How Machine Learning Can Manage Retail Store Operation
Retail stores often face challenging task to deliver products to customers. Managing inventories, labor, supply chain, logistic, delivery time, IT Infrastructure, Warehouse management, Omni-channel management, sales, marketing, store design, pricing etc. Well!!! Machine Learning can handle all the work for you.
People take action based their immediate needs and speculating behavior based previous data still presents lot of uncertainty. Everyone has priority of behaviors and they often act on the most immediate needs. How this data can be used by the retail stores. Through social media, by doing the old fashion market research of asking the consumer what products are they likely to buy. This makes the argument more consumeristic and people centric .
What do you do when the consumer spending dwindles out?
When the consumer spending seizes out, then retail stores need to manage inventories in such a way, holding inventories which are in immediate of the consumer. People buy products based on priorities which is of immediate need to them. First quarter of 2017 was bad for retail stores, but they can shrug off the loss by managing inventories inline with the consumer demand.
Demand for certain goods would have fallen during tough but people still buy goods that are essential. Bulking up inventories isn’t a good idea for retail stores.
Forecasting sales is really important to allocate resources to meet the demands of the consumer. Machine learning can predict sales from the previous history of sales and precisely knockout how the marketing was driving the traffic for the sale. Resources like labor and inventories can be adjusted based on this prediction. Pricing of goods play crucial role in volume of sales. With the regression model, pricing of goods can be managed so that the variables that affect the pricing like transportation cost, gasoline, taxes, regulation, etc can be mapped. This brings lot of variables into equation but still there will be uncertainty. When the retail sales decline, it shows a trend that the priorities of people have changed due to some reason.
Marketing, promo, ads, PR, social media, events, coupons, season, etc play a huge role here, as they drive traffic to the store. The campaigns can be managed to target specific audience who are likely to come back to the store as well as new audience with similar preference. Social media is great place to do old fashion market research on the consumer preference. This can be reward providing insight about the priority and their future behavior.
Future Behavior of the consumer isn’t based on the previous behavior but on the actions taken by the individual to solve his most immediate need. Listen to consumers and fill up inventories on that predicament.
Retail stores manage ton of resources like IT, Warehouse, labor, infrastructure, logistics, inventories, customer service, store design, etc. All the resources are planned out by the omni-channel team. Machine learning can identify key resources which are causing loss of cash. This causes reallocation of resources. Procurement team often gets all the purchase orders sorted out to new services or existing services. Machine learning can predict the cost of new product acquirement and it’s impact on retail business.
Stock management leads to delivery of inventories at the right time. Stock management depends on the sales forecast, seasonality, events and the marketing campaign, promo, PR, coupon, discounts and flash sale. Anticipation of sales is difficult and it varies according to consumer behavior, competition, technology, time and various factors. Disappointing sales can lead to bulk of inventories. Lowering prices can help to ease out inventories preventing retail losses. Warehouses can become huge liability if the retail sales dwindles.
Delivery of inventories to the warehouse and to the consumer on time helps in meeting the demand. Transit time often plays havoc in fulfilling orders. Placement of delivery system is important when there is a surge in sales in one region and inventories need to delivered quickly. Just like Uber places their cars to route efficiently. Calculating price is a key variable of the product based on the cost incurred. Regression model help anticipate the fluctuation in prices to aid better judgement.
Supply chain is about anticipating the demand and supply to divert cash into productive tasks. Transit time between the production to the warehouse as well as to the consumer. Once the orders come in then resources need to reallocated to meet the demand before deadline. Machine learning classifiers can provide probabilistic models.
Line of production works to meet the consumers need. Routing resources to produce product on time brings customer satisfaction. This involves use of machinery and expertise to create a well crafted product. Based on the market research new product designs are made and cash is routed to purchase inventories and labor is used to make finished products. Machine learning can check the quality of the products, predict the delivery time from line of production to finished product, cost involved, resources etc.
All these optimization leads higher productivity since Machine learning enhance every part of the retail operation. It provides data for future planning, anticipate recession, reduce inventory pile up, and much more…
Retail stores need to adapt to changes quickly if they wish to shrug off the early sluggishness for the year 2017.