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RPA in Manufacturing

Robotic Process Automation (RPA) is a low-hanging fruit—a step towards smart manufacturingthat will help you stay competitive in the Industry 4.0 era

The Industry 4.0 era sees the manufacturing industry on a steady march towards smart manufacturing. This high-tech revolution—involving technologies like Internet of Things (IoT), Robotic Process Automation (RPA), and Artificial Intelligence (AI)—may be the solution for manufacturing to thrive amidst perennial challenges such as manpower shortages.

For example, with ubiquitous IoT devices feeding data from machines, technicians and operators spend less time and energy physically going back and forth from machine to machine. Each person can now man more machines, and can even do so remotely.

But signals and data need to be analysed before they can become useful. This is where RPA and AI come in.

Role of RPA in Industry 4.0

RPA helps to link dispersed, disconnected devices together—it can be used to automatically and quickly fetch data from disparate systems and link them together, for example in the ERP system or by creating reports. By incorporating AI capabilities, RPA can also automate data analysis and some cognitive decision-making. RPA can also be used to automatically trigger actions.

RPA eliminates Lean Manufacturing wastes

RPA helps manufacturers become more lean, agile, and productive. In fact, RPA helps eliminate several aspects of the 8 wastes of lean manufacturing. For example:

  • Automating the generation of  Bill of Materials (BOM) cuts down the wastes of waiting and defects. It reduces errors and the amount of time spent, thus resulting in shorter time-to-market and less waste or rework.

  • Automating stock item reordering cuts down waiting and inventory because it results in a shorter internal reaction time and consequently lower buffer stock levels.

  • Cutting down manual processes frees up workers’ time from mundane tasks (cuts down under-utilisation of talent), and allows better use of their expertise.

Benefits of RPA in Smart Manufacturing

Better decisions through better data

Large volumes of data, processed automatically and quickly through RPA and AI, can give never-before-seen insights that provide a more complete picture and help the management make better decisions—more accurate production planning, capacity planning, and more.

Reduced reliance on manpower

Each staff member, both technical and non-technical, can now do more—man more machines, analyse more processes, process more transactions—since they don't have to do as much mundane, repetitive work like manually copying and pasting data, compiling reports, etc.

Agile and responsive to changes

By automating data processing with RPA, companies can detect changes in real-time—be it shop-floor conditions, customer demand, or changes in supply—and take corrective actions, fast.

RPA Use Cases in Manufacturing

Bill of Materials

Bill of Materials (BoM) is one of the most critical documents in manufacturing. It contains specific information on all the raw materials, components, sub-assemblies, and packaging required for production. Precisely because of its detailed nature, preparing the BoM is tedious, time-consuming, and highly prone to error—and any error can cause rework, delays in shipment, and even massive loss to the company.

 

RPA can automate the preparation of the BoM, getting it done in less time and with higher accuracy. Besides, by deploying RPA bots to also monitor the actual consumption of the items, the BoM can be updated in real-time and grow more accurate with time.

Inventory Control

Slow, manual procurement processes and lag time in inventory data reduce a company's responsiveness. This results in having to account for a longer internal lead time and provisioning excessive buffer stock, all of which add up to an inflated inventory cost that eats away at profits.

RPA can monitor customer demand, inventory levels, and production capacity in real-time and reorder items with low stock level, reducing reaction time to changes in customer demands. Digitising paperwork and automating procurement processes—sourcing, RFQs, evaluation, reporting, and more—also result in more optimum inventory levels.

Production Floor Administration and Reporting

From routine administration work—such as shift scheduling, allocation of machines, and maintenance scheduling—to strategic analysis of data, manufacturing organisations deal with a lot of data processing, which is often highly manual, error-prone, and time-consuming.

RPA can help automate these administration and reporting work to achieve more accurate results in less time. For example, RPA bots can make changes to staff and machine allocations based on pre-defined triggers, or automatically generate regular reports across different factories for analysis and decision-making by the management.

Vendor Management

Depending on company size, manufacturers often have hundreds, if not thousands, of vendors and suppliers. Communicating with vendors and maintaining vendor master data are gargantuan tasks that are highly manual and error-prone, leading to a high cost of operation.

 

RPA can automate the bulk of this manual work—creation of vendor records, checking vendor documents, checking for duplications across different entities, GST and ACRA validations, and more—eliminating human error and drastically reducing resources needed.

Regulatory Compliance

Gathering data and conducting regular inspections of the production floor are critical in ensuring compliance in manufacturing. However, preparing inspection reports is an inefficient and time-consuming task. Delays in completing and disseminating these reports negatively impact the management's ability to intervene in a timely manner to ensure compliance.

 

 With the help of digitisation technology, RPA bots can prepare and disseminate reports in minutes or seconds, enabling swift actions to be taken. Furthermore, digital information and actions taken by RPA bots leave audit-ready trails so you don't have to scramble to prepare information for regulatory audits.

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