The Elusive Measure of Future Readiness
Most metrics measuring the business efficacy of an operating model focus on efficiency as the primary parameter of operating model transformation. However, there is a growing recognition that the operating model has a profound impact on business process outcomes or ‘total cost of ownership’ in processes.
For example, if the only measures for the accounts payable process in an organization are on-time payments (controllership) and cost of processing, then those are the only outcomes that it will deliver. However, if we measured accounts payable as part of the overall purchase-to-pay cycle, lower cost of ownership would then be the key business outcome. The metrics would therefore include measuring discounts capture, dynamic discounting, vendor consolidation, and improving AP days — all of which have a far bigger impact on overall business.
So if there is an increased recognition of the need for reduction in total cost of ownership, why doesit not reflect in outcomes? As the adage goes: What gets measured gets done! Traditionally, processes have been measured in silos, so it's easy for most to measure the outcomes on efficiency and controls – however when it comes to going beyond – it challenges the accepted normal.
Similarly, given the rapidity of change in today's business environment, while planning any transformation — especially in the operating model — it is important we not only benchmark against the best-in-class of past performance but also the new best-in-class and the new normal. So how do we measure future readiness of an operating model?
While cost reduction, business process effectiveness, and controllership are important outcomes, a target operating model must be future ready. This paper examines three key levers that can help design a future ready target operating model for well-balanced outcomes.
The Levers to Better Business Alignment
Aligning an operating model with changing corporate priorities on a global scale and ensuring it delivers desired business outcomes across different markets is a huge challenge even for outperforming companies. Take the case of ERP implementations. Typically, by the time these implementations are completed, successfully negotiating various hurdles and stakeholders across different units, functions, and geographies, the CTO may find that business priorities have changed. The long implementation period combined with a tactical approach would often mean that there was no emphasis placed on how the ERP solutions would respond to changing corporate priorities.Similarly, current operating models trying to squeeze out as much efficiency gains as possible through shared services or outsourcing models, ignore the potential to go beyond arbitrage advantages and the setting up of centers of excellence. How would these shared services centers become sustainable five years hence when the arbitrage advantages would have tapered off and these centers start looking for more strategic or partnership roles with the parent company or with vendors but lack the framework or the capabilities to change?
Target Operating Models (TOM), therefore, need to have mechanisms that can allow for quick alignment with changed corporate priorities.
Global best practices show that outperforming companies achieve better business outcomes from their target operating model using three critical levers.
Figure 1: Identifying the Critical Levers
Actionable Intelligence: Making Business Sense of Relevant Data
Globally, organizations are using less than one percent of the data available to them. However, the larger question is whether they need to invest in using all of it? And the answer is NO. There is so much data available today from processes, automated tools, social media, blogs, and other digital channels, that organizations need to decide what data to use that provides ‘actionable’ intelligence. Therefore, choosing the right metrics that not only provide business and operational performance measures but are also aligned to the organization's core objectives and vision, and market drivers is the key.
While all organizations have some metrics and measurements already in place, often what is missing is the capability to continuously update these. They need to invest in capabilities to continuously evaluate new data streams and exploit them when relevant. They should also regularly review measurements that are redundant.
A Target Operating Model that specifically focuses on building ‘Actionable Intelligence Center of Excellence’ for the organization as a whole, and therefore invests in relevant capabilities as discussed above and also in capabilities to execute on imperatives indicated by such intelligence will be able to drive sustainable results in the future.
Figure 2: Defining Metrics & Measurement
Building Flexibility and Agility through the Operating Model
Global companies are compelled to review their operating models often amidst constant change in the business environment. Market forces are altering strategic priorities of businesses rapidly. This leads to changes in the operating model, not only in terms of metrics and measurements but also in the organizational structure, capability and technology investments, analytical investments, among others. However, can companies set direction and roadmap for a future operating model instead of frequently altering their operating model in response to changing global business conditions?
A target operating model describes the end state operating model and is a roadmap to where the company wants to be and how it can be best organized to be more efficient and effective to deliver the organization's strategy across all business processes.
Global businesses often stumble when it comes to designing common global processes across all markets while simultaneously allowing locally relevant processes. The struggle between standardization versus customization impacts flexibility as it results in varying operating models, especially in large global companies with operations in several countries.
What is needed is to look at processes from many different aspects at granular activity level (and not necessarily the process as a whole). These different aspects may be business outcomes, skill sets needed to deliver, business impact activities deliver (commoditize or specialize) – all analyzed at activity level and not necessarily as an overall process.
Let us take the instance of analytics processes. Often analytical organizations act in silos with different teams making ad-hoc project-based efforts with little standardization to enable enterprise scale solution (e.g. financial planning and analytics, salesforce analytics, marketing analytics, and spend analytics). Some outperforming companies, however, have successfully built the Center of Excellence model for enterprise analytics function. This is built on strongly defined layers of expertise at various levels that interact to create a systematic response to a business need (see figure 3).
Figure 3: CoE Model for Enterprise Analytics
In the above figure, the data layer has access to both internal and external streams of data, including tools and software licenses to quickly analyze data streams and sources for relevant data inputs to any business need
The research layer has skill sets and tools to manipulate data and reorganize, interpret and report data to generate meaningful insights aligned to business need
The analytics layer has the statisticians, data scientists, subject matter experts and tools to build data models, forecasts etc.
The productization layer can convert any successful proof of concept from the analytical layer into a standardized and repeatable process
Finally, the visualisation layer focuses on skill sets and tools to be able to present data in a manner that is understood by most users in a self-explanatory mode
Such a design allows significantly higher analytical capability at the enterprise level with negligible investments. However, more importantly, it offers a great degree of agility to respond to new analytics requirements and skill sets.
This ability to visualize processes markedly different from the traditional way, makes it possible to design operating models that have higher agility to adapt to future needs.
Optimal is the operative word here! Apart from a clear definition of business vision, objectives and priorities, there should be a clearly articulated process for ensuring that the existing technology investment is fully exploited. The business case for new investments should be evaluated only on incremental capability.
Given the furious pace of change in technology, the possibility of new technology investments turning redundant is extremely high.
Therefore, it is important that investments achieve the fundamental balance of not lagging behind industry capabilities and at the same time, not being too early or late so that they end up redundant. It's best to minimize investments, especially long-term capital investments to maintain the flexibility of switching to new standards when needed.
While the aforementioned levers help us define and move closer to the target operating model, the core foundation to ensure sustainability of these models will include:
Change Enabling Capabilities: This should not be confused with change management which typically has a project management (and therefore one-time activity) role. Change enabling capabilities involve putting together a team, creating an operating model design and building an overall organization culture, which enable change on a day-to-day basis
Governance Models: Be it commercial models like outcome-based pricing to align objectives of vendors or pay-by-drink models to ensure flexibility, a comprehensive governance model aligned to overall objective of balancing the outcomes of the target operating model is a must
Right Skill Sets: Often access to skill sets or the lack of it limits the operationalization of the target operating model. Therefore, it is crucial to design the strategy upfront for the required skill sets and their sourcing (global sourcing or third party vendors)