More and more companies have been reporting higher productivity upon applying machine learning and internet of things in their supply chain management. The following article from Information Management explains how analytics in conjunction with artificial intelligence can equip organizations with a strong competitive advantage and stronger control over all aspect of their supply chains
Artificial intelligence is transforming the business world – advancing industries, modernizing operations and processes, and facilitating a more efficient, effective and well-equipped workforce.
Among the industries to benefit most from AI adoption, supply chain management is in the top three, per the findings of a recent McKinsey global survey. Seventy-six percent of the survey respondents at supply chain companies have already seen moderate to significant value since deploying AI initiatives.
The great promise of AI revolves around the initial premise that the technology gets smarter with access to increasing amounts of data. But its full potential encompasses more. The fact is that AI’s power grows even greater when it’s integrated with other technologies, such as analytics, according to PwC.
The convergence of AI with analytics makes new data-driven business models more potent and plays a critical role in identifying patterns in data to support everything from systems maintenance to supply and demand planning. According to the PwC survey, integrating AI and analytics systems to gain business insights from data was the top AI-related data priority for 2019.
Predictive, Prescriptive and Descriptive Analytics in the Supply Chain
Analytics is the collection, analysis, processing and presentation of data that drives business intelligence and smart decision-making. Broadly speaking, there are three major types of analytics used by businesses:
- Predictive analytics attempts to understand what might happen in the future through modeling and forecasting. Predictive analytics answers the question “What is going to happen?” Or more precisely, “What is likely to happen?”
- Prescriptive analytics identifies and advises on possible and likely outcomes before decisions are made. Prescriptive analytics answers the question “What should be done?” Or “What can we do to make XYZ happen?”
- Descriptive analytics explores what has happened by analyzing historical data or content and understanding past business outcomes. Descriptive analytics answers the question “What happened?” Or “What is happening?”
The Benefits of Predictive and Prescriptive Analytics for the Supply Chain
While descriptive analytics is a useful way to look at the past and optimize supply chain operations, it’s the two other relatively newer forms – predictive and prescriptive analytics – that promise to unlock real value in the field by helping analyze, model, predict and prepare for future changes in the supply chain. Ultimately, these insights may provide ongoing relief and improvement in areas such as reducing waste, streamlining processes, and minimizing costs.
Predictive and prescriptive analytics have multiple functions and benefits throughout the supply chain, including:
- Manage supply and demand planning – Supply and demand varies greatly based on seasonal trends, promotions, consumer needs and other factors. Predictive analytics could help supply chain managers better understand and anticipate future demands, while prescriptive analytics analyzes the likely impact on inventory levels based on specific demand-planning decisions.
- Predict estimated time of arrivals (ETAs) and facilitate proactive resolution of disruptions – Supply chain managers can use predictive analytics while monitoring shipment events to accurately predict ETAs and plan the movement of their goods accordingly. Meanwhile, the application of prescriptive analytics will enable systems to flag any exceptions and make informed predictions to improve supply chain performance, resilience and responsiveness.
- Match supply chain decisions to financial outcomes – Predictive and prescriptive analytics can provide supply chain professionals, and organizations as a whole, greater understanding of the costs associated with specific supply chain decisions by removing any disconnect between supply chain operations and the financial outcomes. Modeling inefficiencies in the supply chain and linking that back to costs allows a business to accurately understand how delays and quality issues directly impact revenue and profits.
- Implement continual improvement to streamline supply chain operations – With predictive analytics, supply chain operators can better understand and reduce existing bottlenecks, delays, and quality issues throughout the supply chain. Prescriptive analytics explores how specific changes will impact operations and outcomes, providing recommended improvements.
- Elevate risk management and mitigation planning – Weather-related, environmental, geopolitical or countless other factors can significantly impact the flow of goods across the supply chain. Predictive analytics gathers data from multiple areas to analyze external trends and suggest how these factors and potential risks are likely to increase costs, delay the flow of goods or cause additional issues. This insight can help improve risk management and mitigation planning in the supply chain.
The Challenges: Predictive and Prescriptive Analytics for the Supply Chain
While the benefits of applying an advanced understanding of data into supply chain operations may seem evident, there is a certain degree of expertise needed to implement analytics into the supply chain. Often, the obstacles faced have more to do with the learning curve, and less to do with the actual and perceived value of the exercise.
The main challenge of using predictive and prescriptive analytics is to understand and address the following questions and pain points:
- What to measure – What current and future data points will capture the right insight to provide business intelligence?
- How to measure and why – Is the data high-quality and is it captured in a consistent and reliable way? Can the data be linked to specific supply chain and business outcomes?
- How to model – Are the predictive and prescriptive models tested to ensure reliable outputs?
- What to act on – Are stakeholders able to act on the business intelligence delivered by predictive and prescriptive analytics?
More organizations recognize the value of advanced analytics for optimizing supply chain resources, especially when used in conjunction with AI and machine learning. Predictive and prescriptive analytics can hopefully equip organizations with a strong competitive advantage, along with heightened control over every aspect of their supply chains.
Without a crystal ball to predict disasters and unknown variables, organizations need strategies and tools to help avoid disruptions. The advanced capabilities of predictive and prescriptive analytics can serve as a guiding light for the supply chain, analyzing environmental factors and using data to inform decisions, predict, prepare, plan and advise.