Market forecast analysis in ERP sand table simulation management

Market forecast analysis in ERP sand table simulation management

When making business strategy and sales strategy, we must use various scientific methods and means to predict the market demand of products according to the changes of market demand. There are many kinds of market forecasting methods. This paper attempts to use time series to make forecast analysis and provide forward-looking forecast information for decision makers.

I. Introduction

In the market economy environment, increasing product market sales and expanding product market share are the basis for the survival and development of enterprises, and market demand directly affects the sales of enterprise products. In the sand table simulation operation, according to the market forecast and the actual situation of the enterprise, the development strategy of the enterprise is formulated, and under the leadership of CEO, the specific enterprise strategic plan is formulated for management. Especially when formulating business strategy and sales strategy, we must use various scientific methods and means to predict the market demand of products according to the changes of market demand. There are many kinds of market forecasting methods. This paper attempts to use time series to make forecast analysis and provide forward-looking forecast information for decision makers.

Second, ERP sand table simulation management overview

(1) Introduction to ERP [2]

ERP (Enterprise Resource Planning) was first put forward by gartner group Inc, a famous American consulting company, in the early 1990s. It is developed on the basis of MRP and MRPII, and it is a modern enterprise management mode based on information technology. At that time, ERP mainly expanded MRP II in function, and added equipment management, quality management, distribution management, fixed assets management, salary management and human resources management on the basis of MRP II. The basic idea of ERP is to regard the manufacturing process of an enterprise as a supply chain connecting suppliers, manufacturers, distributors and customers, and emphasize the overall management of the supply chain, so as to make the manufacturing process more effective and the integration of enterprise processes closer, thus shortening the time from customer ordering to delivery and meeting the market demand quickly. ERP is committed to making full use of modern information technology in all activities of enterprise management, establishing an information network system, integrating and integrating enterprise logistics, information flow, capital flow and business flow, realizing the overall optimal allocation of enterprise resources, improving enterprise management level and performance, and ultimately improving enterprise economic benefits and competitiveness.

(2) Brief introduction of enterprise sand table simulation operation.

The simulation management of enterprise sand table is based on production enterprises and based on manual and electronic sand table tools. Generally, six groups (that is, six virtual enterprises) compete, and each virtual enterprise has five main roles: CEO, CFO, marketing director, production director, purchasing director, and possibly financial assistant and business spy. Each group of students (that is, members of an ERP team) plays their own roles and simulates the whole business process of an enterprise for 6-8 years according to certain rules. Each enterprise team formulates enterprise strategy and business strategy according to market forecast and actual situation of the enterprise, and under the leadership of CEO, formulates specific enterprise strategic plan for business management. ERP sand table simulation management is all-encompassing and involves a wide range of knowledge, which can be called the comprehensive application of subject knowledge. Knowledge of management, accounting, marketing, leadership, production and operation, logistics and so on is required.

Third, the connotation of time series forecasting methods

Time series refers to a set of data series obtained by arranging historical statistical data in time order. Time series forecasting method is to arrange the historical data of the predicted object into time series in time order, then analyze their development trend with time and extrapolate the future value of the predicted object.

The application of time series forecasting analysis is based on the following assumptions: each component acts on the actual demand independently, and the mechanism that worked in the past and now will continue into the future. There are many types of time series analysis. Here, only the types with linear trend and equal seasonal fluctuation are analyzed and applied to ERP sand table simulation management. The key to forecast with this analysis is to find the linear trend equation (linear equation, let the general formula of the equation be y=a+bx) and the seasonal coefficient.

The so-called "seasonal index (SI)" is the average of the ratio of actual value to trend value Tt. The general formula of linear trend equation is

Tt=a+bt

Where Tt is the predicted value of time t.

A and b are coefficients.

Fourthly, the application of time series analysis method in ERP sand table simulation market forecast.

Suppose that in the ERP sand table simulation operation, the relevant information of the order status (that is, sales status) in the first three years of operation is shown in the following table 1, and the sales status in the fourth year is predicted according to this information.

According to the above related data, the following time series analysis can be carried out:

The first step is to draw the following table 2 according to the relevant data of ERP sand table simulation sales in the previous three years.

The second step is to find the trend linear equation. Draw a chart according to the data in Table 2.

In order to find the straight line of the trend value, the least square method can be used. For the sake of simplicity, visual inspection can be used in the process of ERP sand table simulation management. Find a straight line and let it pass through the middle of the moving average. The data points are distributed as far as possible on both sides of the straight line. This straight line represents the trend line, and its intercept with the y axis is a, where a=80. At the other end, when t= 12, the sales volume is 100. So the value of b is

b =( 100-80)/ 12 = 16.7

From this, we can get the trend linear equation of sales in the first three years:

TT = 80+ 16.7 tons

The third step is to estimate the seasonal coefficient. For example, for 1 in the first quarter, At/Tt= 80/8 1.67=0.98. By analogy, the At/Tt of each quarter can be obtained, as shown in Table 3.

Fourth, calculate and forecast the average seasonal coefficient. The average seasonal coefficient of each quarter and the forecast sales volume of the fourth year are shown in Table 4.

For example, the average seasonal coefficient in spring, because seasons 1, 5 and 9 are all spring, Si (Spring) = (a1/t1+A5/T5+A9/T9) = (0.98+1.02+/. When forecasting, we must choose the correct t value and seasonal coefficient. In this example, the corresponding t values of the four seasons in the fourth year are 13, 14, 15 and 16 respectively, and the relative seasonal coefficients are SI (spring), SI (summer), SI (autumn) and SI (winter) respectively.

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