According to the McKinsey global survey; Supply Chain is one of the top areas where businesses are gaining greater revenue from Artificial Intelligence and technology investments. 76% of the respondents reported moderate to significant value from deploying AI in their organisations. In fact, AI for supply chains hold high potential for boosting both top-line and bottom-line value, also functioning as an innovation accelerator.
On average, businesses estimate they spend around 55 hours per week on manual and paper-based processes and discrepancies/errors control, plus 23 hours of supplier or client management. By manually performing tasks, teams waste the majority of their time in never-ending tasks. Tasks which are data heavy that could be performed faster and more efficiently by a machine. This evidences a great opportunity for AI, to handle the immense volume of data (often unstructured) generated by a typical supply chain.
Supply chain managers would agree in stating that the ability to generate reliable, real-time data is vital to take immediate actions and mitigate disruptions at any point along the supply chain. Artificial Intelligence (AI), Deep Learning (DL) and Machine Learning (ML) can be implemented to gather, understand and inform decision makers within the supply chain.
AI capabilities for Supply Chain
In an on-demand, globally trading world, supply-chain disruptions are on the rise in many industries. Businesses should build flexibility into their supply chain, increase the accuracy of demand forecasting and improve risk management strategies.
From a business continuity and risk management standpoint, it is critical to have control over the supply chain as a whole. Awareness of potential threats is also vital when developing risk management strategies and critical situation response plans.
These technologies can track and clean data, detect anomalies and generate predictions to improve and create a smooth network for the supply chain from beginning to end.
Technological advances brought about by a digital transformation of the supply chain management, when configured correctly, can increase supply chain resilience through analytics, data and information sharing and scenario modelling. Business continuity is maintained through access to real-time data, followed by confident and data-driven actions.
Having the correct implementation of Machine Learning in Supply Chain Management tools can revolutionize the agility and optimization of Supply Chain decision-making. Machine Learning has the ability to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most critical factors impacting the delivery of goods, as well as identifying the best course of action.
One direct example of where AI is supporting Supply Chain Managers today is with Skim’s Supply Chain Risk accelerator. This AI accelerator can be used to identify events across the globe from thousands of news sources, social media, and weather data feeds, to identify ‘at risk’ trade routes. These risks are based on events such as environmental or political, however further models can be developed specifically to industries requirements.
Further to this event detection model, we’ve built the capability to match company names, such as Suppliers, (whether Tier 1, 2 or 3) to a risk area. Using the internal supplier database for a manufacturer, we can extract the address information and plot that on a map of the affected areas. This simple visualisation of suppliers and risks on a map offers so much valuable insight to supply chain managers who need to get a bigger picture of the macro effects on their business.
The challenges of implementing AI solutions in Supply Chain Management
Referring back to the McKinsey survey previously mentioned, the fundamental challenge identified in the report is in finding skilled people to implement AI effectively. The majority of respondents stated that they are “hiring external talent, building capabilities in-house and buying or licensing capabilities from large technology firms.”
With the increasing demand for Data Scientists in the UK and the US and the increasing gap between supply and demand for skilled professionals, businesses are outsourcing data science to gather an efficient team that can hit the ground running with the project and build a cutting-edge technology that can save time and internal resources.
Although events such as earthquakes, or terrorist attacks are hard to predict, supply chain disruptions could be reduced by having a clear overview of all the affected suppliers, products and raw materials along a trade route.
An effective AI solution would support decision makers through access to time critical and accurate data to support their role, this would need to be able to handle unstructured data from external sources to give a complete picture. The learning element of a system would use the managers’ response to further learn how to course correct and improve the data it provides, and eventually be able to make decisions.
Ultimately, with these technologies it comes down to clear business case development, understanding the data and processes in place to ensure that an AI system is designed to be as effective as possible.
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