Competing in the dynamic and global business environment with the support of traditional supply chain execution systems is getting increasingly more difficult as customer expectations and pricing pressures continue to rise. That is why and where analytics is poised for growth. As per the report by SCM World, 64% of supply chain executives consider big data analytics a disruptive technology.
However, only 17% of the executives report having already implemented analytics in one or more supply chain functions (Source: Accenture). This implies that the necessary foundation for supply chain analytics is still at the early stages of development at many companies, and would take the time to evolve like other processes, technologies, and systems.
This made me think of how supply chain maturity is evolving differently in different organizations—from the semi-functional enterprise where different functions of the organization worked in silos and then to an integrated enterprise that enabled more integration and collaboration among different functions and finally, to the most optimized extended enterprise through enhanced collaboration among the multiple supply chain partners.
The same applies to the adoption of different levels of analytics by organizations, and their supply chains play an important factor in determining the type of analytics those organizations should invest in and expect RoI from. Analytics, over time, has evolved from being just descriptive to an advanced level of predictive and prescriptive states leading to optimized proactive decision making. As per the report by MarketsandMarkets, the global supply chain analytics market is expected to grow from USD 2.5 billion in 2014 to USD 4.8 billion by 2019, at 14.6% CAGR. Though the potential is huge, the widespread adoption of analytics have been curtailed by several barriers: Poor quality and unavailability of data, functional silos, unclear strategic fit, and rudimentary IT infrastructure to name a few. According to Gartner research, more than half of all analytics projects are failing—either they are never completed due to budget or schedule constraints or they fail to deliver the benefits that are optimistically agreed upon at their outset.
Supply chain maturity has a profound impact on some of the factors slowing analytics adoption such as data availability whether it is the data from within the organization or beyond the organization’s walls and the coordination of functions to break the “silos” within the enterprise. Analytics will not yield the expected benefits if not applied in line with the supply chain integration and maturity stage. For example, applying multi-echelon inventory optimization to the semi-functional or even to integrated enterprises in some cases, will not deliver the expected returns. Meanwhile, applying advanced demand forecasting capabilities without capturing demand signals available with multiple supply chain partners but not shared through collaboration will yield sub-optimal results.
Analytics has a wide range of application areas across the supply chain—from forecasting new products demand by identifying product cluster to which it belongs with known sales history and forecast models, sourcing of raw materials at the optimized total landed cost factoring supplier risk, smart manufacturing to distributing finished products through optimal vehicle routes and fleets, and finally, to providing excellent outbound customer service through predictive models.
Analytics is excellently augmenting supply chain capabilities and the successful application of analytics entails proper supply chain processes and necessary IT infrastructure in place. The idea is to start with the small and “right” analytical projects, which can demonstrate impressive results, build the confidence of the leadership, and help the organization stay focussed on implementing new projects till it leads to clear differentiation in the competitive positioning. Analytics will enable a closed loop supply chain capability and maturity building model where it would increase the supply chain capability, which in turn would lead to better decisions and requirement of better processes. This requiring more analytical capabilities would set in motion a continuous improvement cycle.