Nestle its financial reporting with Management Science



Course title:


‘Nestle its financial reporting with Management Science’

Article Summary

This paper is a review of the article ‘Nestle its financial reporting with Management Science’ and it is written by Christophe Oggier, Emmanuel Fragniere, and Jeremy Stuby. The article was published in 2005 volume 35 issue 4, in the 271-280 pages. The company’s executive information system department collects data from its subsidiaries to offer operational, strategic, and financial information to the management. The company made a decision of improving its service by the application of tools of business analytics based on management science. This action was aimed at encouraging controllers and analysts to make maximum utilization of information offered. Four modules developed included forecasting, optimization, simulation, and sensitivity analysis. This has resulted to an increase in the number of managers familiarized with quantitative decision making through the application of management science models.

Firms need to develop analytics techniques that managers are able to handle directly without having to consult experts. This is what management science upholds although it has not always done so. In 1997, Nestle began a partnership between the Nestlé’s executive information system department and the University of Lausanne’s business school. The two courses offered in the university Operations Management and Business Quantitative Methods emphasized modeling instead of algorithms. Nestle is divided into several products and markets, being the largest food and beverage firm worldwide. Nestlé’s EIS department collects, checks, validates, and combines data before storing in databases. The department developed tools to give standardized tables and charts of results. In addition, it developed these tools to ensure the access of accurate and relevant information through a secured client-server by controllers and decision makers. Since the development of these tools, potential users were interested in using them. Modeling system is considered a simple and a quality approach despite arguments that it cannot visualize the mathematical formation of the models. However, due to its simple nature, it is used in most organization across the world.

The four modules developed by the company focus on all areas including regression and smoothing techniques, risk and uncertainty analysis, mathematical programming, and optimization techniques. The four modules are not used alone rather they are incorporated in a financial analysis framework. This enables the selection of the best investments with the highest returns. Economic profit is used as the indicator of value creation. Performance drivers that Nestle uses include profit margin, sales growth, income tax rate, fixed capital intensity, working capital intensity, duration, and cost of capital. The company relates economic profit to market value added. This is what the company uses to determine ways of ensuring sustainable profit growth.

The sensitivity analysis module is used in describing the best consensus or guess that is developed from the knowledge of the analyst, and used to study the effect on the solution of certain parameters by modifying their values. The company applies the system widely at the headquarters and subsidiaries to make investment decisions. On the forecasting module, regression analyses and time series are used in predicting of several indicators. Forecasts are given to financial analysts who then perform checks by applying techniques of management science to challenge or cross-check the data given to them. On the simulation module, the technique used is Monte-Carlo simulation to develop a variety of scenarios affected by random factors as per the probability distribution laws. The @Risk Software is used to generate and analyze the simulations. The Excel spreadsheet may also be used to perform these simulations. On the other hand, the optimization module applies the Excel Solver and mathematical programming. It is concerned with how to devise the problem that must be resolved by differentiating decision variables, a target variable, and constraints. The sensitivity report and the solver output are then analyzed. The company uses the four modules in a coherent and logical manner to calculate the NPV that is essential in making decisions on whether or not to make investments.

The EIS team in Nestle trains and gives support to users with the aim of offering accessible information in areas including corporate controlling, zone management, accounting and reporting, strategic business and reporting, mergers and acquisitions, treasury, insurance and pension, and technical. Nestle measures the effectiveness of capital budgeting by the application of the four MS modules, and interest in the application of these modules has increased. Despite challenges such as political interference, the company ensured the presentation of this approach in a way aimed at ensuring managerial efficiency and effectiveness. At Nestle, the EIS department plans to offer users with open models, as well as, training them on OR/MS tools rather than limiting them on a single model. The application of whatever model depends on the task being handled. This has proven effective in its application. Most departments of executive information are not acquainted with quantitative techniques. Courses in MS tools in 1996 made a difference to this. Nestle upholds the application of MS techniques and intends to spread them across other companies in the world. So far, the company has carried out presentations, training courses, and workshops of the company in various regions have been successful.

Article Critique

This article is essential in the management field for understanding new trends in the field, in the modern world. The selection of the article is first because Nestle is a leading firm in the food and beverage industry in the world. Second, the demand for application of MS techniques in companies across the world has increased. This article provides a clear explanation on the benefits associated with the application of such models for companies that have implemented then such as Nestle. In addition, the article discusses the four modules of the MS model and how they are applied for different functions. The authors of the article, Oggier, Stuby, and Fragniere provide the reader with a deeper comprehension on how the different modules may be applied for different purposes in organizations as illustrated by Nestle.

The main objective of the article is to discuss the significance of MS techniques in ensuring accuracy, effectiveness, and efficiency in organizational functions. For instance, the EIS department of Nestle collects, checks, validates, and combines data before storing in the databases. The department tries to offer accurate information to the controllers and analysts in order for them to be able to monitor the performance of the firm and lead to appropriate decisions. This is the reason why Nestle developed MS tools to help controllers, decision makers, and analysts in accessing accurate and relevant information that is secure, and to offer standardized charts and tables of results. The MS techniques perform this task. The authors of this article use the company to explain the significance of applying MS tools and techniques. The article is aimed at illustrating that modeling using Excel Spreadsheets can be essential in organizations despite its criticism that it is difficult for users to visualize the mathematical structure in the model. However, the model has proven successful for Nestle. Areas such as sensitivity analysis, optimization, forecasting, and simulation are essential in all companies in order to decide on what investments to adopt and which ones to consider or wait for another time. This necessitates the application of techniques and procedures that can effectively and efficiently perform this task. Authors demonstrate that the MS techniques have worked for Nestle, and the company is advocating for other companies to apply them. The authors explain factors that are essential and are considered by Nestle in determining the company’s performance. These include profit margin, sales growth, income tax rate, fixed capital intensity, working capital intensity, duration, and cost of capital.

Firms need to establish small business analytic techniques that managers are able to handle directly without having to consult specialists, consultants and experts. The article, therefore, is focused on elaborating in depth how the process of determining the company’s performance and selection of the best available option of investment is made easier through the application of the four modules applied by Nestle in the executive information system department. The sensitivity module is concerned with smoothing and regression tools which are essential in performing correlations between variables. Simulation analysis is essential for identifying risks and uncertainties associated with various projects. These are essential in making significant decisions on what to do with a project. Optimization module is essential in resolving cash-management problems in organizations. This is why it uses mathematical programming that is vital for developing solutions to challenges that arise in organizations. Every organization performs forecasting of concerns that might arise for the companies. Forecasting analysis is, therefore, a crucial tool in forecasting different variables such as sales and profitability of a company. Therefore, these modules stand out not only for Nestle, but also for other companies that effectively implement them. The authors, being employees of the company elaborate the success of these modules in the company, as well as, the success of the company’s workshops, presentations and training programs. Although many executive information system departments are not familiar with the application of quantitative techniques in their organizations, many managers across the world are demanding the adoption and implementation of the MS tools in their organizational functions.

Many executive information system departments do not recognize the quantitative methods in their organizational operations. This to some extent has to do with the fact that there are no adequate courses offered in MS to ensure that individuals are acquainted with adequate knowledge and training on how to apply them in organizations. It is only until this is done that people in organizations would begin top realize the significance of the Management Science in improving efficiency and effectiveness in organizations. A good case has been demonstrated by authors in this article, the case of Nestle. The MS course at the University of Lausanne business school in 1996 has significantly meant success for the company. Managers at the company are able to apply tools of business analytics in order to improve their decisions in business. However, there was an assumption that individuals in the company could apply these simple techniques to improve the quality of business decisions. It is thus, difficult to measure the success of work on the basis of cost reduction or profit increase. This counts as part of the problems facing companies that apply the MS techniques. Therefore, quantitative expertise is essential in enabling companies maintain their leadership positions in whatever industries and regions of operation.

The authors argue that Nestle intends to spread the techniques to other regions farther, faster, continuously, and in a systematic manner in order to establish sharper and specific modules. These include linear programming, real options, and dynamic simulation for business and technical expertise as among the centers of expertise. This implies that the MS techniques may be extended to functions besides the finance roles and functions. Timing is one of the key factors that need to be considered to avoid early introduction of these techniques when organizational managers are not ready to adapt them. This is also essential to avoid late introduction of the techniques that could lead to the loss of competitive advantage of the company. The introduction of these techniques across other organizations will ensure improvement in efficiency and effectiveness at all levels of operations.

The article is of much beneficial because of a number of reasons. The management and business fields are faced with so many changes in the environment that come as a result of both internal and external factors that influence organizations. These changes require adjustments if companies are willing to maintain their positions in the industry and continue experiencing success. The choice by Nestlé to introduce management in order to improve its financial reporting is one of the ways of adjusting to environmental and technological changes in the managerial field. The article brings it clear to the reader that MS techniques, although not widely recognized have much benefits for organizations. This is demonstrated by the case of Nestle that the authors describe that uses these techniques in its operations. For anything to be successful in the business field, it must be tried to determine its practicability. Nestle is a leading company in food and beverages industry, and this implies that if the company has applied this technique and still holds its position in the market, the techniques are beneficial.

The article and subject of concern is important as it illustrates that quantitative modeling is effective if applied or taught in a user-friendly environment. Offering courses in Management Science is of great advantage for future managers to enable quality decision making. Managerial functions involve the decision making role of the management. These decisions might be short term or long term. In whatever case, the decisions made need to be quality ones. Therefore, imposing MS models in companies ensures quality improvement in the process of decision making. The article highlights on the importance of modeling as a process. The process involves steps of identifying and defining problems, gathering data, developing and solving models, and evaluating the results. The approach is not complicated if implemented effectively. Efficiency, accuracy, and effectiveness are improved without wastage of crucial resources such as time. The MS areas such as forecasting, optimization, sensitivity analysis, and simulation are essential for decision making in organizations as illustrated in the article by the three authors.

The article states that Nestle gathers both primary and secondary data in a systematic way. This is done to determine the effectiveness of techniques applied in the company and ensure the techniques are practical. This is essential for organizations that have adopted techniques such as MS to demonstrate their applicability. For instance, Nestle carried out a survey to establish whether plans were logical. The main sections of the survey included EIS access tools, EIS data, and EIS training and information. The results implied that potential users were interested in the access of new tools and techniques that would ensure that thorough analyses were possible. The EIS department of Nestle collects data, checks before validating and consolidating data in order to store them in EIS’s databases. Both primary and secondary data are essential for the company to perform these analyses.

The quantitative techniques of analysis described in the article that are essential in management include the sensitivity analysis module. This involves the description of the best consensus or guesses from the knowledge of the analyst and is used to study the effect of the solution of certain factors by modifying their values. This is essential in understanding what to be invested to get a 12 percent internal rate of return or to break even. Conditions for the break even can thus be depicted using charts. In addition, there can be an assessment of the pricing policy, as well as, quantity sold on NPV. It is also possible to establish a chart of trade-offs between indicators and their impacts. These are essential for facilitating the decision making process. The forecasting module is essential in estimating future quantities. The simulation module is also essential and can be used to determine uncertainties associated with various projects. The optimization module identifies problems and solutions to these problems. The models work together to produce qualitative results that are used in making quality decisions. Important areas of focus include strategic business and marketing, technical, zone management, reporting and accounting, corporate controlling, pensions and insurance, and mergers and acquisitions. The implementation of these tools, however, requires some resources, both financial and non-financial. For instance, there is a need of knowledgeable and skilled human resources. In addition, time as a factor is also an essential resource required. Some costs related to financial expenses are also incurred in the implementation process. These and other resources are required in order to ensure successful implementations in organizations.

It is, however, essential to note that restricting managers to one model is not a good idea. Rather, managers should be able to choose the model that best suits their requirements. This ensures maximum profitability is attained as managers are knowledgeable enough in this field. It is not only profitability that is improved. Performance, efficiency, and effectiveness are also improved as illustrated in the case of Nestle where managers are allowed to use OR/MS model. Financial managers may use their skills of these techniques in many areas such as finance, decision making, sales and marketing. For instance, managers can apply the sensitivity analysis to calculate sale prices for finished goods and services. Managers in marketing may also adapt models that suit their needs and requirements. In addition, managers of the treasury department frequently apply the optimization module and risk analysis for cash management. On the other hand, departments of acquisitions and mergers apply risk analysis in calculating risks and opportunities related to a particular investment or project.

The article is well structured and easily understandable by most readers. The authors critically examine the benefits associated with the implementation of the MS model in organizations. The reader is able to gather sufficient knowledge on the model and the modules discussed in the article including simulation, optimization, sensitivity, and the forecasting module. These are essential in enabling the decision making process by managers in organizations. The authors illustrate the importance of these quantitative techniques by giving an example Nestle, a leading food and beverage firm in the world. Companies should adopt these techniques due to their efficiency and effectiveness in providing managers with quality decisions on what to invest in based on the NPV obtained. The modules perform a remarkable function when used jointly. Although a few weaknesses exist especially on assumptions about the performance of the techniques, the benefits outweigh the weaknesses. On data collection methods, secondary data should entail thorough investigations on the internet to determine the effectiveness of the techniques elsewhere. Managers should consider such assumptions bearing in mind that companies are different, and factors that might influence one company may not influence another company. Despite all the weaknesses, however, the application of MS model needs to be emphasized across all nations in the world as they improve the quality of the decision making process. The model may be used in other organizational areas apart from the financial area, and improves results in a similar manner. The article on Nestle has provided background and essential on the usefulness of the MS model and the four modules for managerial functions.

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