Study On The Prediction Of Daily Consumables Sales Of Y Supermarket

Y supermarket is a small comprehensive supermarket in Xi'an,which operates two categories of all kinds of daily consumables and durable goods,with annual sales of approximately 200 million yuan.From 2012,the foot traffic of Y supermarket started to grow rapidly and consumer groups gradually expanded,at the same time the actual sales of daily consumables in Y supermarket was also increasing unceasingly.Due to the lack of suitable sales forecasts,there were often too large material storage or out of stock phenomenon in Y supermarket.Adapting prediction method based on time series,this paper studies the daily consumable sales forecasts of Y supermarket,to reduce the cost increase or fund waste because of too much or too little inventory.The main results of this paper are as follows.The analysis of current situation and existing problems of daily consumable sales forecast of Y supermarket.Through investigation of current situation of daily consumable sales forecast of Y supermarket,classification of daily consumables was given.Selecting the new goods in daily consumables and seasonal goods as two kinds of representative commodities,and through checking inventory data and drawing sales forecast chart and inventory status change curve to analyse,we found that there existed a quarter end inventory problem that was caused by inadequate consideration of inventory levels in new product sales forecast of daily consumables in Y supermarket;It was unable to grasp peak and sluggish seasons of seasonal products,which led that the sales forecast was too small or too large.Daily consumable sales forecast based on time series in Y supermarket.Adapt prediction technique based on time series to carry out sales forecasts for the new commodities and seasonal goods in Y supermarket,and compare the previous forecast value and the actual sales volume,then draw a relation schema.The results shows that the forecast sales volume of new goods in daily consumables under the mean value prediction model is closer to actual sales.The forecast sales volume of seasonal goods in commodity products under the moving average prediction model is closer to actual sales.