The Manufacturing sector has always led the way with designing and adopting methodologies to gain higher efficiencies and productivity. Lean Manufacturing and Six Sigma are two of the more popular tools used to drive operational excellence. The success of Lean and Six Sigma in Manufacturing drew the attention of other industries, with the Service sector especially demonstrating its efficacy in process improvement. Now, the Service sector is adopting Robotic Process Automation (RPA) to build previously inaccessible levels of reliability, accuracy, speed and cost reduction. The Banking, Financial Services & Insurance (BFSI) sector adopted RPA early to manage its voluminous transactions and repetitive tasks (KYC, loan origination, claims processing, accounts payable, account closure, fraud detection, etc.), improve customer responsiveness, reduce error rates and meet compliance norms. Manufacturing companies can learn from the experiences of Service sector and deploy RPA to create a new wave of efficiency improvements across functions.
Using RPA, manufacturing organizations can achieve several magnitudes of improvement in efficiency, productivity, speed to market, customer delight and reduction in operating costs. The key is to learn from the best practices adopted by the Service sector and use the technology across functions like Procurement, Logistics, Planning, Finance, Production etc. for a range of processes such as Bill of Materials (BOM) management, material planning and purchase order management, invoicing, accounts receivable, order fulfilment, logistics, reporting, compliance, etc.
When manufacturing organizations consider RPA they invariably have three questions that need to be answered:
- Which are the most error-prone and time-intensive process to which RPA can be applied?
- Which of these will yield the highest ROI in the shortest possible time?
- Which processes should we make agile and responsive using RPA?
All three questions are important and must be answered. Depending on the industry, the business imperatives and the maturity of the enterprise, the answers vary. But there invariably are a handful of operations and processes that should be the first targets of RPA.
Our experience in RPA implementations in manufacturing has led us to create a short list of four areas where manufacturing organizations must apply RPA to improve efficiency and agility:
Handling demand fluctuation, scaling support functions: Manufacturing organizations have demonstrated tremendous efficiency improvements using tools for operational excellence. Their journey of improvement continues and is a constant source of inspiration to other industries. But the support functions in manufacturing have not seen the same level of concurrent efficiency improvement as operations. The opportunity for manufacturing companies is in business processes and support functions like finance, procurement, warehousing and logistics. These functions require multiple documents, data entry and routine report preparation. Processes such as purchase orders, GRN, delivery orders, invoices and shipment documents can be automated using RPA to save process time and release employees from tedious, repetitive and error-prone work. Instead, employees can be used for problem solving and driving continuous improvement projects.
Typically, manufacturing companies place considerable emphasis on skill development and manpower planning for shop floor associates (direct manpower). This is not always true for manpower employed in support functions. The result is that support functions become bottlenecks when business volumes increase. RPA is the solution. It allows organizations to manage fluctuating workloads better—without having to hire or retrench at short notices.
Enhancing Customer Satisfaction: Over the last few decades the Manufacturing industry has noted the importance of customer satisfaction (C-SAT) and has made considerable investments in improving C-SAT metrics. The goal has been to address factors that impact C-SAT such as quality, cost, delivery and responsiveness. However, in this digital era, customers expect feedback and resolution of their queries at a much faster pace. Therefore speed of responsiveness has become critical to maintaining competitiveness and retaining customers. To keep pace, smart manufacturers have been implementing RPA to reduce response time for customer queries and complaints. They use RPA to source data from multiple IT applications in a short interval (that could take hours in the earlier scenario) and ensure faster complaint resolution and response to queries. RPA can also, similarly, respond to vendor queries related to GRN status, payments, etc. without the need for any manual intervention.
Reporting & Performance Monitoring: Multiple reports are prepared by most departments to monitor shop floor KPIs and track production. While some of these reports are easy to prepare (from complexity and time perspective), many are complex and require data from multiple departments, like an integrated daily production report. Due to data being in different formats across departments and the lack of integration, these reports are time consuming and prone to error. RPA automates these reports and delivers timely and accurate reports in a cost-effective manner.
The concept of using RPA for reports can be extended to performance reviews, a trend that is catching on. Bots review performance metrics and when any data point is out of range, it is flagged as an exception with the respective manager. For example, a bot calculates the productivity data for each of the manufacturing lines and if all the lines have achieved the productivity as per defined norms, no exception report is sent to the production head. Though this use case is still at a conceptual stage, I believe this type of out-of-the-box thinking will result in harnessing the true potential of RPA in the Manufacturing sector.
Managing Complex Planning & Scheduling Process: Over the last few years, some manufacturers have invested in software for the purpose. However, the most widely prevalent practice to manage advanced planning and scheduling is to use excel sheets. Planning is an area ripe for RPA. Multiple data points like demand, capacity, material availability, FG stock levels, maintenance plan, backlog and priority orders, shipment plan, etc. need to be collated to arrive at a manufacturing plan. Using bots it is possible to mine the data from multiple sources and update standard templates. Based on defined intervals, bots can update actual performance against plan, changes in demand, actual sales data etc., based on which planners can optimise their manufacturing plan to maximize plant efficiency.
RPA presents opportunities to improve multiple manufacturing processes and functions to enhance efficiency, flexibility, responsiveness, manage workload variations and reduce costs. Most RPA investments are paid back quickly, making them highly attractive. Findings by Leslie Willcocks, professor of technology, work, and globalization at the London School of Economics’ Department of Management, show that a return on investment could be between 30 and 200% in the first year.1 The manufacturing industry, that has an extraordinary number of repetitive and labour-intensive processes, stands to make considerable gains from RPA implementations.
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