Automation in production and logistics
Companies with an automation strategy see potential in performance, whether they are transporting or producing material. RPA can help your organization focus on its core specialization, while reducing costs and increasing timeliness, accuracy and flexibility.
Systems from ERP to production and warehouse management or shop floor supervision put the flow of information to the test. Using intelligent automation solutions allows for faster and error-free transfer of information between systems. This happens without system-level integration, because RPA has a lightweight structure that can make it work without an IT integration project.
Onboarding the supplier
The collection and evaluation of the provider’s data may take hours. RPA may download data to allow for an initial evaluation.
Using templates and extracting data from e-mails can start the process of creating basic supplier data – with additional possibilities such as marking deviations from best practices. To create a recommended list of suppliers, you can validate and apply the supplier selection criteria and the reason for selecting suppliers.
Easier purchasing process
With the help of robots you can achieve a reduction in the time of handling invoices, increase the accuracy and timeliness of purchases.
By using order-dedicated robots and invoice reconciliations, the results show improved material availability, reduced human error and fewer internal queries about order status.
Invoice reconciliations with exception handling can be automated. Using artificial intelligence for order data, you can predict supplier performance and mitigate operational risks.
Digital workers can improve customer satisfaction by updating their delivery schedules.
Sales staff and planning teams can use data from logistics companies to establish locations and check the availability of components. When the same information is available on the customer portal, it is easier to estimate delivery dates thanks to the information that is automatically provided on the order status.
Warehouse management systems can be automatically updated from external systems
transport in a safe and compatible manner.
Demand and supply
Machine learning offers great potential for combining the order book with seasonal trends and historical data.
Orders and sales may use detailed data on demand, delivery times and production execution. Automation helps improve delivery performance and customer service. Results can also be seen in inventory reduction.
Forecasting can be improved using data on production waste, scheduled maintenance and available resources. Using the knowledge generated by RPA can trigger the process of checking new suppliers in case of increased demand or updated delivery dates in the online store.
The use of intelligent automation to plan production interruptions can significantly reduce downtime and maintenance costs.
By using sensory data from production machines (e.g. vibrations or other predispositions), unplanned maintenance interruptions can be avoided and executed on demand. With scheduled maintenance you can avoid failures and plan machine downtime. With downtime included in your production plans, you can better plan your production effort and keep your customers informed of potentially changing delivery schedules.
There are many ways to benefit from this transformation in planning, including human resources and material requirements.
Quality assurance teams can reduce the response time to complaints by automatically filling in customer and sales data.
When complaints are automatically classified and sent without errors to the correct team, the time taken to process complaints is also reduced.
Time spent by staff on quality issues can be automated and cross-checked by date or batch so that root causes are analysed using immediately available data. RPA thus makes it easier for staff to focus on more urgent issues, while improving the quality of customer service through timely handling of complaints.