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Case Study: Improved Customer Response from Better Data

B2B Manufacturer Reduces Customer Response Time, from days to 4 hours – Thanks to Accurate & Reliable Data

Data drives numerous Processes, and when promising Orders to customers it’s vital the information is accurate and that you keep your commitments. Good customer service also requires the responses are timely. Manual validation and lack of confidence in the data, has significant impacts on the speed of business processes and the ability to react to change.

The Company

The mid-size manufacturer supplies thousands of customers large and small, in a complex just-in-time Supply Chain. The large product portfolio ranges from custom solutions to commodity parts in a competitive market, making customer service a key driver of market share.

Customer Service and Supply Chain Planning teams rely heavily on daily reports for promising orders, providing order status information to customers and making sensitive decisions about customer orders.



The Challenge

The increasing product portfolio meant more data to manage across planning, execution and customer facing processes, including customer attribute preferences and new product transitions. More manufacturing and stocking locations and new customer segments also increased the network complexity and planning challenge.

Data was more important than ever, but as complexity grew, data quality suffered. This led to poor visibility and unreliable information on order statuses including projected ship and deliver date. An increase in customer-facing mistakes quickly escalated the problem, however increased manual validation of the data just slowed the process and the teams couldn’t gather or provide accurate information on time.

The Solution

Planning and execution or Orders across a complex network includes multiple data dependencies. Even if a couple Orders and variables change, the trickle-down effect cascades through the supply chain. One small exception or data error can have a huge impact, so the Solution had to address multiple levels of data validation.

Data Exceptions, Root-cause analysis and Analytics

Some data exceptions were known, but it look the team too long to track down root causes, make fixes and complete analysis to make decisions and respond to customers. The first step was comprehensive data quality and exception reporting including drill down to root causes along with potential fixes and impacts.


Result

With better visibility into data quality exceptions, the teams had more confidence responding to customers quickly, knowing the information was accurate and how likely they could deliver. With root causes already identified, multiple hours were saved and the process accelerated to proactively fix data before it became an issue.

Cross-system Data Validation of Orders & Locations

Cross-functional processes involving Orders, Inventory and Distribution results in the same data elements existing in multiple places at once. For example an Order and its status may exist in ERP, Planning, Order Promising, Warehouse and Transportation systems, and often these systems to not reconcile. Using application data, advanced database functions and programming logic, the solution reconciled and validated data across the multiple systems to determine where the supply existed currently, and the reasons for any discrepancies or exceptions like a delay in the delivery of an Order.

Result

The data reconciliation across systems help unify the cross-functional teams and processes, and accelerated the respective function. Where previously they each did manual validation back and forth, they now had confidence in the data and knew the cause of exceptions, which expedited the resolutions.

Process Exceptions & Data Correlations

With the increased reporting of data quality errors, root causes and processes exceptions, the teams were able to complete expanded analysis to determine exceptions by functions and locations and what the impact was.


Result

Knowing the data accuracy and exceptions empowered users to identify patters and correlations, and recommend fixes. For example the factory managers identified Order patterns and capacity problems that were causing the most revenue impact. Processes were adjusted across planning, manufacturing and order promising to ensure the optimal balance across asset utilization, revenue, inventory and on-time delivery.

Business Benefit

Having a solution to identify and resolve data exceptions saved hours each day, previously spent manually validating and reconciling data. This not only improved efficiency within respective functions, but reconciling data across systems and functions, greatly accelerated the end-to-end process.

Previously, the data and processes complexity resulted in 48 hours – 2 whole days, being required to promise Orders and respond to customer exceptions. With the new data validation processes, this was reduced down to 4 hours.

The added bonus of the increased data validation and analysis resulted in the solution becoming mission critical for daily reporting and analysis across several functions, resulting in millions in efficiency gains, cost savings and process improvements.


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