The Importance of Big Data in Logistics and Supply Chain Management

Big data analytics in logistics and supply chain management

Data analytics is quickly becoming one of the most practical tools in not only distribution and logistics management but every industry. Modern analytics allows distributors to find issues within their operations to help create a better and more optimised distribution network. For example, this could be analysing bottlenecks, fulfilment, or any other area in the supply chain.

Access to better tools for big data analytics in logistics and supply chain management enables distributors to predict demand and changes within the market with extremely high precision. By having insight into market patterns such as what and how much stock is needed at various times of the year, distributors can fulfil orders on time, increasing the likelihood of customer retention and loyalty.

Big data has also proved effective for distributors of perishable goods such as food and medicine. Often these items require specialised storage and transit conditions. Big data can provide this information to guarantee goods are stored in the proper conditions to support their shelf-life.

Unlock the value of your Big Data in logistics and supply chain management with ERP software solutions

Distribution ERP software technology enables distributors to make sense of the big data they have. Distributors can reap the rewards and benefits throughout many areas of the warehousing process. Whereas some technologies may be useful for collecting big data analytics with higher precision and volume, other technologies allow for that data to be analysed and utilised to improve processes throughout the business.

AI/Machine Learning 

Artificial intelligence and machine learning are fast becoming leading technologies that can improve the efficiency and capabilities of distributors. AI and machine learning coupled with accurate raw data can provide distributors with game-changing data analysis, insights and highly accurate forecasting abilities. Providing your employees with insights from big data enables them to make better decisions and improve their efficiency over time, thus improving the business.

It is near impossible for humans to manually analyse the vast amount of data collected every hour of every day and provide a high degree of accuracy which will benefit a company. AI and machine learning are designed to ‘learn’ and not only make sense of the raw data that distributors have, but also provide suggestions and guidance, and find key trends and patterns that will enable processes to be improved with a higher level of accuracy.

Distributors can take advantage of: 

• Turning raw data into real insights 
• Improving customer service experiences
• Identify trends & patterns 
• Accurate demand forecasting  

IoT Technology 

IoT technologies and devices have seen a rapid increase in not only the capabilities which they have, but also their availability to many businesses. IoT incorporates different pieces of technology like sensors, software, and machine learning to collect data. Big data can then be analysed and used to improve processes throughout the distribution process.

IoT technology can be used to better the experiences that you provide to your customers. Being able to enhance the customer experience, distributors can better compete in an already highly competitive logistics market, not only in the UK but throughout the world.

IoT can be used to enhance areas of the distribution process including: 

• Increased pick accuracy/quantity 
• Improved inventory management 
• Improved tracking and traceability of products 
• Improved customer experiences 

Automation

Automation is being able to automatically run simple and complex tasks or processes without the need for human interaction. This saves time and can also reduce errors within the system to ensure the accuracy of your data and free up your employees to spend their time more efficiently.

Critical tasks can be automated, although this relies on big data being involved. Demand forecasting, for instance, requires data to be available for highly accurate forecasts to be produced. The higher the volume of big data in logistics, the higher the degree of accuracy you can achieve across all automated processes. With AI/machine learning constantly improving, key areas of the distribution network where accuracy and efficiency are a challenge will undoubtedly become more automated.

Areas of the distribution industry which can be automated include: 

• Demand forecasting 
• Inventory management 
• Reporting 
• Sales order processing