How AI can solve the Supply chain and logistics problems

Study by McKinsey found that 61 per cent of executives report lower costs and 53 percent Improved sales as a direct result of the introduction of artificial intelligence in their supply chains;

More than one-third report a five per cent rise in sales. 

Although the scope for AI application and implementation in this graph by HBR can be easily visualised, a percentage allocated for supply chain management ( SCM) is missing. That's because the application of AI to SCM-related activities has not been revised on a large scale by the companies surveyed

AI can be used in many fields of supply chain which would help the company save a lot of time and money. Some of the major uses of AI for this purpose can be in

 

Demand forecasting:

 

The worst sight for someone responsible for inventory management is overflowing inventory and an eager customer leaving the store disappointed. Ideally, retail stores' inventory capacity should be precisely matched with consumer demand within a given time period. But we don't live in an perfect world and conventional demand forecasting methods lack the ability to take fluctuating consumer demands, supply conditions, and evolving market trends into account. But with AI-driven demand forecasting models, one can predict the demand with an accuracy of 90+%, so that situations of under and overstocking are avoided.

Chatbots for Operational Procurement:

 

Streamlining procurement-related activities by automating and improving Chabot capability includes access to comprehensive and smart data sets where the 'procuebot' may be used as a reference frame;

Chatbots may be used as for everyday tasks to:

· Chat during casual interactions with suppliers.

· Set and submit suppliers acts relating to governance and materials enforcement.

· Insert requests for payment.

· Examine and answer internal procurement functionalities or supplier / supplier set queries.

· Invoice receipt / filing / documentation, and payment / order requests

 

Machine Learning (ML) for Supply Chain Planning (SCP)

 

Planning the supply chain is a key task within SCM strategy. In today's business world, having smart work resources to create concrete plans is a must.

ML, applied within SCP may help with inventory, demand and supply forecasting. If implemented correctly through SCM work tools, ML could revolutionise the agility and optimization of decision making in the supply chain.

 

Based on intelligent algorithms and machine-to - machine analysis of large data sets, SCM experts — responsible for SCP — will have the best possible scenarios. Such capability could optimise the distribution of products while managing supply and demand, and would not require human review but action setting for performance parameters.

 

 Machine Learning for Warehouse Management

 

Taking a closer look at SCP 's market, its performance depends heavily on proper warehouse management and inventory-based management. Regardless of demand forecasts, supply shortages (over-stocking or under-stocking) can be a catastrophe for just about every consumer / retailer dependent business.

 

ML offers an endless forecast loop, carrying a continuously self-improving performance. Such capabilities could reshape the management of warehouses as we know today.

 

Autonomous Vehicles for Logistics and Shipping

 

Information and data in logistics and shipping has in recent years become a centre-stage kind of focus within supply chain management. Faster and more reliable delivery decreases lead times and freight costs, adds elements of environmentally sustainable operations, lowers labour costs and – most important of all – widen the gap between competitors.

If autonomous vehicles were built to the potential-speculated by some market analysts the effect on logistics optimization would be astronomical.

 

 Supplier Selection and Supplier Relationship Management (SRM)

 

Data sets produced from SRM actions, such as supplier appraisals, audits and credit ratings, provide an essential basis for further supplier decision making.

This (otherwise) passive data collection could be made active with the aid of Machine Learning and intelligible algorithms.

 

Supplier selection will be more reliable and intelligible than ever before; building from the very first partnerships a forum for success. All this information will be easily accessible for human inspections but created by automation from machine to machine; providing several 'best supplier scenarios' based on whatever parameters the user needs.

What’s the catch? 

One could hypothesise that for the better and the worse, SCM is a part of the value chain that will be heavily impacted by AI implementation. Augmentation and automation inevitably pose safety and security issues for Both infrastructure and human life.

But, something potentially much more business-threatening: AI implementation will begin to replace jobs.

References

https://www.mckinsey.com/featured-insights/artificial-intelligence/global-ai-survey-ai-proves-its-worth-but-few-scale-impact#