Impact of machine learning on supply chain management

Retail / eCom   |   
Published January 22, 2019   |   

Improved performance is of prime concern for any business or enterprise, and companies in the 21st century are using business intelligence-gathering systems for this purpose. The order fulfillment cycles use the root cause or the post-mortem data analysis for the identification of the gaps. The growth of machine learning has helped the companies to analyze the transactional data in the real time.
But what are the needs which strengthen the idea of technical changes in business and supply chain management?
The potential loss of revenues, hike in the production and transportation costs, and lack in the customer service due to the operational inefficiencies have molded such a platform where the advancements become the need of the hour. These negatives when experienced collectively result in diminished profits, which leads to the advent of machine learning in the industry.
For example, social media has a crucial role to play in this sector. It has contributed to amplifying the damages recalled by product recalls, along with raising the expectations of the consumers. LeanLogistics is using flowing transportation data through its network, for example, carrier performance, transit times, rates. The development of the “transportation index” for TMS visibility to market-level trends is the main purpose.
Another instance is the delay in marine shipment to Singapore from the US. The process takes 20 more days to complete than the scheduled time.
Yes, social media and its relation in the context is resulting in the trigger of the chain of operational and business issues.

How is ML impacting SCM?

But how we have reached an automation level in the manufacturing industry where the reliability score is high is exciting. Big Data and the advanced analytics give a boost to the impact of machine learning on supply chain management. The ways involved in the advancement are-

1. Hike in the analysis power of distinct data sets and enhanced demand forecasting accuracy

Machine learning and improvements have resulted in considering the existing factors in the production sector, one of the variable areas of supply chain management.
For instance, Lennox has mastered the supply chain and has encountered improvement of input in the SAP Planning System. Moreover, the balance of the service levels and the inventory cost is now maintainable.

2. Assist in improving supply chain management performance

Unlike the other technologies in the field, Machine Learning and its core constructs provide vision and methods to enhance the performance. All the learning methods such as supervised learning, unsupervised learning, and reinforcement learning, ML is turning out to be an effective technology.

3. Abate supplier risk, minimize freight costs

It is the most anticipated and needed improvement in the supply chain management industry, and ML identify the horizontal collaboration synergies between multiple shipper networks. An example of the technology with which it has been made possible is ‘The Predictive Policy’.

Past Situations Achieved Situations
Ambiguity in interpretations of pieces records and orders Apt and clear shipment and pieces identification (No non-piece lines)
The clutter of measurement units Complete set of measurements: surface/pallets, volume, and loading meters
Missing information of size at the piece and order level 3D loading factors with the help of full set measurement of each piece, i.e. length, width, height, volume
Problems such as redundancy in data, for instance, senders with multiple name spellings AI has grouped the senders
The unreliable and missing capacity of information for linehaul Artificial Intelligence algorithm for the estimation of missing capacity information

4. Impact on the maintenance of physical assets

The advent of visual pattern recognition has changed the support of physical assets across the supply chain network. Inspection of the inbound quality has also been automated by Machine Learning with the help of algorithms, isolation of product shipments, logistics hub.
For example, Watson Supply Chain in IBM Watson has combined visual and systems-based data for the tracking purpose. It has been used to check is there is any damage.

5. ML+ Related Technologies – Higher contextual intelligence

Logistics Control Tower operations are utilizing the power of technologies to get new insights for the improvement of warehouse management, collaboration, logistics, supply chain management.

Artificial Intelligence applications in SCM

The areas on which the technology can be applied within SCM activities are numerous. Two of them are as follows-

In carving the strategy of supply chain management & supply chain planning

It plays a crucial role. To be competitive in the business world, a perfect set of work tools are needed.
The affected and improved areas are-

  • Demand
  • Supply
  • Forecasting Inventory

What would be the result of the application?

Revolutionizing the optimization and agility of supply chain decision-making is the outcome. The intelligent scenarios based on the related algorithms as well as machine-to-machine significant dataset analysis are utilized by the professionals for a responsible SCP.

Chatbots for Ease in Operations

“Procuebot” is the term which signifies the brain of the machine. The related tasks require automation and augmentation of Chatbot is a medium to streamline procurement which further needs the access of intelligent and robust data sets.

What have the chatbots been empowered to do?

  • Handle the governance and compliance materials
  • Speak to suppliers when conversations are trifling
  • Request for purchase

Implementation of AI has seen SCM as a part of the value chain and has affected it, for the good and the bad. It has also risen the security and the safety concerns of IT infrastructure and humans. Yes, automation and augmentation are heading towards job replacement.