With the rise in e-commerce, the supply chain business is swooning. Companies, stakeholders, shippers, retailers, and distributors are all linked together to complete the tasks of shipping goods and raw materials. All these operations require supervision and evaluation to drive efficiency and productivity and increase profit margin. The different types of supply chain analytics help collect and filter through vast and diverse datasets to find relevant and accurate insights that may help improve the business market and customer experience. Let us look at the various kinds of supply chain analytics and how it fixes the shipping industry’s problems.

What is supply chain analytics?

Companies affiliated with the tasks of the supply chain, including the ones in the shipping industry, use supply chain analytics to improve their operative efficiency. For this, the container, logistics and transportation industry collects enormous amounts of data daily while shipping operations. Companies effectively utilise these diverse and giant datasets to perform analysis. Supply chain analytics refers to collecting data from multiple systems and skimming them to locate relevant data that is further filtered for valuable insights. The tools used in supply chain analytics help enhance the efficiency of procuring, processing and distributing goods. It is done by implementing new action plans and improving the decision-making process. 

For example- If an organisation working with the supply chain wants to find out the reasons for its operational inefficiency and enhance productivity, it can combine all data generated from different systems along with some historical data and put it through analytics. The different types of supply chain analytics follow different processes to filter valuable insights. It offers easy management of shipping operations and minimises the challenges by tracing and addressing the loopholes. Every company fulfils different needs and can perform this computational analysis to identify potential risks by analysing market trends and customer data to perform accurate planning.

Types of supply chain analytics

The various types of supply chain analytics target specific operations in the supply chain.

1. Descriptive– It is meant to understand an organisation’s supplier and distribution market. Descriptive analysis extracts raw data from various systems in the supply chain to analyse the behaviour of suppliers, shippers and the company’s sales. This gives a better insight into the supply chain loopholes, discovering customer trends and patterns to gain accurate data for decision-making. It is utilised by manufacturers, retailers, and distributors in demand forecasting and improving sales.
2. Diagnostic– It enables organisations to determine the loopholes and inefficiencies leading to poor performance. Diagnostic supply chain analytics helps dig deeper into data to perform correlation analysis and locate deviations and anomalies. These data sets can then be studied in isolation to understand ‘why’ a particular event or mishap occurs. It is mainly used in the logistics industry, where automation is flourishing to complete tasks. Identifying the reasons for the breakdown of machinery or equipment helps minimise such problems in future.
3. Predictive– It performs calculative mathematical analysis on historical datasets to forecast any potential mishaps or breakdowns in the machines, vehicles or equipment used in the shipping industry. Predictive supply chain analytics uses technology to predict and prevent huge losses that could occur due to uncertain or abrupt failures that may cause supply chain disruptions. It can also be used to forecast sales, influx of demands, and inventory management.
4. Prescriptive– After forecasting and determining potential loopholes and challenges and their causes, a course of action is needed. Prescriptive supply chain analytics helps use data to anticipate various outcomes to filter out the most accurate and appropriate one. A digital twin can perform this task to offer the efficiency report for various solutions.
5. Cognitive– It is done by utilising machine learning to organise the extracted information from other analytics to turn the insights into actions. Cognitive supply chain analytics promotes automation in the shipping industry by using data correlation, semantic learning, and context awareness. It, therefore, tries to integrate diverse data sets into machines so that they can autonomously perform operations without human interference.

What are the benefits of supply chain analytics?

1. Locating potential risks– Supply chain analytics spots trends and patterns in data and can identify any underlying mishaps before they occur. This makes it better for companies to take action on it before time and implement measures to avoid any such anomalies in future.
2. Reduce operational costs– Real-time visibility of all operations and analytics of diverse data ranges helps in forecasting inventory management, maintenance tasks, solving challenges and inefficiencies, and getting potential solutions backed up by accurate data. It helps in minimising operational expenditure and driving the profit curve.
3. Accurate planning– Data provided from various sources and multiple systems is collected to sieve through relevant and accurate insights. For improved accuracy, data from blockchains can be utilised to filter valuable insights and perform real-time planning. The effectiveness and efficiency of products and resources can be measured to replace, modify, or discard them for more remarkable results.
4. Enhance supply chain efficiency– An overall analysis of different facets of the supply chain helps improve customer satisfaction, warehouse management, supplier relationships, logistics and transport operations and timely deliveries. It leads to a well-balanced and systematic flow of information and goods in the supply chain.
5. Future planning– By integrating technology such as AI, machine learning, digital twins, blockchain, and big data analytics in the supply chain, companies can efficiently and accurately plan for their future. It can be done by optimising decision-making, predicting the success of products and strategies, and balancing supply chain relations.

Applications of Supply chain analytics

1. Shipping industry– A well-maintained and managed inventory can handle the uncertain and bulk flow of orders and cater to it without any disruptions. It helps in demand planning by sieving through the insights gathered by the different types of supply chain analytics. For example- Companies need to know the customer and market trends as it helps in finding when an influx of demands may seep in. It also facilitates AI-powered predictive maintenance for shipping companies to prevent the uncertain breakdown of machinery and other technical resources that can cause significant delays and disruptions in the supply chain.
2. Logistics industry– Supply chain analytics helps in performing logistics management. One of the rising challenges in the logistics industry is the time gap between estimated time arrival (ETA)and actual time arrival (ATA). Both early and delayed arrival are significant financial concerns. For example,- Supply chain analytics helps reduce potential problems that can cause delays in delivery, such as machine breakdown, delay in procurement of goods due to inefficient inventory management, congestions due to ill-forecasting, and insufficiency of resources. Analytics can help address, target and present the reasons for all such problems so that organisations working with the logistics industry can help enhance their efficiency and profitability.
3. Manufacturing industry– The manufacturing industry is responsible for upgrading the products, and analytics proves beneficial. For example,-  Supply chain analytics helps in intelligent manufacturing by assisting with development methods, designing models and testing their efficiency, filtering the best material, and modifying products according to market needs. Raw material sourcing from economic suppliers helps reduce manufacturing costs, forecasting and measuring user requirements and how the products will be received in the market also plays a vital role in increasing the productivity and profit margins in the manufacturing industry.

Challenges in supply chain analytics

1. Large investment– The software tools used in supply chain analytics are expensive and require proper updation and maintenance, and renewal of resources from time to time. Hiring expert technicians to operate the installed software also requires investment. It takes time before you expect an enhanced ROI.
2. Expert operators– Integrating any software into the company’s technical ecosystem is a task. Be it standalone software or software that is a part of the more extensive software system, experts are needed to run them efficiently and fix the fundamental issues that emerge in their work. Some companies also offer expert training to increase efficiency and productivity in employees.
3. Data quality and accuracy– Organisations that work with supply chain analytics rely heavily on data, so data accuracy is a top priority. Data collection from multiple sources across multiple systems can lead to data loss or manipulation, which may impact decision-making. 
4. Data scalability– Organisations may not always require large datasets to come up with a solution or to filter insights. It’s challenging to scale data up or down to the desired level and ensure the datasets have appropriate and sufficient information for filtering relevant insights.

Future trends in supply chain analytics

1. Blockchain technology– Supply chain analytics requires immense stores of accurate and quality datasets. Collecting data from external sites hampers accuracy and may also provide manipulated data. Blockchains in maritime are digital ledgers that store enormous datasets in blocks with accurate information regarding assets, stakeholders and companies.
2. AI– It is difficult to perform operations manually with total efficiency. The risks of human errors are still a possibility that may hamper the analysis flow. Artificial Intelligence can access datasets or filtered insights to forecast underlying challenges in the supply chain and offer probable solutions.
3. Digital Twins– An amalgamation of Artificial Intelligence and machine learning, the digital twin can determine the efficiency of new or existing products to modify them and improve supply chain operations. The insights filtered using supply chain analytics need to be converted into potential solutions whose real-life implementation can be tested through technology.

These few technological trends may make it easier to seamlessly fulfil the demands of different types of supply chain analytics and make analysis more accurate and reliable.

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