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Given these developments, we anticipate that over the next five years executives will increasingly focus on developing analytical platforms and skills within the supply-chain organization. We see the advent of the Internet of Things ("IoT") with embedded sensing technologies dramatically increasing and improving access to supply chain and operational data. This increased data availability will, in turn, foster more insightful cross-functional analytics.
One approach to cross-functional data analytics is combining finance, risk and supply-chain data. This process starts by embedding computing devices that stream sensor data — vis-à-vis the Internet of Things — throughout the manufacturing process and the broader supply chain, thus capturing a constant stream of real-time process performance data. By then feeding the data into an analytical data hub, where they are consolidated, standardized and combined with similar data from other enterprise functions (e.g., Finance, Sales), companies can create powerful, proprietary, enterprise-wide data sources to develop descriptive, predictive and even prescriptive analytics for operational optimization. Companies can augment classic risk measures such as standard deviation (i.e., lead-time, forecast error, etc.) and mean time between failure (i.e., production downtime, transportation capacity, etc.) and link real-time operational outcomes with actual financial outcomes.
These tailored, cross-functional analytics may, in turn, inform decision-making. For example, a company that better understands the real-time cost of downtime for a given manufacturing process can optimize the procedure for bringing the process back online. (“Why spend $100,000 for an emergency replacement part today if the part can be replaced in three days for $70,000 and the downtime has a maximum value of $80,000 for the next three days?”).
These analytics could also help prove the business case for investing in supply-chain efficiency. (“It makes economic sense to invest $3m to optimize in-region order fulfillment because we foresee a $4.5m quarterly profits increase — and we can monitor and track these profits.”) In addition, these analytics could improve risk management. (“We forecast a 3.5-percent chance of supply disruption during the coming quarter which will result in missed profits of $23m. Our maximum risk appetite for missed profits is $10m in a given quarter and so we should purchase $5m of safety stock inventory.”)
Supply-chain executives sense, intuitively, that they can, and should, play a bigger role in business decisions — both operational and strategic. One opportunity to expand their roles and amplify their voices is to develop cross-functional data analytics within the organization. Organizations that move promptly to develop these analytics — particularly those coupling finance and risk analyses with operational data — will develop operational advantages to their peers which lead to outperformance as soon as 2020 and certainly during the coming decade.
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