The transition from being purely technology- and product-centric to one that puts customer feedback and their insights into the center of a business model can only be achieved by measuring processes and improving them (Liu, Guo, Lee, 2011). Nowhere is this more evident than in the company's quoting process. Ironically for a company so well-known for its build-to-order process online, tis enterprise-wide quoting systems are seriously broken and often can take weeks to produce a highly specific quote for a series of servers for example (Gunasekaran, Ngai, 2009). The lack of customer insight into why customers are ordering a specific configuration also needs to be assessed. This lack of information has led to incorrect configurations being recommended to long-standing customers and the quoting timeframes drastically increasing from weeks to in some cases months for very complex system and network configurations. The consumer and low-end configurations of systems is very efficient as it has become integrated into the overall supply chain workflows however the higher-end systems lack CRM and process integration. Proposed Solutions

There are many potential solutions to the lack of customer insight and intelligence as it relates to the quoting process. One of the most expedient is to create a small CRM system based on Salesforce.com, which is delivered entirely over the Web, and integrate the back-end systems to the quoting application, an approach used in other manufacturing companies successfully (Rapp, Trainor, Agnihotri, 2010). This approach would provide easily usable applications and would also be paid for using operating expense (OPEX) funding, an approach Dell relies on as Capital Budgeting (CAPEX) is a lengthy process and heavily reliant on the cost of capital and interest rates. The SaaS-based approach would allow for quickly defining the strategic priority of customers and also give insights into how best to create a more customer-driven enterprise quoting process (Wu, 2010). The downside of this approach however is the lack of security for the data involved and the fact that data capture and analysis is not as well defined within the Dell process workflows as well. As a result of these factors, going to a SaaS-based approach to implementing advanced CRM systems into Dell to further increase their insights into customers is not going to be effective, however it will have a rapid turn-around time to be completed.

A second approach is to define the customer data management requirements of the complex quoting systems workflows, and selectively define a CRM system that only focuses on accelerating the quoting workflows. According to an analysis of the latest levels of performance for the complex quoting process for highly integrated Cloud, virtualization and storage systems, the average turnaround time for a quote is up to 21 days. This has been derived from an analysis of the days-sales-outstanding, inventory turns by complex systems and the deals won on complex system sales. In addition, the use of customer data could also streamline the response to quoting requests over time, as the workflow connection points would be more easily planned for and defined. In short, customer data could become the basis of accelerating the quoting process if used as a means to create trust with customers and also unify Dell around customer centricity, not technology. The transition from being technology to customer driven often accelerates the more complex processes if customer data and their metrics become the critical goal first, not just internally derived...

Often the implementations of CRM systems to manage customer intelligence at this level are highly integrated into ERP, SCM and production planning systems to ensure businesses stay on track to the goals that matter most to customers over time. Often this type of CRM system is designed to be installed and operated entirely within the company.
Recommendations for Executive Committee

As the lack of customer insight into the more complex quoting processes Dell is increasingly relying on for generating system sales is slowing down response times, a CRM system that can deliver insight and intelligence, in addition to analytics is critical. The use of customer-based data management systems that can track quoting performance to customers' original quotes for storage networks, virtualization-based servers and private Cloud systems, which are together the most complex solutions Dell provides, is critical for long-term profitability. There is also the need for creating a more streamlined series of processes in the need assessment, design validation, automated drawing generation, product configuration and document generation processes.

The recommendation to the executive committee is to invest in a CRM system that can bring both customer-based relationship data in addition to quoting intelligence and history into the workflows shown in Figure 3. The need for creating a single customer data record is also essential for the performance gains in complex quoting from 21 days to 1-hour be attained.

Figure 3:

Comparing the Quote-to-order Workflows for Complex Networks at Dell

Based on analysis of sources: (Dell Investor Relations, 2011 and SEC Filings) (Kapuscinski, Zhang, Carbonneau, Moore, Reeves, 2004) (Gunasekaran, Ngai, 2009) (Bilek, 2010)

References

Bilek, G.. (2010). The Value of Information Sharing in a Build-to-Order Supply Chain. The Business Review, Cambridge, 15(1), 131-136.

Ernst, H., Hoyer, W., Krafft, M., & Krieger, K. (2011). Customer relationship management and company performance -- the mediating role of new product performance. Academy of Marketing Science. Journal, 39(2), 290-306.

Finnegan, D., & Currie, W.. (2010). A multi-layered approach to CRM implementation: An integration perspective. European Management Journal, 28(2), 153.

Gunasekaran, a., & Ngai, E.. (2009). Modeling and analysis of build-to-order supply chains. European Journal of Operational Research, 195(2), 319.

Roman Kapuscinski, Rachel Q. Zhang, Paul Carbonneau, Robert Moore, and Bill Reeves. 2004. Inventory Decisions in Dell's Supply Chain. Interfaces 34, no. 3, (May 1): 191-205.

Ling-yee, L.. (2011). Marketing metrics' usage: Its predictors and implications for customer relationship management. Industrial Marketing Management, 40(1), 139.

Liu, C., Guo, Y., & Lee, C.. (2011). The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management, 31(1), 71.

Rapp, a., Trainor, K., & Agnihotri, R.. (2010). Performance implications of customer-linking capabilities: Examining the complementary role of customer orientation and CRM technology. Journal of Business Research, 63(11), 1229.

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