Wisutsunthornwas founded in 2009has been oriented towards Europeis a consulting & commercial brokerageis also a player & investor in the Fintech
AI and Supply Chain Management
In a world of big data and high customer expectations, the future of the supply chain rests in the predictive and prescriptive power of artificial intelligence (AI) and machine learning (ML).
This kind of technology application is not without its share of obstacles, risks, and challenges to overcome, but those organizations that do that properly will gain real-time, beyond-human-level insight, and set the standardfor supply chain management in the future.
A major challenge in the actionable analysis of a company’s vast supply chain data is the integration of potentially numerous legacy data sources into one holistic, unified picture from which decisions can be made. Fortunately, the data models employed by major enterprise resource planning players do not tend to shift significantly over time, making consistent connections with third-party cognitive analysis solutions easier to draw.
Modern cloud-first AI platforms tend to operate on their own layer above an organization’s existing data sources. In addition, they’re getting better and better at collecting, indexing, and harmonizing information without impacting the performance of the underlying systems from which they draw this information. Furthermore, today’s open source AI algorithms (sometimes referred to as “skills”) are robust enough to play a significant role in specific, calculative areas such as demand forecasting.
This kind of technology application is not without its share of obstacles, risks, and challenges to overcome, but those organizations that do that properly will gain real-time, beyond-human-level insight, and set the standardfor supply chain management in the future.
A major challenge in the actionable analysis of a company’s vast supply chain data is the integration of potentially numerous legacy data sources into one holistic, unified picture from which decisions can be made. Fortunately, the data models employed by major enterprise resource planning players do not tend to shift significantly over time, making consistent connections with third-party cognitive analysis solutions easier to draw.
Modern cloud-first AI platforms tend to operate on their own layer above an organization’s existing data sources. In addition, they’re getting better and better at collecting, indexing, and harmonizing information without impacting the performance of the underlying systems from which they draw this information. Furthermore, today’s open source AI algorithms (sometimes referred to as “skills”) are robust enough to play a significant role in specific, calculative areas such as demand forecasting.
These enterprise systems don’t come cheap. This is even more unsettling when considering they’re meant to interact with different deployments of ERP systems and with underlying systems that are themselves subject to regular modification and upgrades. Fortunately, a cognitive layer with a consistent data model can at least be a way to bring these disparate systems under one data roof.
And to address financial concerns, careful buyers will do well to ensure that the cost of these systems is directly connected to the benefits they are meant to provide. Ultimately, proper demand planning, for example, can reduce waste in the supply chain and help cover the costs, along with providing reduced working capital, better servicing, increased margins, and other direct benefits that justify the investment in digital transformation.
On the more human side, these analytical technologies tend to be a good fit with modern-style natural language processing technology, allowing humans to interact in time-saving, conversational ways, similar to the way they interact with Siri, Cortana or Alexa, which they do so at home as regular consumers.
Looking to 2019, the impending trade wars and other uncertainties will mandate increased agility and flexibility in supply chain operations. Technology must enhance human ability to perform analyses based on better data access and anomaly detection. Ultimately, the development of AI and machine learning will result in a future in which machines handle the lion’s share of analysis and action, with human involvement limited to supervision and value-adding input.
And to address financial concerns, careful buyers will do well to ensure that the cost of these systems is directly connected to the benefits they are meant to provide. Ultimately, proper demand planning, for example, can reduce waste in the supply chain and help cover the costs, along with providing reduced working capital, better servicing, increased margins, and other direct benefits that justify the investment in digital transformation.
On the more human side, these analytical technologies tend to be a good fit with modern-style natural language processing technology, allowing humans to interact in time-saving, conversational ways, similar to the way they interact with Siri, Cortana or Alexa, which they do so at home as regular consumers.
Looking to 2019, the impending trade wars and other uncertainties will mandate increased agility and flexibility in supply chain operations. Technology must enhance human ability to perform analyses based on better data access and anomaly detection. Ultimately, the development of AI and machine learning will result in a future in which machines handle the lion’s share of analysis and action, with human involvement limited to supervision and value-adding input.