A Report by Zebra’s 2018 Shopper Vision Study says that 66% of the shoppers preferred a same-day or the next day delivery, while 41% of the consumers used two or more channels during the buying process. Hence, the challenge for retailers is to meet these lofty expectations from the consumer’s end. In fact, for brands it is a case of, “adapt or perish”. Across industries if businesses fail to change they will be overtaken by their competitors. As far as the retail industry is concerned, they face considerable challenges because of the humongous volume of products and also the fast moving nature of the products increases the complexity of the retail supply chain management. The question that arises is what the way forward for the retail industry is.
RPA & AI – The next level of Retail
Experts are of the opinion that a synergistic combination of Artificial Intelligence and Robotic Process Automation solutions is the next big thing in retail. A healthy marriage between both these technologies can solve a lot of problems that the retailers face. The primary importance of AI and RPA solutions is to reduce the amount of human error and make business processes more efficient. Just like automated checkout, personalized recommendations, churn rate minimization, customer segmentation are some of the powerful use cases of using AI solutions in retail, similarly supply-chain management in retail is an apt area where a combination of AI and RPA can bring forth unexpected results.
However, initially robotic process automation solutions used in retail supply chain management was not flexible enough to handle some of the most complex scenarios because they were not intelligent enough. This is where a harmonious blend of RPA and AI had a major role to play. With the incorporation of intelligent bots infused with machine learning capabilities and cognitive abilities, RPA has come to resemble humans to some extent.
Among the emerging markets across the globe, retail has been one of the most growth oriented sectors. A report by eMarketer says that the retail industry is expected to reach a figure as high as $27.7 trillion in global sales by 2020. However, whether it is the brick and mortar store or the ecommerce store, many processes are actually involved behind bringing a product from the stage of production to the shelf for selling. For instance, processes like supply chain management to sales analytics were done by humans even a few years back but with technological innovations like robotic process automation implementation, brands are looking beyond human capacities so that business processes can be managed more efficiently and accurately.
The Fusion of AI and RPA in the Retail Sector
At the core of the retail sector is Inventory Management. There was a time when humans had to sync between all channels of sale for a brand and keep a tab on the inventory levels as well, and hence it was quite a complex task with higher chances of error. However, with RPA, inventory management became far easier. The software can actually take care of the inventory levels and notify the concerned people when the stock levels are low and actually re-order the products when they go below a certain level. However, AI and Machine Learning go a step ahead and do the task of forecasting the inventory levels based on customer demand analysis. This forecasting happens based upon some intelligent algorithms and the result is increased agility and optimization in the supply-chain along with a digital transformation in the retail industry.
Customer service, which is an important aspect of the retail sector, is up for the next level with the incorporation of cognitive bots. The NLP powered bot is able to identify a customer request, understand their emotion, and offer the right kind of solution and at the same time triggers backend processes with the help of RPA so that a faster implementation happens.
Let us run through a use-case where AI and RPA extract each other’s synergy to bring forth an amazing customer service:
Customer: This is ridiculous! I had asked the customer care representative to notify me when the bill payment date is due, why has that not happened?
Bot: I am really sorry Mr. Smith – Let me have a look into the matter
Analysis: The bot senses the tone of the customer and associates the right kind of emotion with it and uses a tone that is in sync with the customer sentiment.
Bot: Thank you Sir for waiting – I just checked that we had this conversation 12/23 but we did not update the account on time and hence the miss
Analysis: The bot understands the context that the customer is talking about referring to the past conversation and his request for a specific request and it is at this stage RPA comes to action whereby the Bot accesses the required system at the backend and does the necessary processes so that the desired information can be given to the customer with minimal wait period
Customer: I am really disappointed
Bot: I am really sorry for the inconvenience caused – I can make some adjustment so that you need not have to end up paying any late fee
Bot: So, Mr. Smith, shall I proceed?
Analysis: The Bot comes up with a corrective measure based on the intelligence which in turn will solve the issue faced by the customer
Conclusion: This is a case where the Bot has the capacity to implement corrective actions and also trigger RPA Bots at the backend so that the necessary adjustments can be made in the customer account.
Order Processing and Payments form a cornerstone of the entire supply chain management. With the help of RPA, order processing and payment processing can be automated so that the information is directly fed into the database, and payment gateways can process the required amount and also a software solution can send email confirmation for the order placement. However, all these processes need to be integrated tightly so that the productivity is optimized and this becomes possible only by converging AI with RPA.
Last but not the least, there will be a massive retail digital transformation in the days to come in the days to come. However, to stay abreast of the change that is coming about in the retail sector it is imperative that cognitive software systems are brought forth and there is a robust implementation of artificial intelligence so that the whole process becomes a lot more simple and flexible. Nevertheless, the one-size-fits all is not a desirable model for the retail sector, it should primarily be need-based and RPA implementation must be designed taking into consideration the end goals.
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