Intelligent Automation Solutions in

Manufacturing & Logistics

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Harnessing the potential of intelligent automation in manufacturing

Companies with an automation strategy find potential for efficiencies whether they move material or manufacture it. RPA can assist your organization to focus on core expertise while reducing costs and increasing timeliness, accuracy and flexibility.

Systems from ERP to manufacturing and warehouse management or shop floor supervision challenge information flows. Applying smart automation solutions allows system-to-system handovers that are faster and more likely to be error-free. This happens without system level integration as RPA has a light structure that can make it work without requiring an IT integration project.

Heatmap for Manufacturing & Logistics processes

high
Automation potential

High

medium
Automation potential

Medium

Supplier Management Procurement Logistics Inventory and Planning Manufacturing Customer Delivery
RFP / RFQ generation and data aggregation Purchase order creation and matching Logistic service provider contract management Demand and supply daily reports BoM generation Order status updates
Vendor mapping (metadata search) Matching and reconciliation of invoice and goods receipt Performance based delivery time updates Material transaction updates Predictive maintenance Return processing, automated order information collection
Contracts management Vendor performance reports Delivery management and tracking, proof of delivery Material master data maintenance, e.g. PDM and ERP Production plan generation Customer complaint handling, order information and forwarding
Vendor selection Freight tendering Customs clearance documentation handling Demand forecasting Production cell-to-cell data transfer
Supplier risk management Material requirement and shortage reports EDI data handling Available to Order (dynamic delivery time) Scrap / Waste reporting
Update scorecards and dashboards Vendor managed inventory (VMI) reports and replenishment Shipping information generation and extraction Inventory level optimization In-cell quality control & cross-referencing

Process examples

manufacturing-supplieronboarding

Supplier onboarding

Collecting and assessing vendor data can take hours from your day. RPA can source data to make a pre-assessment possible.

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Using templates and extracting data from emails can start the process of creating supplier master data - with additional capabilities such as flagging deviations from best practices. Selection criteria for suppliers can be validated and applied to create a recommended list of who to choose and why.

better-purchasing

Better purchasing

Reducing the handling time of invoices, improving accuracy and timeliness in purchasing can be achieved using robots.

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With robots dedicated to requisitions and invoice reconciliation the results show improved material availability, reduction in human error, and fewer internal inquiries about the status of procurement.

Invoice reconciliation with exception handling can be automated. By applying AI procurement data a forecast is possible to determine supplier performance and enhance operational risk mitigation.

manufacturing-trackingdelivery

Tracking delivery

Digital workers can enhance customer experience through updating delivery schedules.

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Sales department staff and planning teams can utilise data from logistics service providers to view tracking and find out more about component availability. When the same data is available through the customer portal there is more information about order status automatically shared to help estimate delivery dates.

Customers’ warehouse management systems can be automatically updated from external transportation systems in a secure and compliant way.

demand-supply

Demand versus supply

Machine learning offers great potential for combining the order book with seasonal trends and historical data.

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Procurement and sales can use detailed demand data combined with supplier delivery dates and production lead times. The improvement in delivery performance and customer service validates this investment in automation. Results can also be seen in decreased inventory.

Forecasting can be improved with the addition of data on production waste, scheduled maintenance and available resources. Applying knowledge generated by RPA can trigger investigation of new suppliers due to increased demand or automated updates in online store delivery dates.

maintenance

Predictive maintenance

Using intelligent automation to schedule production breaks can significantly reduce down-time and maintenance costs.

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By using sensory data (e.g. vibration or other predictors) from production machines,  any unplanned maintenance breaks can be avoided and performed on-demand basis. By using predictive maintenance, manufacturing can avoid catastrophic failures and plan the machine down-time. With the down-time incorporated into production plans, the company can better plan its shop-floor resources and keep customers updated on potentially changing delivery schedules.

There are many other ways to benefit from this transformation in scheduling from human resources to material requirements planning.

manufacturing-customercomplaints

Customer Complaints

Quality assurance teams can improve response times for complaints with auto-populating customer data and sales.

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When complaints are automatically classified and forwarded to the right team without errors, complaint resolution times are also reduced.

Staff time spent checking quality issues can be automated and cross-checked by date or batch so that root causes are analysed with immediately available data. RPA benefits staff who can focus on more pressing issues while improving the customer experience with timely resolution of complaints.

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ErikGaarder_DWF_Web kopio

Erik Gaarder

Senior IA Consultant
+47 92091089
erik.gaarder(at)digitalworkforce.com

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