“Without data, you are just another person with an opinion.” – W. Edwards Deming

That is how important data is and with it increasing by leaps and bounds, managing it in a proper way is highly crucial to the success of any organization. Especially when it comes to a volatile and vital industry like the logistics domain. There is so much happening at every step of the supply chain and logistics industry.

At each step of the supply chain, data is getting generated – in different forms, in different sizes and not necessarily, in the correct format. But it offers a lot of information that can prove valuable for business, only if you know how to leverage it correctly.

There is a series of activities that it contains, each of which has data. Without data governance, it could lead to bad data quality, data silos and directly impact client relations and business outcomes.

For taming the data that is wild and unruly, you need proper data management strategies that must be implemented. The outcome would be usable and disciplined data that will help bigtime in taking the right decisions, at the right time and at the right place.

Why is Data Management Crucial to the Logistics Industry?

There are several challenges that the industry faces, when it comes to data:

• Securing data governance and complying with the privacy regulations like GDPR etc.
• Managing user access for sensitive information
• Blowing up of data volumes due to sensors, IoT devices etc.
• Wastage of time and quality over preparing data to be available for analysis
• Lack of skilled resources who are well-trained for data science and engineering activities
• Access to client, employee, financial data in a confidential manner

Salient Benefits of Data Management in Supply Chain & Logistics

There are certain evident benefits that data management can tag along and can help in overcoming the above-mentioned hurdles:

• Centralized management of information and easy access to stakeholders
• Effective last mile delivery
• Increase in business efficacy through automation of tasks
• Seamless integration with other systems leading to pattern finding, trend analysis
• Reliable, secure, scalable data lying in properly arranged formats
• Easy backup and restore mechanisms with almost 100% uptime

Key Data Management Best Practices for Effective Logistics Monitoring

When it comes to data management, it must include major stakeholders like the team who is responsible for different activities, the policies and procedures that are involved, best practices that are included at each phase and technologies that are instrumental in managing the data.

Here are some of the strategies that can be implemented for effective management of data:

• Demand Planning and Forecasting

If data is effectively recorded and managed, it can be of great benefit while planning the demand cycle and for future use. Knowing about what could be needed in the coming days can be effective in taking stock of the inventory and planning for its reorder level. It would also help in maintaining least dead stock. Data silos can be avoided since there is an overall view of the complete ecosystem.

• Appropriate Security and Access Control

The prime underlying matter of concern for any type of data is the security of the same. Any type of unwanted access can lead to data manipulation or theft. Hence, having a proper strategy for ensuring security access in the right manner is very important. Modern day technologies, that enforce role-based security mechanisms, must be leveraged to do the same.

• Seamless Data Transfer Across Other Systems

In any supply chain setup, there could be disparate systems that are functional and may need to talk to each other. The right way is to setup a connection between all involved systems through digital technologies so that they can easily communicate with each other. This way, data can flow within systems and remain secure and available to the right target audience.

• Measuring Effectivity of Supply Chain Operations

In this industry, it is the supply chain activities that must be monitored through modern techniques. The general performance must be interpreted so that inferences can be taken from that, with corrective action. Key stakeholders must be kept aware of what is happening when and how. This can empower them to take full control of the situation, through a transparent mechanism and march ahead towards better management of data.

• Leveraging Tools and Technology to Ensure Quality of Data

Having data along is not going to suffice, since the state in which it is, matters the most. If the data is not of good quality, there is hardly any value that organizations can obtain, even if it is in huge bulks. But, good quality data, even if in smaller chunks, can prove fruitful. To have an enhanced version of data, the cutting-edge tools and technologies like AI & ML models must be implemented to get best of results.

Good Read: Generative AI in Supply Chain Management

In a Nutshell

Looking at the above data strategies, it is important to understand how critical it is to manage the data coming from supply chain and logistics domain. The data must be good quality, secure, reliable, well-arranged, accessible, valuable and only then can it be of any value to the company. And for that to happen, latest technologies like AI & ML need to be incorporated.

At Ridgeant, we have observed significant outcomes in supply chain optimization for clients across the world, through our data and AI-driven solutions. Our proficient taskforce has been exploring the limitless possibilities of AI and Machine Learning ranging from natural language processing to recommendation systems and embark on a journey of data-driven transformation.

Our exhaustive suite of data and AI-driven solutions for the supply chain and logistics industry, that empower your supply chain, ensure timely deliveries, cost optimization, and streamlined operations, are well-designed and full proof. Right from route optimization to real-time tracking and predictive maintenance, our solutions encompass it all.

React out to us for exploring our range of AI and ML solutions that can be considered fit for the supply chain and logistics industry.