With every advanced technology being implemented across industries, the conventional methods of operations are not applicable anymore in the business ecosystem. The crisis in the retail industry today is no different. While millennial shoppers expect much more than just a buying-selling experience, retail providers are forced to deal with express deliveries, smooth e-commerce experience, consumer satisfaction, and post-sale support. Robotic Process Automation (RPA) has already been an optimum solution in retail by improving the workflow of several repetitive tasks such as inventory management, returns processing, supply chain, and much more. However, the pandemic has left a lasting effect on the changing customer behavior in digital retail.
In our previous post on the importance of cognitive automation in the retail landscape, we discussed how cognitive technologies such as AI and machine learning take retail operations one step ahead of RPA. Although RPA has shown great promise in terms of supply and demand planning, retail providers are looking beyond conventional RPA to find equilibrium between supply and demand in the post-pandemic world. Cognitive automation in the supply chain leg of retail will allow organizations to predict demands and be 100% ready to cater the unexpected spikes and downfall in demand.
Cognitive Automation in Demand Sensing
From a practical standpoint, conventional supply and demand planning wasn’t exactly a cakewalk for organizations. They had to extract data, collate them, and convert them into pictorial representations. Eventually, the workforce had to find anomalies in the data and then plan accordingly. Moreover, several known variables pertaining to a product also needed to be factored in these projections. Henceforth, automation technologies are taking over long-standing demand-planning methods in order to produce a real-time data on market events with the help of artificial intelligence and machine learning techniques.
However, organizations have been predicting future consumer demands based on past experiences and historical data that might be misleading due to obvious reasons. This technique fails to adapt to the changing market climate in real-time or enhance productivity across the supply chain. The bigger picture here is that demand trends are often hidden in huge amounts of voluminous raw data downloaded from various sources, which makes it highly impossible for the professionals to comb through all the data to identify demand patterns.
Elucidated below is a similar challenge faced by one of the retail providers while trying to improve the transparency of their supply-demand planning.
One of the leading gift manufacturers in the UK wanted to improve their vision in their demand planning and execution with the notion of expanding their global presence and holding a competitive edge against their competitors.
In light of the complex supply chain and recurring global bottlenecks, the main aim of the organization was to analyze multiple demand types and reduce the friction in the chain from the ever-changing demand. The organization started to experience demand peaks and troughs on one of their bestsellers and unable to prioritize supplies as a result. As traditional planning required continuous human intervention, the productivity of the employees plummeted. The gift manufacturer also wanted to reduce forecast bias and ensure an unbiased demand forecast.
This is where AI-powered RPA comes into play. Unlike human brains, cognitive technologies always complement large volumes of data and ensure 100% accuracy while delivering insights. They were able to deploy cognitive RPA in their business processes and increased their visibility on inventory positions. The automation of demand planning and the successful implementation of cognitive technologies was a critical initiative that the gift manufacturer had prioritized while achieving the following:
Real-time Decision Making
Cognitive RPA provides you the luxury of automating mainstream processes, involving voluminous data, within demand planning to the next level by analyzing and interpreting meaningful demand patterns. The use of AI and machine learning made the process almost 50% faster than RPA and at the same time freed up the employees from creating short-term demand plans and stock replenishment.
With loads of voluminous data readily available, cognitive RPA takes a holistic approach by processing the entire data to decipher subtle demand patterns that the human workforce had missed out. By collating data from ERP and IoT systems, the technology was able to offer accurate demand planning forecasts even after factoring in exceptions such as calendar of events and seasonality of products.
The gift manufacturer was also able to integrate those forecasts with supply and inventory planning to have a better visibility on inventory stocks and automate reminders to have the right amount of products in the store.
This is one of the hallmarks of cognitive RPA since the technology relies on real-time calculations to counter the disruptions in the supply chain rather than depending on historical data to provide forecasts. Cognitive RPA is the perfect fit for the changing customer behavior especially during times like these, while improving sales and customer satisfaction. The gift manufacturer was able to work with real-time decisions and transparent inventory levels.
Deploying cognitive RPA in demand planning is becoming imperative for retail providers in order to maintain the golden spoon against their competitors, impress customers, and accelerates business profits.
5 Steps for Transforming Supply Chain with Robotic Process Automation – Download Article
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