Within the next two years, 72% of companies are expected to be using robotic process automation (RPA) to minimize costs, reduce transaction times, increase productivity, and improve levels of compliance. At least that’s what is suggested by a 2017 study by global technology research firm Information Services Group, reported on by the Chartered Institute of Procurement and Supply. Moreover, the ISG study shows that the automation technology is allowing for a 43% reduction in resources needed for order-to-cash processes, 34% for invoicing, and 32% for vendor and talent management.
Those are tremendous gains for any company, but especially for those concerned with effective management of their complex supply chains. The uptake of automation within the supply chain has, until recently, been slow. However, the development of new capabilities for automation technologies means that a growing number of companies globally are relying on RPA to streamline the flow of goods on their supply-side and gain a competitive advantage with customers on the demand-side.
But how, more specifically, is the leading technology trend poised to impact supply chain management? What are potential use cases as well as their logistical benefits? What can be expected of software robots in the future? Let’s look at the potential for automation within the supply chain.
In optimizing their supply chains, companies across many industries — manufacturing, retail, healthcare, and more — have long relied on a range of technologies: TMS (transportation management system), ERP (enterprise resource planning), CRM (customer relationship management), and RFID (radio frequency identification). Still, automation technologies like RPA have only gradually been adopted within supply chains... until now, that is.
In the very beginning, RPA software robots were unintelligent and lacked the agility required to handle the skill-based, non-standardized interactions of complex supply chains that depended on human intervention. However, continuing advancements in the evolution of the automation technology show great potential for supply chain management. More and more, the incorporation of cognitive and knowledge-based capabilities with RPA is allowing software robots to act like human employees. In fact, intelligent automation is developing as an overlap between cognitive process automation, intelligent computer vision, and intelligent OCR (optical character recognition) to automate beyond tasks based on well-defined business rules and clear instructions for processing inputs.
As part of this, knowledge-based capabilities allow for judgements based on data patterns. Within supply chain management, for example, this level of automation can involve automated delivery delay escalation, customer service bot interactions, and change requests for transport slots. At an even higher level, cognitive automation relies on complex algorithms and pattern recognition guided by self-learning to make predictions and support decision making. With respect to supply chains, cognitive automation can involve the automation of supply/demand balancing as well as vendor selection.
As a result of such cognitive augmentation, RPA is being increasingly adopted within the supply chain to mimic the actions of human employees: capturing, replicating, and processing data, communicating with customers, as well as making judgements and learning from past actions. Take, for example, a leading food producer based in Europe looking to streamline its vendor and customer relationships. With the adoption of RPA, the company was able to automate a range of processes on the supply-side and on the customer-facing side.
In selecting and procuring vendors for seeds, fertilizers, and transport materials, the food producer engaged in a highly manual process that involved employees preparing an RFQ (request for quotation) package, communicating to vendors, performing a preliminary analysis of vendor documents, evaluating the vendor and running a credit check, as well as finalizing the vendor selection. Upon implementation of RPA, the company was able to automate the majority of these steps. Human intervention was only required for the preliminary work involved with specifying the project for sourcing, generating a list of potential vendors, and engaging in face-to-face site visits and negotiations. Post-automation, the food producer was able to improve cycle time by 25-50% and processing time by 15-45%.
The food producer in question regularly receives inquiries from customers about the status of their order shipment. Prior to automation, shipment status communication was entirely manual: the employee received and opened the customer email as well as opened the shipment system to find the shipment record in ERP. The employee then gathered the necessary information, sent a status update to the customer, and closed the case in the system. RPA, however, was able to take over opening the email system, recognizing text from the customer, logging into the shipping portal, determining the shipment status, replying to the customer, and moving on to the next customer email — with human intervention only being required for exceptions. Post-automation, the food producer was able to eliminate 40-60% of the manual effort required in answering customer status queries.
Planning is a crucial component of the management of any supply chain, especially with regards to predicting future requirements of supply and demand. Prior to automation, such planning was no easy task: Employees were tasked with seeking out and gathering the necessary data–for example, from vendors, customers, market intelligence, as well as the production and sales teams–, combining the collected data into a standardized format, running simulations, analyzing data exceptions, and confirming and communicating the plan.
With RPA, the company was able to automate the majority of these responsibilities: gathering and merging the necessary information from various sources, running data cleansing tools, as well as transforming the final data into a plan and providing the necessary communication to partners, customers, transporters, and logistics teams. Post automation, the human role was limited to handling robot exceptions, running simulations, and running supply and demand meetings to seek plan consensus. The food producer attained 20-40% improvements in the data collation and admin effort involved with supply and demand planning.
With RPA, supply chains attain enhanced cycle time and agility, increased capacity and asset efficiency, improved receivables, as well as high levels of supplier, customer, and employee satisfaction. But in addition to recognizing these benefits, it’s also important to acknowledge the foresight that is needed in order to leverage automation successfully at scale. Companies should set up an RPA governance team and steering committee, put thought into process selection, and foster discussions around the redeployment of employees.