Artificial intelligence (AI) is defined as a discipline that applies advanced analysis and log-based techniques to interpret events, support and automate decisions, and take action. AI has evolved significantly over the years, and today it can be integrated into nearly every aspect of business and technology. Particularly in supply chain planning, AI can enable more accurate forecasting, increased efficiency in production, improved insights, and more. For supply chain planners looking for the key to flawless planning, AI is the answer.
Why Integrate AI into Your Supply Chain?
In addition to improving supply chain planning as a whole, AI is also instrumental in addressing common supply chain challenges. One of the most significant challenges that a supply chain planner faces is planning for internal organizational processes yet overlooking external factors. In reality, the external factors that impact your supply chain are the factors that require devoted planning.
Unpredicted interruptions, market changes, or changes of resources all need to be planned for – in addition to internal challenges such as machine breakdowns, lack of staff resources and experts, and more. While these can be resolved after the fact through rescheduling and maintenance, that can be time-consuming and expensive. If your supply chain planner cannot plan for both internal and external complications, your supply chain may face shortages, disruptions, or complete halts.
Even beyond the fundamental challenges that supply chain planners face on a daily basis, they also must facilitate specific expectations. Within an organization, supply chain planners are expected to provide accurate forecasting options of products and transports, patterns or orders and transports, improve data quality, and conduct statistical analysis in addition to performing data detection and migration strategies.
While there is software to aid supply chain planners in these tasks, it can still be quite tedious and time-consuming. If done incorrectly or too slowly, the data produced could be rendered useless, leaving the supply chain without proper direction while wasting time and money. For this reason, supply chain planners must integrate AI into their planning processes. With AI, supply chain planners can efficiently plan for internal and external challenges while fulfilling all other expected tasks.
Trends in AI
Smarter, More Responsible, and Scalable AI
Each year, businesses discover more ways to integrate AI into their organization, enabling them to support more complex processes with ease. To support these complexities, companies are adopting smarter, more responsible, and scalable AI platforms.
- Smarter: AI with better learning algorithms, making AI more resilient and creative but less data-hungry.
- Responsible: Interpretable systems that are ethical, reproducible, transparent, and secure.
- Scalable: Platforms with shorter time-to-value that improve AI robustness, accessibility, productivity, deployment, and operations.
These platforms allow businesses to scale and operationalize machine learning (ML) while supporting complex operations and protecting private data. While it may seem as though all AI platforms possess these capabilities, they are growing even more advanced. This is due to companies' growing need to process significant amounts of both new and historical data. As more data is collected for analysis, companies need AI platforms that can compute this data quickly and efficiently while also analyzing it for patterns and insights.
Current data-intensive approaches are limited by the volume of data they can compute, in addition to being inflexible and containing bias. To overcome these challenges and optimize supply chain planning, businesses need an AI platform that can accelerate and operationalize AI to drive decisions.
Composable Data and Analytics
While AI is instrumental in collecting data and computing analytics, it is essential that the insights made by AI platforms can be connected to actions. To conduct this, supply chain planners are seeking AI platforms with composable data and analytics capabilities.
Composable data and analytics utilize container architecture and data fabric to create flexible, modular, reusable, and consumer-friendly data and analytics capabilities from preexisting assets. As a result, organizations can compose components from three or more analytics solutions to build decision-oriented applications, to then act based on those decisions. This trend essentially leverages the work done by AI applications along with the data and analytics teams to improve agility and accelerate intelligent decision-making and actions.
Engineering Decision Intelligence
For AI-based decision-making to be effective, the decisions must be based upon accurate analytics. To achieve this, engineering decision intelligence is required. As decision-making has grown more complex over the years, there is more uncertainty surrounding market fluctuations and the durability of a decision as a result.
Fortunately, with AI-enabled decision intelligence, supply chain planners can obtain more accurate decisions – and this applies to more than just individual decisions. In addition to individual decisions, decision intelligence can also apply to sequences of decisions, grouping them into businesses processes and networks of decision making.
In adopting this trend, supply chain managers can be more confident in the decision-making process, as their decisions are backed by accurate and intelligent analytics. Planners can create new decision-making habits by training decision-making platforms to practice critical thinking, trade-off analysis, recognize bias, and listen to opposing views. As a result, supply chain planners have more accurate decisions to base their actions on, optimizing the entire supply chain.
Meet Supply Chain Objectives Through AI
In adopting these AI trends, supply chain managers can meet objectives more accurately and efficiently. Some primary objectives include detecting anomalies in the supply chain, producing accurate forecasts, conducting machine learning analysis, and correlating data topics. As a result, supply chain managers can combat common challenges while boosting existing intelligent supply chain solutions, optimizing supply chain performance.
Even beyond these benefits, integrating AI into your supply chain can provide companies with enhanced safety, reduced operations costs, on-time deliveries, end-to-end visibility, and intelligent decision-making. With these capabilities in their toolbox, any supply chain manager will be able to make accurate and efficient plans, resulting in increased success and productivity in their supply chain.
AI is no longer a technological leg-up in business but is now a necessary tool to staying competitive and operating efficiently. With AI integrated into your company, your supply chain will operate with an unmatched level of intelligence and speed.
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