In recent years, companies have been investing in cutting-edge technologies to enhance analytics across their entire enterprise, with the aim of unlocking the estimated $9.5 trillion in business value, as suggested by the McKinsey Global Institute, that could be achieved with comprehensive integration of advanced analytics.
Despite these investments, supply chain planners still find themselves devoting a significant amount of time to data collection and analysis, using processes that haven’t fundamentally evolved. The increasing volume of daily decisions and the complexity of data and variables have outpaced the capacity of even the most skilled planners.
This growing gap between planning and execution is hindering leaders from addressing strategic issues effectively. Furthermore, data quality remains a concern, with only 3% of a company’s data meeting the minimum standards, according to a study by HBR.
These issues have led to a concealed yet substantial problem: crucial decision-making opportunities are being missed across various areas of the supply chain, including planning, procurement, logistics, finance, and marketing.
The promising news is that predictive analytics and enterprise AI offer a solution that can improve decision-making within supply chains, particularly at critical junctures.
A Quick Overview: Enterprise AI vs. Other Technologies
Before delving deeper into the topic, it’s essential to differentiate between the types of AI that hold the most potential for addressing these challenges.
Much attention has been focused on generative AI, like ChatGPT, which employs unsupervised and semi-supervised algorithms to create new content using pre-existing text, audio, video, images, or code. Generative AI holds promise in specific business applications, especially in marketing and customer engagement.
On the other hand, enterprise AI combines artificial intelligence, machine learning, and data science into software solutions designed to tackle business requirements. These technologies play a vital role in decision intelligence, a growing field that facilitates the digitization, augmentation, and automation of business decision-making. Traditional decision-making processes are often fragmented and disjointed, necessitating the use of spreadsheets and other tools for data analysis and decision-making. Decision intelligence streamlines this process, enabling quicker and more accurate decision-making on a broader scale and in real-time.
Decision intelligence platforms are specifically engineered to provide additional functionality and frameworks for operationalizing AI in a business decision-making context. These platforms coordinate the data, intelligence, automation, and user engagement capabilities needed to assist companies in making faster and more precise decisions.
Data-Driven Decisions on an Enterprise Scale
These technologies empower supply chains to address decisions that are currently beyond their reach due to constraints in time, visibility, or data analysis capabilities across various business functions. These decisions have the potential to help companies reduce costs by optimizing inventory, adapt to shifting demand patterns, or react more efficiently to unexpected challenges.
For example, manufacturers can now adjust in real-time to sudden surges in demand for specific products in certain regions. They can also fine-tune marketing strategies to better gauge and meet demand. Furthermore, they can respond promptly and effectively when faced with constraints in the availability of raw materials. Given the increasing importance of environmental concerns and regulations in supply-chain decisions, companies can also strike a balance between carbon emissions and service levels in logistics and shipping.
These are just a few examples of the types of decisions that enterprise AI is addressing on a daily basis. By considering data from the entire value chain, rather than just specific segments of it, an enterprise AI system can apply predictive analytics and machine learning models to assess various scenarios and select the most optimal course of action.
One multinational company I have collaborated with implemented a decision intelligence platform over five years ago to tackle these challenges. Today, the team routinely receives millions of AI-generated recommendations related to tasks such as inventory rebalancing, demand forecasting, production planning, and procurement decisions. The cumulative impact on revenue and cost savings from these recommendations amounts to tens of millions of dollars.
Some of the most complex decisions that supply chains face today involve the intersection of various functions, such as supply chain with marketing and trade promotions, procurement with manufacturing, and sustainability with logistics. Decision intelligence technology empowers teams to transcend data and process silos, utilizing data from all their point solutions and data sources. The result is enterprise-wide visibility, facilitating a true digital transformation of decision-making and its associated benefits.
Today, enterprise AI is paving the way for a data-driven, collaborative environment that McKinsey & Co. predicts will be the norm by 2025. In this environment, machines synchronize data across the enterprise, and AI-generated recommendations and predictive analytics become integral to the daily work of nearly every employee. Not only does data quality improve, but decision-making challenges that previously took days or weeks to resolve can now be addressed in mere minutes or hours.
Getting Started on Your Journey
Achieving the level of digital transformation described here might have seemed impossible just a few years ago. However, contemporary AI solutions can be integrated into existing systems without the need for an extensive overhaul.
By adopting a data model designed for decision-making, busy planners can offload data-wrangling tasks to machines capable of faster and more accurate analysis. These systems can then offer data-driven recommendations while learning from the outcomes and context of each decision made.
This capacity for learning is instrumental in reaping the full benefits of advanced analytics across the end-to-end value chain. AI empowers the automation of routine or time-sensitive decisions and the creation of a digital record of these decisions, their contexts, and their outcomes.
From selecting a new supplier based on OTIF (On-Time In-Full) performance thresholds to adjusting manufacturing capacity utilization according to demand signals or rerouting inventory to overcome delays, the AI systems of today are relieving planners of the burden of extensive data analysis. Enterprise AI also uncovers fresh insights and opportunities to mitigate risks, boost revenue, and operate more efficiently in an increasingly digital landscape.
Your go-to for supply chain report news updates: The Supply Chain Report. For international trade tools, see ADAMftd.com.
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