Findings of our Recent Study on Supply Market Intelligence
Organizations are facing increased uncertainty in economic markets, and are increasingly aware of the need to closely monitor market conditions and respond to these changes through improved supply chain strategies. As more organizations seek to build sourcing strategies to identify cost savings opportunities, they are recognizing that there are major shortfalls in market intelligence and cost modeling capabilities that form the basis for effective strategies and negotiation. Further, the need for integration of market intelligence into operational decisions, including budgets, profit objectives, market pricing, technology insights, global expansion, and other components of competitive strategy is generally not well executed. The result is misalignment between demand and supply planning, and major gaps in operational performance and risk mitigation.
I recently worked on a study focused on identifying key actions that organizations are taking to remedy this situation. The research is based on interviews with subject matter experts in a number of industries who have deployed or are in the process of deploying centers of excellence for supply market intelligence, as well as surveys with 89 global supply chain executives through IACCM. The first group represented a core group of companies who have developed more mature supply market intelligence organizations, while the survey results provide insights into what a broader set of organizations, many of whom are lagging, are doing by comparison. We discovered in the course of this research several key important insights.
First, organizations with successful supply market intelligence (SMI) programs excel not so much in the process of data collection and analysis, but develop a team of internal MI analysts who are proficient in defining knowledge requirements, as well as disseminating information to ensure that it is effectively applied in key business decisions. Current research suggests that successful organizations are creating Centers of Excellence for MI, with analysts co-located in multiple business units globally, but coordinating through centralized processes. MI analysts are generally responsible for $1.5 to $2B of organizational spend per FTE. In most firms, however, many companies are not developing dedicated teams of MI analysts, but are relying on category managers to perform this function.
Second, it is increasingly being recognized that category managers are often not well equipped to build MI analyses, due to the increasing demand for other activities. This is important, as it justifies the need for a dedicated MI function. Further, the return on investment on these individuals dictates that it does not make sense for them to be conducting routine market analyses. Over time, however, executives interviewed believe that these individuals should become full-fledged experts in their category. Best-in-class companies are all focused on having category leaders increasingly rely on an MI Center of Excellence for coordination of data collection, analysis, synthesis, and insight as a core foundational component of sourcing strategy. Internal MI analysts are best equipped if they come from an engineering, financial, supply chain, or cost accounting background. Economics and financial analyst experience will become increasingly important for MI analysis.
Third, there is an increasing trend towards outsourcing of MI data collection, synthesis, analysis and reporting. Key areas where third party providers are collecting and synthesizing data include global market analysis, benchmarking, inflation/deflationary pricing, value-chain mapping, global cost-reduction sourcing opportunities, and emerging market sales and channels. Implicit in this trend is the recognition that best in class companies recognize that MI is fundamentally about the application of individual and cognitive methods to weigh data and test hypotheses. As such, the primary role of an MI function is not to collect data and process it; rather, the focus of an MI team should target engagement and understanding of internal client requirements, context, and application of the information to business decisions. Proper understanding of information requirements is fundamental to a successful MI function. Analysts need to truly understand the right question before embarking on data analysis, to ensure that the appropriate hypotheses and data are collected by external Mi providers. Best-in-class organizations also rely on MI teams to process external MI reports, and explain the implications and insights through knowledge transfer mechanisms, to ensure that the intelligence is translated into meaningful insights that are useful and practical to the stakeholder.
Fourth, best-in-class companies recognize the importance of establishing expectations to clients about what can and cannot be delivered through the MI Center of Excellence. The breadth and depth of data will determine the lead time required to create the report. Clear guidelines must be communicated and acknowledged by the client, to understand the limitations regarding what can be produced within a given time horizon, as well as the appropriate types of data required for business decisions. This is an important educational process that is part of any supply chain transformation process.
Fifth, the research points to the importance of conducting performance evaluations of MI reports, and tying these back into port-mortems and lessons learned that can be filtered back into the organization. Many companies are seeking to tie MI investments to meaningful measures of cost savings. In our opinion, this is a difficult approach to apply in a systematic and standard way. While anecdotal data can point to cost savings achieved through effective MI applied to specific projects, these are highly contextual and specific in nature. Instead, best-in-class companies are relying on a systematic evaluation of client feedback, with a long-term and strategic understanding of the importance of MI’s contribution to key enterprise-wide procurement metrics and value. This requires leadership support and alignment of procurement strategy to other core organizational strategies, and an ability to link intelligence to these outcomes.
Sixth, our research suggests that most organizations are for the most part not effectively linking MI projects and insights into operational decision making. For example, in mature organizations, cost models need be aligned with savings projects and profit targets for corporate and business unit level budgeting processes. Several case studies discussed in this report provides examples of how successful organizations are achieving this. For the most part, this requires multiple communication channels, often through simple “lunch and learn” discussions that provide opportunities for face-to-face dialogue, discussion, Q&A, and debate.
Finally, most organizations do not have a good process for a meaningful on-going monitoring of supply risk. There are generally a good number of companies who are monitoring financial health of suppliers, but other market-level issues are often not captured. This is one of the reasons why organizations are still susceptible to intelligence failure, due to the inherent nature of surprise associated with supply market incidents. The nature of surprise is not attributable to omission or commission of information, but rather the need for contravening cognitive processes. As such, there is a need for development of talent that has the ability to develop missing hypotheses and mental models that can begin to predict the potential behavior of market participants in a specific context.