Improve the Order-to-Cash Process with Master Data Management

With so much attention given over the past two years to the supply chain, it can be easy to overlook the broader organizational processes in which it is involved. One is the order-to-cash process, which spans end-to-end, from receiving orders from customers to paying for goods or services. With multiple functions involved in this process, it can be difficult for organizations to ensure efficiency and effectiveness.

In a 2021 APQC survey of global and cross-industry professionals, 76% of respondents rated their order-to-cash process as somewhat or very inefficient. This leaves significant room for organizations to improve this process which impacts a large part of the business.

Many of these organizations are looking to data as a starting point to improve their order-to-cash process. In the APQC survey, 56% of respondents indicated that improving the quality and management of master data is one of the next improvements they plan to make to the order-to-order process. payment. This is a logical next step given that master data management aims to organize and synchronize customer, supplier, and product data across the enterprise.

In researching master data management best practices, APQC found that centralized data management, robust processes to ensure data quality, and assigning the right business unit to manage data led to better results for the order-to-cash process.


Related Infographic: Master Data Management Improvement Factors


Data Governance Foundation

Clear data governance is an essential part of an effective master data management program. According to the APQC survey, a majority of organizations say their master data is managed by an enterprise-wide function (Figure 1). However, 36% indicate that the management of their organization is decentralized to some degree.


Figure 1: How master data is managed in the organization

Source: APQC


Decentralized master data management can undermine an organization’s efforts to maintain clean, high-quality data. While it may seem logical to manage data by geography or function to meet the needs of these areas, this introduces the possibility of varying management practices across the enterprise. This in turn reduces the likelihood that master data management will help the organization improve its end-to-end order-to-cash process.

Similarly, when asked which function is responsible for master data management, more than 60% of organizations say that an enterprise-wide master data function is responsible (Figure 2) . Yet many organizations have multiple functions responsible for master data management, with just over half also indicating that IT has responsibility.

These survey results should cause organizations to caution about the systems they create to take ownership of master data management. Order-to-cash relies on high-quality data that is consistent every step of the way. The more functions responsible for master data, the greater the chance of data irregularities affecting the order-to-cash process.


Figure 2: Function in the organization that holds MDM responsibility

Source: APQC


APQC research on leading organizations reveals that those whose master data management is owned by an enterprise-wide function (such as a chief data officer) perform better than organizations that do not. don’t have any. They have fewer days of backordered sales and a higher percentage of orders delivered on time and in full. This is likely due to the consistency and visibility provided by centralized enterprise-level governance.

Data quality required

Governance and ownership of master data management are just two factors in ensuring consistent, high-quality master data is available across the organization. Organizations must also put processes in place to ensure that data in progress is synchronized and can flow seamlessly between systems within the business.


Figure 3: Consistency of master data from sales to order to invoicing

Source: APQC


As shown in Figure 3, nearly half of the organizations surveyed by APQC say their customer data processes meet these criteria. However, 41% have problems with matching records despite synchronizing customer data.

For these organizations, data flow challenges likely stem from a lack of consistent and effective data curation and maintenance. It is perhaps telling that there is no standard way among respondents to define data quality. When asked to identify the data quality requirements they set for error-free orders, more than half of APQC survey respondents indicated timeliness, validity, completeness and consistency. Just over 40% of respondents also indicated integrity.

The fact that no single data requirement was clearly dominant among organizations shows uncertainty among organizations as to how to determine priority requirements for their data. Half of the organizations surveyed by APQC report issues with their data that is not consistent, effectively stored or maintained. As a starting point, organizations can use past performance data to select three priority requirements. They can then closely monitor how their data meets these requirements to ensure a flow of data across the enterprise.

The key to a consistent and transparent master data flow is to consistently apply strong processes to ensure master data accuracy. Data from APQC’s Open Standards Benchmarking research shows that nearly every organization has a process in place to ensure the accuracy of their vendor master data. This includes assigning ownership for each piece of data and automating processes to help identify potential errors.

Of organizations whose customer data is synced and flows seamlessly from sales to orders to billing systems, nearly 75% say they have enterprise-wide master data ownership with an owner such as than a chief data officer. The implications of data inaccuracy go beyond the need for manual interventions to enable the transfer of data between systems. Inaccurate information can degrade forecast accuracy because the business does not have a single version of the truth for any of the activities in the order-to-cash process.

Improvement through master data

Organizations approach master data management in different ways. For a large healthcare organization studied by APQC, the solution was to create a supply chain data management program. As part of this program, it aligned its business processes and strategies with its data management strategy.

The organization has embraced centralized data ownership and developed documentation with guidance for master data management. It has also implemented data management and analytics tools, as well as a business process that includes related data governance, training, and quality standards. The organization was successful in its efforts. The result was that it streamlined data governance and reduced rework, business interruption, and supply chain cycle times.

Another example is IBM’s efforts to improve its quote-to-cash process using master data management. As part of this transformation, IBM appointed a chief data officer responsible for master data management, created a taxonomy to standardize product master data, adopted an enterprise-wide data repository and created teams of data architects. Through his efforts, IBM was able to make significant improvements in its quote-to-cash process, including cash flow, productivity, cycle time, return on investment, and seller satisfaction.

Ensuring the sustainability of the program

Many organizations have identified master data management as a key component to improving their order-to-cash processes. To succeed in master data management, organizations must have centralized data governance as well as clear guidelines and processes to ensure data accuracy.

The order-to-cash process is a compelling example of successful master data management. It relies on high-quality data that flows seamlessly throughout the process. To ensure that it has a sustainable data management program, an organization should not view master data management as a one-time improvement process, but rather as an effort to assess and continuous improvement through clear governance and focus. ddd


About the APQC
APQC helps organizations work smarter, faster and more confidently. It is the world’s leading authority on benchmarking, best practices, process and performance improvement, and knowledge management. APQC’s unique structure as a member-based not-for-profit organization makes it a market differentiator. APQC partners with over 500 member organizations worldwide across all industries. With more than 40 years of experience, APQC remains the world leader in organizational transformation. Visit us at apqc.org and find out how you can make best practices your practices.

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