit is an Estimate. Financial Intelligence, Revised Edition: A Manager's Guide to Knowing What the Numbers Really Mean, 43-48.
Bhattacharyya S., & Nain A. (2011). Horizontal acquisitions and buying power: A product market analysis. Journal of Financial Economics, 99, 97-115.
Bragg S. (2009). Measures for profitability. Controllership: The Work of the Managerial Accountant, 60-62.
Lazov I. (2017). Profit management of car rental companies. European Journal of Operational Research, 258, 307-314.
Martins P. (2017). Integrating financial planning, loaning strategies and project scheduling on a discrete-time model. Journal of Manufacturing Systems, 44, 217-229.
Melicher W.R., & Norton E. (2013). Financial analysis and long-term financial planning. Introduction to Finance: Markets, Investments, and Financial Management, 385-394.
Scott T.J., & Scott J.T. (2016). Troy the entrepreneur's idea and outside finance: Theory and evidence about entrepreneurial roles. European Economic Review, 86, 118-130.
Siegel G.J., & Shim K.J. (2006). Contribution margin analysis. Handbook of Financial Analysis, Forecasting, and Modeling, 14-22.
Spencer T., & Stradling B. (2005). Preparation of forecast financial statements. Cambridge International Diploma in Management at Higher Professional Level: Financial Analysis, 15-18.
销售管理系统中环境监测的分析算法
【摘要】通常，经济科学中的传统因素模型侧重于评估报告期内财务业绩的变化。作者强调了因子分析模型在获得财务结果方面的巨大潜力，因为它们能够识别利润下降的原因，并促进有效的行政和管理决策。然而，在当前的监控和企业管理任务中，衡量因素影响的经典方法似乎无效，因为与短期分析相比，它缺乏及时性。本文提出了一种使用能够监测因素特征影响动态的算法对财务结果进行当前评估的方法。在分析作战效能时，建议使用各种经济分析方法工具研究由此产生的指标的动态。在这方面，本文证实了使用现代化的ABC-XYZ分析来评估因子建模结果的可行性。将这些方法整合到管理决策系统中需要适当的文件记录。因此，提出了一种管理报告结构，将因素模型结果和ABC-XYZ分析相结合，对正在发生的变化进行富有成效的评估。作者认为，这种运营管理报告表将提高运营监控效率，因为它允许根据使用因子模型研究的销售动态来评估特定产品的优先级。
【关键词】ABC-XYZ分析、财务结果分析、边际利润分析、边际收益因素分析、系统
1.导言
对研究的需求源于传统因素分析在评估公司财务业绩方面的局限性。在评估表明新出现问题的因素时，至关重要的是快速评估情况，监测动态，跟踪重大变化，有效地提供数据以进行管理监测，并整合方法论技术，全面分析因素对关键绩效指标的影响。为了优先考虑众多产品项目之间的变化，我们建议将确定性因素分析模型与增广ABC-XYZ方法相结合。重点不是因素模型本身，而是旨在实时运营监控的方法汇编。分析针对的是一个销售各种商品的假设零售组织，使提出的模型具有普遍适用性。
2.方法
向现代市场实践的转变和经济不稳定对内部管理流程产生了重大影响。在这种环境中的管理决策依赖于对短期经济活动数据和结果的运营分析。研究人员建议使用乘法因子模型进行运营分析和管理报告，识别易受攻击的财务责任中心，并及时采取措施和做出决策。Spencer和Stradling强调了现实模型编译和应用在管理问题解决中的重要性（2005）。Melicher和Norton将基于因素的成本、利润和盈利能力动态评估视为关键的运营管理工具（2013）。Scott和Lazov深入研究了小企业财务业绩的运营分析（Lazov，2017；Scott和Scott，2016）。Nain和Battacharyya间接解决了横向合并中的运营管理报告问题（Bhattacharyya和Nain，2011）。
像西格尔这样的外国作者主张“即时”因素分析来指导产品定位决策，并指出财务结果作为模型输出，是一种指示性工具，而不是行动呼吁，突显了商品项目结构分析中的缺陷（西格尔和希姆，2006）。相比之下，Alexander考虑了中期财务规划的利润率分析（2018年）。Berman的经理手册和Martins的论文提到因子分析是一种财务规划工具（Berman，2006；Martins，2017）。Bragg讨论了总边际收入对价格、可变成本和财务业绩的重大影响（Bragg，2009）。
在各种因素中，总边际收益的短期变化对经营财务业绩的影响最大。本研究侧重于使用两种方法确定单个产品的数量、结构和边际收入因素的影响：考虑每单位或每美元销售价格的边际收入，以及使用价值或数量方面的数量因素。这些方法如图1所示。
边际收入、实物销售量和销售额结构被选为因素模型的组成部分。我们认为，与价值模型相比，自然模型获胜，因为它的因素属性得到了更好的解释，自然体积对管理者的感知更直观。它给出了特定头寸的销售动态，而不考虑由于定价方法的变化而可能发生的价格波动。我们认为，建议将拟议的边际分析因素模型视为管理监控的工具，同时建议使用每周测量间隔，并以管理报告的形式呈现分析结果，为确定运营销售管理的具体方法奠定信息基础。
使用因子模型对公司财务业绩进行运营分析并形成管理报告涉及几个阶段。在这些阶段，确定了影响最终指标边际收入变化的因素的影响，并将其反映在管理报告的指标中。正在发生的变化不是单独考虑的，而是与以前的时期结合考虑的。因此，在第一周和第二周的因子分析中，报告期变成了第二周和第三周的基期。使用该模型时，一个重要方面是数量和结构因素的分配；否则，由于用另一种产品替换一种产品可能不会影响总边际收入的变化，因此分析结果可能会得出错误的结论。因此，根据模型，总体结构转变并没有表现出显著的影响力，因为数量和结构指标的积极和消极影响相互抵消。事实上，库存单位的结构转变是显著的。在这种情况下，关注总数是不正确的，因为库存单位对管理分析很重要。
图1总边际收益的因子建模方法
该研究考察了开发财务业绩管理报告表的各种方法，这些方法由公司的方向、管理需求和选择替代管理决策的重要性决定。由于管理报告应反映相邻监测期的动态，本文提出了一种表格列报变体，以更清楚地确定对财务结果影响最大的因素。
3.结果：
根据因子分析结果，得出以下因子对所得指标的影响（表1）。计算显示，第二周总边际收入增加了972754美元，主要是由于商品1、3和4的总边际收入增长。因素分析结果表明，所寻求的指标出现积极变化的最重要原因是与实物总销售额增加相关的定量因素的影响。
观察到销售构成的结构性变化，但由于总销售因素（+972754对-11222），其对总边际收入的累积负面影响与其增长相比微不足道。在这种情况下，应该指出的是，模型中几乎不存在结构因子的影响，这是由于在相同裕度的位置上存在结构等效但相反的动态变化（第3:98551.97项，第4:95886.55项）。因此，结构转变是显著的，但在所使用的模型中没有被诊断出来，对其实际应用施加了客观限制（表2）。
表1销售利润总额因子分析结果（第1-2周）
表2销售总边际收入的因子分析结果（第2-3周）
根据第三周的结果，边际总收入增加了436520美元，主要是由于商品1和4的销售构成发生了结构性变化。降低每单位利润收入较高的商品1的销售比重会对公司的业绩产生重大负面影响。此外，商品4比重的急剧增加导致了强烈的结构性转变，将总边际收入的变化因素从负（-52200）变为正（+69450）。
边际利润的因素分析，即使采用专业管理报告的格式，也可能无法完全评估公司的财务业绩。由于产品种类繁多，从销售管理的角度来看，比较管理报告中分析产品项目的因素和优先级的发展动态可能是最有效的。为了改进所提出的方法，传统ABC-XYZ分析方法的标准应辅以“总边际收入份额”指标，与销售规律性因素一起提供三维估计。这允许对因素分析进行更有成效的比较，并确定某些职位边际收入变化的重要性，从而促进财务监控方法的选择。
在ABC分析中，商品根据其对销售额的贡献进行分类和分组：A（最有价值，20%的分类=80%的销售额），B（中等，30%=15%），C（价值较低，50%=5%）。增加MNP分组引入了一个新的因素，用于表征产品在总边际收入中的份额，同时保持将商品归因于特定群体的相似百分比值。这通过边际来评估金融活动的体积指标，将ABC-XYZ分析直接应用于研究对象，形成三维矩阵评估。
在XYZ分析中，商品按需求稳定性和预测准确性分组：X（稳定需求和高预测准确性，变异系数0-10%），Y（需求波动但可能预测，10-25%），Z（随机需求，不可能预测，25%及以上）。定期更新ABC-MNP-XYZ销售分析结果，并根据这些结果及时采取行动，可以提高运营管理效率。该信息作为信息数组和管理报告通用结构的独立组成部分。
动态分析表明，在3周的观察结束时，销售量系数显著下降（-23000），需要特别关注商品1和3，AMY集团的领导者，他们的利润收入系数已经下降（分别为-6和-2）。在单个库存单位的背景下评估总边际收益动态的最合理方法是研究反映与所分析商品头寸优先级相关变化原因的因子特征。拟议方法的实际实施需要证明管理报告中呈现运营监测指标的格式的合理性，使管理人员能够专注于变革的关键方面，并及时实施适当的业务管理工具。
表3ABC-MNP-XYZ分析结果
表4 公司财务业绩运营监控管理报告表
4.结论
对边际分析工具应用特征的研究结果是，基于边际收益分析确定性模型中因素特征的构成和函数连接形式的合理性，提出了一种方法。调整观察期、将结果与修改后的ABC-XYZ分析算法相结合，以及以管理报告格式呈现分析指标体系，以揭示销售边际收入的运营动态，也是这种方法的关键组成部分。
致谢
这项工作是根据喀山联邦大学俄罗斯政府竞争性增长计划进行的。
参考文献
Alexander J. (2018). Objectives of financial analysis and performance management. Financial Planning & Analysis and Performance Management (Wiley Finance), 134-142.
Berman K. (2006). Profit is an Estimate. Financial Intelligence, Revised Edition: A Manager's Guide to Knowing What the Numbers Really Mean, 43-48.
Bhattacharyya S., & Nain A. (2011). Horizontal acquisitions and buying power: A product market analysis. Journal of Financial Economics, 99, 97-115.
Bragg S. (2009). Measures for profitability. Controllership: The Work of the Managerial Accountant, 60-62.
Lazov I. (2017). Profit management of car rental companies. European Journal of Operational Research, 258, 307-314.
Martins P. (2017). Integrating financial planning, loaning strategies and project scheduling on a discrete-time model. Journal of Manufacturing Systems, 44, 217-229.
Melicher W.R., & Norton E. (2013). Financial analysis and long-term financial planning. Introduction to Finance: Markets, Investments, and Financial Management, 385-394.
Scott T.J., & Scott J.T. (2016). Troy the entrepreneur's idea and outside finance: Theory and evidence about entrepreneurial roles. European Economic Review, 86, 118-130.
Siegel G.J., & Shim K.J. (2006). Contribution margin analysis. Handbook of Financial Analysis, Forecasting, and Modeling, 14-22.
Spencer T., & Stradling B. (2005). Preparation of forecast financial statements. Cambridge International Diploma in Management at Higher Professional Level: Financial Analysis, 15-18.
