Case Study: Faster Customer Response Times
  • Share this:
  • Manufacturer Reduces Customer Response Time, from days to 4 hours

    Data drives numerous processes across the business. When promising Orders to customers it’s critical the information is accurate and that you keep your commitments. Good customer service also requires that 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.

    Company

    The B2B manufacturer supplies thousands of customers 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 status updates to customers, and also making sensitive decisions about customer orders and trade-offs in the supply chain.

    Typical Company with complex supply chain network - DvSum

    Challenge

    Recent acquisitions meant new customer segments and attributes, an increasing product portfolio, and more production and stocking locations. This all increased the network complexity and planning challenge, and meant more data to manage.

    As complexity grew, data quality suffered. This led to poor visibility and unreliable information on order statuses, including projected ship and delivery dates. Mistakes impacting customers increased and quickly escalated the problem. Increased manual validation of the data just slowed the process further and teams couldn’t gather accurate information in time to respond to customers.

    Where is my order - order visibility in the supply chain - DvSum

    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.

    Gaining efficiency by applying Machine based data analysis to improve business efficiency - DvSum

    Data Exceptions, Root-cause Analysis

    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.

    Benefit

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

    Order Reconciliation across Systems

    Cross-functional processes involving Orders, Inventory and Shipments, result in the same data elements existing in multiple places. Often these details don't match. Using unique programming logic, the solution checked and reconciled data across the multiple systems to determine exceptions and the reasons for any discrepancies, like a delay in the delivery of an Order.

    Benefit

    Having data validated across systems helped get everyone on the same page and unified the cross-functional teams and processes. The solution also accelerated multiple functions. Where previously teams did manual validation back and forth on Order details, they now had confidence in the data and knew the cause of exceptions, which expedited the resolutions.

    Process Exceptions & Data Correlations

    The Solution also provided detailed reporting on data quality errors, root causes and processes exceptions. This enabled expanded analysis by functions and locations, and the resulting impact on metrics like revenue and customer satisfaction.

    Benefit

    Detailed Data exceptions reporting empowered users to identify patters and correlations, and recommend fixes. For example Factory Managers identified Order patterns and capacity problems that were causing the most revenue impact. Processes were adjusted to ensure the optimal balance across asset utilization, revenue, inventory and on-time delivery.

    Business Value Delivered

    Having a solution to identify and resolve data exceptions not only improved efficiency within functions, it accelerated the end-to-end process from 2 days down to 4 hours.

    Previously the process to promise Orders and respond to exceptions, required multiple teams to manually validate and reconcile data across systems. The network and processes complexity resulted in 48 hours –2 whole days, to confirm details to the Customer. With the new data validation processes, this was reduced down to 4 hours.

    The data validation solution became mission critical for daily reporting and analysis across several functions, and as an added bonus, helped identify millions in efficiency gains, cost savings and process improvements.

    Improvements in on-time delivery improves customer satisfaction - DvSum

    Share this: