Closed-Loop Spend Management
Many companies have substantially intensified their sourcing efforts during the past few years, with sometimes remarkable outcomes. However, the sustained effect on results has often fallen short of expectations. Closed loop spend management offers an all-encompassing approach to address “value destroyers” in a way that is geared to the specific sourcing situation. The challenge usually lies in the fact that procurement only has real influence on a very small segment of the value-creation process. In the case of direct materials, for instance, procurement often becomes involved only after specifications have already been defined by the technical division—it has some leeway in selecting the supplier and concluding the contract but little influence on the process. Furthermore, there is often a lack of transparency into how demand planning is done, when the order is actually placed, when the goods are received, and when invoices are paid.
The aim of closed loop spend management is to optimize expenses throughout the value-creation process and generate sustainable value for the company. In a targeted analysis for specific product groups, potential value destroyers (imperfect spend transparency, demand management, user and supplier compliance, payment management, and process costs) are identified and concrete measures are initiated. Successful companies have established closed loop spend management as an end-to-end process within the responsibility of procurement. This means procurement is given not only the required information but also the power to implement necessary measures in conjunction with product users, internal users of third-party services, and those with functional responsibility.
However, there are several challenges when establishing and implementing a closed loop spend management solution: incongruent taxonomies as well as incomplete and inaccurate data from desperate bad data sources. To overcome these challenges, closed loop spend management should be forward looking and anticipatory about the coming age of big data, machine language, pattern recognition, and natural language processing. Because data integration is a continuous process, machine learning techniques should be leveraged for developing ontology-driven (textual attributes such as product descriptions) classification algorithms and mathematical model-driven clustering methods with the intent of reducing integration costs and accelerating deployment of these new capabilities over time.
As companies continue to invest in supply chain and procurement-related systems, big data and machine learning-enabled closed loop spend management solutions provide the most accurate, unified, and real-time visibility into spending across the organization, enabling procurement to conduct performance- instinctive analysis and answer crucial questions. The big-impact areas will be faster and provide more accurate visibility into “dirty tail spend areas” (such as facility or line-level MRO and fragmented temporary labor spending), procurement merger synergies, transportation contract billing audits, and other previously painful data integrity challenges.