The use of data-centric artificial intelligence (AI) and machine learning (ML) is a top priority for procurement managers. This was revealed in the spring of 2022 by the Gartner Digital Business Impact on Supply Chain Survey. AI can process and analyze large amounts of data and thus support human decisions in areas such as supply chain planning, demand forecasting, procurement, warehousing, and delivery. It also offers the potential to redesign business processes for automation. Thanks to AI-powered software, it is possible to centralize data from all touchpoints. This facilitates the use of cross-functional capacities, bundles data, increases agility and strengthens a company’s resilience, leading to better decisions and higher profitability.
However, despite the benefits that artificial intelligence brings, companies are still struggling to implement it, although the availability of the technology is usually not the real challenge. Much more energy is needed to convince management and strengthen existing data management practices to effectively use AI and ML technologies while identifying the right opportunities to use these solutions in planning processes.
AI and ML models require a large amount of relevant data for training and testing. Most companies produce, store, and forget data on a large scale. The challenge is usually not to collect new data, but to find, consolidate and analyze the existing data. Internal data from different areas such as sales, marketing, quality management and finance must be brought together and used to create a comprehensive data pool. Cross-departmental coordination helps with data collection. Where possible, this internal data should be linked to customer and supplier data to gain a better insight into demand patterns, stocks, sales figures and quantities. By sharing relevant data and key performance indicators, you can forge closer partnerships with your suppliers. This not only promotes better communication and mutual growth, but also leads to shorter delivery times, better product quality and more innovation.
Implementing AI and ML to build intelligent supply chains is a challenging but necessary task. In a time of constant change, supply chains need to be resilient and flexible. Added to this are the ever-increasing requirements for sustainability aspects. Companies that do not implement AI- and ML-supported planning processes will suffer a competitive disadvantage sooner rather than later, as they will not be able to react to market dynamics in an appropriately agile manner.
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