Supply chain AI technology to go mainstream in 2020

Date Posted: 22/11/2019 Category:
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Supply chain AI technology to go mainstream in 2020

Date Posted: 22/11/2019 Category:

Artificial intelligence (AI) and machine learning (ML) technologies in procurement will move beyond their current ‘hype cycle’ and deliver tangible use cases that yield demonstrable business value, an expert has predicted.

AI and ML solutions expert Dr Madhav Durbha told Supply Chain Management Review that these technologies are at the point of transition to the mainstream in procurement and supply chain work in all industries.

Dr Durbha predicts that the following new developments will enter the mainstream in supplier relationship management and supply chain management more broadly in 2020.

Accurate forecasting of volatile order patterns

AI and ML tech will allow businesses to more accurately anticipate fluctuations in less stable order patterns from customers.

With major online retailers upping their order volumes in recent times, suppliers are experiencing significantly more volatility in their demand signals.

AI models can deliver significantly more optimal levels of supply in these highly challenging environments.

Accurate sensing of market fluctuations

AI has the capacity to utilise a vast range of external causal data, including weather, CPI, industrial production, GDP and employment levels to make accordingly tailored predictions of market shifts and drivers of demand.

This will bring significantly improved sensory capabilities into decision-making around supplier relationship management, capital expenditure, product portfolios and longer-term capacity planning and strategic sourcing.

Delivering improved cost reductions by reducing chargeback

Procurement and supply chain practitioners are painfully aware that retailers routinely charge heavy penalties for missed On Time in Full (OTIF) deliveries.

Algorithms capable of deep learning can scour vast reserves of shipment data, pinpointing order times, order types, volumes, locations and modes of transport to locate the fundamental causes of chargebacks and therefore predict points of potential failure.

Dr Durbha also explained that digital representations of supply chains need no longer be hampered by inaccurate patchwork models.

Cloud computing and algorithmic intelligence can digitally render a living model of the supply chain, along with simulations of real-world events to predict outcomes and improve rapid, pre-emptive decision-making.

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