Digital technology in the supply chain, such as artificial intelligence and machine learning, supports end-to-end decision-making, and visibility into both network-wide supply-demand information and operational level response. These technologies run on data, which makes timely and accurate data collection, validation, management and analysis fundamental to advancing digital optimization strategies. That’s especially the case in today’s highly volatile market, where it has become increasingly challenging to accurately predict capacity and demand.
Modern shippers are turning to AI and machine learning to add predictability and optimize supply chain operations. But despite the most advanced innovation, AI can only ever be as smart as its underlying data. Data quality and integrity matter.
Many organizations still operate using data that is stored in silos, incompatibly formatted, difficult to access and hard to comprehensively analyze. In fact, industry experts estimate that 35%-40% of all data in supply chain systems is faulty. To overcome this issue, data collection across business units and systems must improve.
Operationalizing AI starts with making sure your data is solid. This means embracing data governance, to ensure the accuracy of the data being used and avoid a “garbage-in, garbage-out” result.
Data governance must focus on creating and processing data that can be turned into an operational asset. This requires paying close attention to all aspects of data handling, including:
Data validation. With thousands of automated transactions daily, data must be correct to realize the benefits of AI. Cross-checking sources is vital to ensuring that data from all touchpoints align.
Data maintenance. Build guards and checks into your execution platform to ensure ongoing data integrity and oversight.
Data ingestion. Make sure you have a streamlined approach to how data is ingested into models in the first place.
Data streaming. As part of data ingestion, make sure you’re able to stream data in real time and that you have an infrastructure to support it.
Data compliance. Have a monitoring system in place that tracks whether results are skewed by data bias, and validates that data is derived from trustworthy sources.
Remember that data governance isn’t a “set-it-and-forget-it” activity. You must continually evaluate the integrity of your data collection and management programs against internal requirements and external developments.
Once you have clean data, its value to your organization increases exponentially. It can provide visibility to business stakeholders, support demand forecasting, and automate decision-making by evaluating the best solution in complex environments. It does all this by following a four-part process:
Descriptive overview of the current state,
Exploratory analysis of what happened and why,
Predictive outlook and model, and
Prescriptive solution.
By analyzing complete and accurate historical data and information, organizations can successfully use AI models to take a forward-thinking approach to managing their business. For example, in transportation management, a predictive outlook could be created around market pricing or capacity to arrive at accurate freight cost and demand forecasts for next-day conditions, or predict delayed load pickup or delivery. A prescriptive solution could then draw on traditional operations research and machine learning to optimize freight matching or routing scenario for market conditions.
Outlook:
AI and other emerging technologies are rapidly transforming the future of supply chain. But only those who can capitalize on AI will come out ahead. This means reexamining the way you collect, manage and analyze data gleaned from operations, customers and suppliers. Creating a comprehensive plan to clean and structure existing data, and properly handle future data, will position your enterprise to thrive now as you prepare to embrace the next generation of technological advancement.
