Claims:We claim:
1) The system and method for predicting seasonal stock inventory by using big data technology is comprising of the contents such as;
a. Backend server- an application predicting seasonal stock inventory is installed;
b. Any open-source framework like Hadoop for data analysis which stored data on big data; and
c. An application for predicting seasonal stock inventory
2) The system claimed in claim 1 wherein, system would categories seasonal inventory.
3) The system claimed in claim1 wherein, system would improve demand forecasting.
4) The system claimed in claim 1 wherein, system would identify timelines of seasonal demand.
5) The system claimed in claim 1 wherein, system would know retailer product lead times.
6) The system claimed in claim 1 wherein, system would streamline order fulfilment.
, Description:FIELD OF INVENTION
The present invention discloses the system and method for predicting seasonal stock inventory by using big data technology. The invention is related to inventory management, which are now well developed due to upcoming technologies. Big data is one of the best technologies which can used for inventory management.
BACKGROUND OF INVENTION
Inventory is generally categorized as raw materials, work-in-progress, and finished goods. Retailers typically refer to this inventory as “merchandise”. Common examples of merchandise include electronics, clothes, and cars held by retailers.
Seasonal inventory is stock which is in high demand during particular times of the year, such as during Christmas or Diwali. These periods of time often coincide with the different seasons, and managers need to be proactive in preparing for the waxing and lightens of demand during these key times.
A surge in demand for such products has obvious benefits for retailers specialising in these items, such as increased sales and moreover, increased profitability. However, the sudden influx of demand is usually accompanied by a drastically reduced demand soon after. This can be a difficult thing to judge in terms of inventory control, as businesses need to order just the right amount of inventory to meet demand.
In this situation, businesses want to have just enough inventory to meet demand, but this can be a difficult task to achieve. Seasonal inventory may result in over-ordering of stock, and if supply drops sooner than expected, retailer may be left with an excess amount of stock. For this reason, while seasonal inventory can be an excellent amount of stock. For this reason, while seasonal inventory can be an excellent time for increasing sales, it can also pose a real challenge to your inventory control processes.
Currently, there is no such a system, that can predict seasonal inventory stock.
The present invention solves the above issues. The system, by collecting the online social network data and information created by users, system suggest the best seasonal stock of inventory which is needed most. Those big data technologies give us a new thought of developing the system and method for seasonal stock of inventory.
Big Data Technology can be defined as a Software-Utility that is designed to Analyse, Process and Extract the information from an extremely complex and large data sets which the Traditional Data Processing Software could never deal with.
OBJECTS OF THE INVENTION
The main object of the invention is that, the system and method for predicting seasonal stock inventory by using big data technology.
Another object of the invention is that, the system should track historical data and plan ahead.
Another object of the invention is that, the system should use predictive analysis about the stock.
Another object of the invention is that, the system should offer discounts and market it.
Other object of the invention is that, system should calculate retailers inventory expenses.
SUMMARY OF THE INVENTION
The present invention discloses the system and method for predicting seasonal stock inventory by using big data technology. The invention application is installed at backend server. Big data technology analyses the information from various sources to get data related to future seasonal inventory. It gives suggestion to retailer which seasonal items should be needed to stock for immediate future.

BRIEF DESCRIPTION OF DRAWINGS
Fig 1 shows flowchart of the system and method for this invention.
DETAILED DESCRIPTION OF THE INVENTION
The present invention discloses the system and method for predicting seasonal stock inventory by using big data technology.
Big data is the vast volume of structured or unstructured data that is readily available at our collective fingertips. These large data sets are analysed to reveal previously unknown patterns and provide insights into businesses, human behaviour, and more. Big data analysis is used for everything from financial services, to gauging someone’s productivity, to even monitoring the weather and also detecting the traffic jam on particular road.
Big data collect data from various sources like internet, website and social media to know about which seasonal items should be stocked for coming seasonal event or festival. Also, it suggests how much seasonal items should be stocked and for how long it should be stocked to prevent any loss in business.
The key feature of big data is to analyse, understand and interpret it in order to give user a full a picture as possible about the predicting seasonal stock inventory.
The system and method for predicting seasonal stock inventory by using big data technology is comprising of the contents such as;
Backend server – An application predicting seasonal stock inventory is installed
Any open-source framework like Hadoop for data analysis which stored data on big data.
The predicting seasonal stock inventory application
Big data consists of five stages such as-
Data sources- web and social media, machines, Sensing, IoT
Content format – structured, semi-structured, unstructured data
Data stores – document oriented, column oriented, graph database, key value
Data Staging – cleaning, transform and normalization
Data processing – Batch and real time
The system and method for predicting seasonal stock inventory by using big data technology is comprising of the following steps such as;
Step 1) An application for predicting seasonal stock inventory is created and data for that an application is provided by big data
Step 2) Inventory team member start the invention application
Step 3) Inventory team member get the list of seasonal items which should be stocked for particular period
Here, the role of big data technology is as of;
Big data technology analyses the information from various sources to get data related to future seasonal inventory. It gives suggestion to retailer which seasonal items should be needed to stock for immediate future. And also, it gives suggestion how much (tentative quantity) should be stocked, so no business loss happened in future.
Big data have some advantages for the invented system, such as;
Categorise seasonal inventory – Categorising inventory stock based on product life cycle helps to differentiate the purely seasonal products from perennial product that are sold year-round but experience times of increases demand according to the season. Purely seasonal products are those items that attract no demand outside of the season with which they are associated. These products include such things as Diwali, Navratra decorations and confectionery.
Improve demand forecasting – Forecasting demand is one of the greatest challenges to inventory control for seasonal demand. At best, retailer taking an educated guess at how many sales expect to make within a given period, but there are many tools available to assist with seasonal forecasting that help predicts basic or seasonal trends. Analyse historic data and seasonal variations to identify issues that may affect the replenishment of perennial products throughout the year and use this information to set minimum stock levels. Undertake an inventory audit by manually counting inventory stock leading into busy periods to ensure the recorded quantities match the actual in-stock numbers.
Identify timelines of seasonal demand – To plan appropriately for peak times, retailer need to understand the length of market peak seasons. This enables retailer to determine degree to which retailer can respond to product sales throughout the season.
Know product lead times – Product lead times will influence many purchase decisions. Long lead times dictate purchasing choices are made in advance of peak demand, while short lead times mean sales during the season will inform additional purchase decisions and reduce inventory risk. A quick response also helps is sales exceed forecasts. Even if lead times prevent reorders being placed with the original supplier, an alternative source with a shorter lead time may be found close to home.
Streamline order fulfilment – Managing inventory packing, tracking and shipping can be a challenge when seasonal demand brings an influx of orders. Implement an order management system to overcome issues and ensure smooth fulfilment tasks during holidays. Software will help retailers seamlessly manage back-end operations, including inventory tracking, order management and smart, more response replenishment.
Timing is everything – Understanding the dynamics of seasonal inventory helps businesses determine the best inventory control method for them. Forecasting, purchasing and channel replenishment need to be approached in different ways for different seasons and products. The aim is to have enough available stock to meet demand but not be left with a surplus and unsaleable stock post season. Monitor sales and if retailer do have stock that is not moving as well as anticipated don’t leave seasonal clearances sales to the last minute. Retailers should start offerings discounts a few weeks before the end of the season to clear out seasonal inventory.