I was recently able to speak with Dipanjan Das, Vice President of Order to Cash Practice and Rana Saha, Transformation Leader at Genpact about how organizations can use predictive analytics to re-design their order management process to match customer needs; resulting in increased revenue, brand visibility, and customer satisfaction.
Dipanjan and Rana will be speaking on our free webinar on July 30 at 11:00 a.m. CDT: Using Analytics to Re-envision Order-Management Processes for Improved Customer Loyalty
APQC: What are the signs that an organization’s order management process needs to be re-designed?
Dipanjan: There are typically efficiency (cost) and effectiveness (impact on business metrics) indicators. Often the leading indicator is when costs are high when benchmarked to peers for various reasons such as non-standard process leaning heavily on manual order entry or multiple back and forth between order management teams and customers, and logistics service providers or warehouse and distribution teams. From an effectiveness perspective, metrics such as perfect order index and voice of customer can also pinpoint issues.
APQC: In your experience which phase(s) of the order management process tends to break down most often and why?
Dipanjan: Where the process breaks down can vary by industry and region or market. Broad manufacturing industries have a different set of challenges from services industries. And within manufacturing, fast moving supply chain or make to stock industries (e.g. CPG) could have issues around order visibility across the supply chain which can lead to order cuts and poor on-time delivery.
Make-to-order (e.g. heavy manufacturing) industries can have challenges on milestone tracking, project setup and installations. Service industries on the other hand, would have very different challenges – mostly in regards to the volume of orders with comparatively lower value and having an effective service delivery model to ensure customer satisfaction and service delivery. In service industries (e.g. media) there would be potent issues around auto renewals and probable revenue leakage from the same when the customers don’t want to continue the service as is, or want a different set of services.
In addition regional nuances are based on the go-to-market model (e.g. if direct sales are prevalent the challenges are different when compared to distributor-led models).
APQC: How important do you find customer segmentation models for effective order management?
Rana: Every customer is different and it’s important to understand their needs. Customer segmentation can measure the customer experience from the order management process and transform the process where required. Take a consumer package goods (CPG) organization, for example, a national retail customer might have a very different set of challenges from the manufacturer or the CPG company when compared to an export customer or food services customer.
Similarly in pharmaceutical drug manufacturing, in Europe, a distributor will have different set of challenges compared to a third party or direct sales customer. Segmentation is important to aggregate performance at the right level and drive process transformation.
APQC: Who would you recommend participating in the order management process redesign?
Rana: We would recommend running a three-day value stream workshop that includes representatives from sales, customer development, demand planning, product supply, IT, distribution, transportation, customer services, and order management.
APQC: What types of customer experience analytics do you find the most useful? What dimensions of customer service are particularly useful for predictive analytics (e.g., time, dependability, communications, convenience or flexibility, or satisfaction)?
Dipanjan: Analytics that bring out what elements of the order management or supply-chain processes are hampering customer experience are most useful. Is it master data, supply chain visibility, demand planning, operational issues with order processing, or logistics issues?
From a predictive analytics perspective, historical patterns on order delivery timeliness, fill rate, accuracy of price, timely communication to customer in case of changes, for example , when modeled correctly, provide valuable insights.
Join us for our July 30 webinar to hear Dipanjan and Rana discuss:
- designing an order management target operating model that meets customer needs,
- gaining predictive intelligence on customer relationships through data-led customer experience scores,
- enhancing order management process governance and controllership, and
- applying technology to transform order management processes that build supply chain visibility and enhance the customer experience.
Learn more and register today!
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