Machine Learning to transform Supply Chain

Before we discuss how Artificial Intelligence and Machine Learning can transform Supply Chain, let us first understand what is ML. 

Machine learning is a subset of artificial intelligence that allows an algorithm, software or a system to learn and adjust without being specifically programmed to do so. 

ML uses the existing data as training data in a computer model where different patterns in the data is analyzed to predict the future outcome

There are various models of ML algorithms, which are great at analyzing trends, spotting errors and predicting values within the massive datasets.

There are various methods of Machine Learning

a)   Supervised Learning

b)   Unsupervised Learning

c)    Reinforcement Training

Below is the architecture of ML

a)  Supervised Learning

In supervised learning, the training data used for is a mathematical model that consists of both inputs and desired outputs. Each corresponding input has an assigned output which is also known as a supervisory signal. Through the available training matrix, the system is able to determine the relationship between the input and output and employ the same in subsequent inputs post-training to determine the corresponding output

The supervised learning can further be broadened into classification and regression analysis based on the output criteria.

b)  Unsupervised Learning

Unlike supervised learning, unsupervised learning uses training data that does not contain output. The unsupervised learning identifies relation input based on trends, commonalities, and the output is determined on the basis of the presence/absence of such trends in the user input.

c)  Reinforcement Training

This is used in training the system to decide on a particular relevance context using various algorithms to determine the correct approach in the context of the present state. These are widely used in training gaming portals to work on user inputs accordingly.

Challenges in Supply Chain :

Few of the challenges faced by Logistics and Supply Chain which ML and AI powered solutions can solves are

a)   Poor Resource Planning

b)   Inefficient Supplier Relationship Management

c)    Satisfying Customer Needs

d)   Quality and Safety

e)   Technical Downtimes

f)     Cost Inefficiency

g)   Determining Pricing

h)   Transportation Cost

 Many of the largest and renowned firms are focusing on ML to improve efficiency of their supply chain. Let us understand how ML addresses the problems and current applications of this powerful technology.

a)   Predictive Analytics  

There are several benefits of accurate demand forecasting in supply chain management, such as decreased holding cost with optimal inventory levels

Using ML models, organizations can benefit from predictive analytics of demand forecasting, these ML models are adept at identifying hidden patterns in historical demand data. ML can also detect issues in supply chain even before they disrupt the business

 b)   Automate Quality Inspection

 Warehouses/Hubs conducts manual quality check to inspect packages for any kind of damage during transit.  ML helps in automating quality inspection in supply chain lifecycle.

Image recognition models can be used to automate the inspection process which can minimize the delivery of defective or faulty goods to customers

 c)   Real-Time Visibility – increases Customer Experience.

 ML in combination with deep learning, IOT, and real-time monitoring can be used to improve supply chain visibility. ML models and workflows do this by analysing historical data from varied sources followed by discovering interconnections between the processes along the supply value chain.

 d)   Production Planning

 ML plays a important role in optimizing the complexity of production plans. ML models can be used to train sophisticated algorithms on the available production data in a way which helps in identification of possible areas of inefficiency and waste.

 e)   Response Time and cost efficiency

 B2C companies are leveraging ML models to trigger automated responses and handle demand to supply imbalances which helps in improving response time and cost reduction.

 The ability of machine learning algorithms to analyse and learn from real-time data and historic delivery records helps supply chain managers to optimise the route for their fleet of vehicles leading to reduced driving time, cost-saving and enhanced productivity. 

Improving connectivity with various logistics service providers and integrating freight and warehousing processes, administrative and operational costs in the supply chain can be reduced.

f)     Warehouse Management

ML enables continuous improvement in the efforts of company towards meeting the desired level of customer experience. ML in supply chain, with its model, techniques and forecasting features can also solve problems related to overstocking or understocking.  The big data sets can be analysed must faster and also avoid mistakes made by humans in typical scenario.

g)   Forecast Errors

ML has various robust analytical tools which helps supply chain companies to process large data sets with greatest variety and variability. Using multiple data sets from telematics, IOT devices, TMS and similar SCM tools, ML based models can reduce the forecast errors by more than 50% .

h)   Last Mile Tracking

ML models can take inputs from different data points about the ways people use to enter their addresses and the total time taken to deliver the goods to specific locations. ML models offers valuable assistance in optimizing the process and providing clients with more accurate information on the shipments.

While ML is surely going to help organization to transform their supply chain, one first need to identify which models is applicable to which type of problems to solve. As mentioned above, today there are various classification and regression models are available which can be used to solve supply chain problem. Few of the statistical and ML models are listed below.

KNN, Random Forest, Artificial Neural Network, Decision Tree, Logistics Regression, Linear Regression, Time Series, Na?ve Bayes, Clustering, Linear Discriminant Analysis, Support Vector Machines, Text Mining etc .

Machine Learning has various benefits for supply chain, implementation of right model/models for improving efficiency , optimizing cost and increasing the customer satisfaction is a key driving factor.  

Organizations need plan for the future and invest in machine learning and related technologies today to enjoy the benefits of increased profit, efficiency and better resources availability.

 In my forthcoming articles, I will be sharing more on these models and its application in solving supply chain issues.