The supply chain world is being shaken by new technologies. The evolution of Artificial Intelligence (AI), advanced data analytics, software development and many other tools, or even the demand for transparency and sustainability are forcing a digital transformation of organizations, which must adapt to these requirements if they want to remain competitive. To do so, they will also have to keep abreast of current trends, be agile in internal transformations and rely on an appropriate organizational culture.
As a result of this transformation, new key forces of change emerge as top priorities. Today, actions such as reducing carbon emissions are mandatory for any organization, but market trends have evolved, pushing demands one step further, resulting in a system where an inordinate amount of information must be handled, thus making data management a daily challenge. Among this nebulous cloud of data, transparency and traceability become key attributes of data sets and business processes, as they can not only prevent misunderstandings and problems, but also prevent risks, anomalies and streamline operations and activities. Furthermore, it is necessary to choose software solutions wisely, so as to reduce development and implementation times, allowing for agility and speed in market introduction.
All in all, the increasing pace of technology has the potential to profoundly alter logistics processes and business models, driven by the ultimate goal of adding value, increasing efficiency and reducing costs.
“This wealth of data has given rise to greater silos of data within the organization which in turn has led to disconnected data sets. Duplication and misinterpretation will become increasingly problematic, too.”
The main inputs and contributions are:
- A new paradigm is emerging in supply chain management. Organizations must optimise their operations, fasten its problem-solving procedures, reduce errors and constantly adapt to modern demands.
- Generative AI has a massive potential for revolutionizing supply chain management. It is capable of processing large sets of data and analysing almost infinitely complex sets of variables, while being actively auto-learning and refining its analysis. Organizations should avoid single-point AI implementations, as it might dissipate effectiveness of the implementation. For this reason, they should perform an internal data scan, to check its quality and adaptability to AI’s.
- AI enabled sales and operational planning and integrated business planning (S&OP, IBP) applications eliminate the gap between planning and execution, whilst eliminating manual work, relieving job stress and reducing human intervention. Then, the knowledge and skills in analytical modelling and relationship management among planners must increase in order to maximize collaboration.
- Data availability, quality, cadence and consistency are critical considerations for the performance of the supply chain. Managing this data is a great challenge for companies to be able to make efficient, informed decisions to optimise their operations. Therefore, companies should be clear that data management is a continuous job, rather than a one-off task, in which data availability, quality, reliability, cadence, and consistency have to be taken care of permanently.
- The barrier between layered tiers of the supply chain has negative implications on its performance. Breaking it can become elemental to identify and prevent further risks. An example of a possible action to prevent these problems is to create cross-functional teams to provide a fuller picture of key use cases and the scope of visibility.
- A supply chain is a dynamic and complex process. Implementing software changes in this environment tends to be problematic. To tackle this, low-code and no-code platforms facilitate the automation of many supply chain tasks, and there are pre-packaged integrations that link previously separated systems.
- The addition of Scope 3 emissions to the focus of businesses has increased the complexity to assess their environmental care. It will be important to identify and prioritise certain supply chain categories, trying to educate suppliers, while looking for technological solutions that can help in reducing emissions.
- Smart logistics and automated transport will also be accelerated with the continued ramp-up of AI, IoT, data analytics and cloud across many use cases – improving traditional route optimization and applying machine learning, predictive and sensing capabilities to make material improvements to network efficiency, customer experience, risk reduction and sustainability targets.
“Through 2024, 50% of supply chain organizations will invest in applications that support artificial intelligence and advanced analytics capabilities.”
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