Data analytics in logistics: a game-changer for companies

Data analytics has come a long way since its start, evolving from a descriptive tool to a revolutionary force for businesses. One of the industries where it is marking a new era is indeed logistics.

The world of logistics produces a vast amount of data every day, from warehousing activities to deliveries to end customers, but only companies that are equipped with the necessary tools to collect and analyse this data can truly gain a competitive advantage.

In this article, we will explore the impact of data analytics in logistics and how companies are driving innovation and decision-making thanks to its transformative potential.

What is data analytics?

Data analytics is the process of extracting and examining large datasets to uncover insights and patterns using statistical methods, mathematics, econometrics, simulations, as well as optimization techniques, assisting businesses in solving complex problems, improving their decision-making and enabling them to predict trends.

The evolution of data analytics

In the past, companies used to rely on descriptive analytics for insights into their historical performance. However, thanks to technological improvements, more sophisticated forms of analysis have emerged, like prescriptive and predictive analysis, both of which have been significantly enhanced by the integration of machine learning and artificial intelligence.

How data-driven strategies are shaping the logistics industry

The impact of data analytics in the logistics industry is immense, especially because Logistics and Supply Chain Management (LSCM) is overwhelmed by significant daily challenges that can easily lead to inefficiencies and unnecessary waste, like logistics delays, unbalances in inventory levels, bad shipping routes, fluctuating customer demands, and surges in costs, just to mention a few. Speed, adaptability, and precision are therefore crucial for success in this industry. Imagine if there was a way to decode the unpredictable aspects of logistics and make them more navigable. 

Data analytics is precisely this! By integrating Big Data Business Analytics (BDBA) and advanced data analysis in their logistics and supply chain strategies, businesses can become more adaptable and transparent, as well as coordinated in their operations. 

For example, while customers might not ever wonder about the journey behind the glasses they are wearing, logistics experts that work with the glasses retailer need to meticulously track every step. In this sense, leveraging data makes the process faster and more accurate. In fact, by examining past sales, retailers can understand trends related to their industry and predict demand fluctuations. This strategy is known as demand forecasting and ensures that materials, products, and distribution operations are perfectly aligned with market demand, thereby enhancing operational efficiency and customer satisfaction.

In short, thanks to data analytics, companies are able to make complex decisions regarding sourcing, the design of supply chain networks, daily logistics operations, demand planning, scheduling, and inventory management.

4 Types of analytics 

In logistics and supply chain management, there are different types of data analytics to consider.

Descriptive analytics

Descriptive analytics employs data to describe trends and relationships, for example the performance of the supply chain or the inventory levels in a warehouse. Descriptive analytics allows businesses to gain precious logistical insights on the different aspects of the supply chain and how it performs. 

Diagnostic analytics 

Diagnostic analytics employs data to identify a supply chain issue that could be the reason behind delayed shipments or missed sales. 

Predictive analytics 

Predictive analytics employs data and machine learning to predict future events, like market demand or potential disruptions. This is essential to be prepared for seasonal demand fluctuations and make prompt inventory replenishment decisions. 

Prescriptive analytics 

Prescriptive analytics employs data to recommend a course of action, for example the best route for a courier or the optimal inventory levels to maintain.

Benefits of data analytics in transportation and logistics

Better inventory management 

Managing inventory accurately and effectively, without incurring in excess stock or low stock problems is one of the major challenges for retailers. In fact, such common, yet unpleasant events are the main causes behind lost sales and inflated storage costs. Data analytics emerges as a powerful tool in this context, enabling businesses to leverage historical sales data and predict market demand. This transforms a management challenge into an opportunity, since businesses can adjust inventory levels proactively or order the right amount of stock in advance, leading to better marketing strategies – and sales! -, improved resource management, and operational efficiency. 

Reduced operational costs

Data can highlight inefficiencies in the supply chain, like order delays or unnecessary inventory holding. This means it identifies areas where performance is low and where automation or process improvements could reduce costs or increase speed, thereby paving the way for strategic advantages and new growth opportunities.

Better risk management and opportunity identification

Identifying risks and opportunities before they arise is not magic, but data driven. Data analysis applied to the logistics industry enables long-term planning and anticipation of events. For example, data can give a better understanding of areas in the supply chain whose performance needs to be improved. In other words, it can mitigate the risk of them turning into actual disruptions by allowing businesses to prepare backup plans beforehand. The same goes for opportunities! Data can reveal trends that are not immediately visible, allowing brands to introduce products ahead of competition.

Real-time data for increased visibility 

Transparency and comprehensive visibility across all supply chain processes are the backbone of successful logistics operations. If in the past this seemed like an insurmountable challenge, it has now become an advantageous reality. Thanks to real-time data insights that facilitate tracking and monitoring of processes, retailers have instant access to critical data, they can anticipate disruptions, optimise operations, and ensure more efficient and flexible logistics operations. This benefits both the business and the customers!

Sustainable logistics thanks to data analytics

Data analytics also plays a fundamental role in advancing sustainability within the logistics industry. In fact, it allows to optimise transportation routes. This translates into faster delivery times, while minimizing the use of resources, thereby significantly reducing the environmental impact of logistics operations. 

The future of data analysis in logistics

The better a company can perform its supply chain and logistics analytics, the better it is prepared for the future. In fact, this is what reinforces its long-term stability and sustainability.

The five Cs of supply chain analytics

For supply chains to thrive in today’s market and produce effective supply chain analytics of the future they must undergo a profound digital transformation, conformed with the five C’s, according to Simon Ellis. The main characteristics of supply chain optimization are that it must be connected, cyberaware, cognitively enabled, and comprehensive. Here’s what each of the five C’s means in detail.

Connected 

The modern and “thinking” supply chain integrates a diverse range of data sources, including social media, business-to-business (B2B) tools, and Internet of Things (IoT) devices.

Collaborative 

Modern digitised supply chains should be interconnected, ensuring the most efficient communication and information sharing between all relevant departments, from suppliers to customer assistance. 

Cyberaware

Digital supply chains are more efficient and interconnected but, for this reason, also more vulnerable to cyberattacks. In fact, modern supply chains should have safer systems and databases to increase protection from attacks.

Cognitively enabled 

Artificial intelligence (AI) is helping to build smarter supply chains. Most aspects of the supply chain are in fact self-learning, with information accessible in real-time, and automated to reduce manual tasks, as well as human error. 

Comprehensive 

The “thinking” supply chain is adaptive by nature, enhancing its data analysis as the volume of information grows.

Logistical insights: the importance of KPIs

Optimising logistics and improving supply chain operations requires a combination of monitoring logistics KPIs and employing data analytics tools. Companies should, indeed, foster an improvement culture backed by data, rather than guessing. In the logistics sector, this approach translates to streamlining and planning logistics activities based on empirical data, alongside meticulous tracking of progress of all processes. Only in this way, can businesses understand if they are making the right adjustments towards their goals and intervene promptly, if necessary. 

If you’re wondering which KPIs to monitor, here are some essential metrics. Not only can you monitor the efficiency of your business operations and drive cost optimization, but you will also be able to maintain high-quality service standards. 

1. Shipping Time: keep track of this KPI to identify potential issues in your order fulfillment activities.
2. Order Accuracy: keep track of this KPI to understand if there are any delays or drawbacks in your warehouse processes.
3. Picking Accuracy: keep track of this KPI to analyse the percentage of orders picked without incidents.
4. Delivery Time: keep track of this KPI to understand how to improve your services.
5. Pick & Pack Cycle Time: keep track of this KPI to understand how much time it takes to pick and pack every order and understand if it is efficient enough. You should set realistic targets to track and improve your pick and pack cycle time based on your total number of orders and your staff availability.
6. Inventory Carrying Costs: keep track of this KPI to understand costs related to storing unsold inventory. Use predictive analysis tools to forecast demand and keep inventory carrying costs as low as possible.
7. Warehousing Costs: keep track of this KPI to find cost optimization opportunities. 
8. Use of Packing Material: keep track of this KPI to avoid waste.
9. Number of Shipments: keep track of this KPI to understand how many orders are shipped from your warehouse. You could further break it down by analysing trends in these orders.
10. Inventory Accuracy: keep track of this KPI to avoid problems like stockouts or overstocking.
11. Inventory Turnover: keep track of this KPI to understand how many times your whole inventory is sold, information which indicates if your inventor planning is accurate enough.
12. Inventory to Sales Ratio: keep track of this KPI to identify potential overstocks.
13. Order Cycle Time: keep track of this KPI to monitor the time it takes to ship your orders. Keep it as low as possible!
14. Transportation Costs: keep track of this KPI to find opportunities to decrease all costs, from order placement to final delivery.
15. Dwell Time: keep track of this KPI to understand the average time drivers wait in the warehouse to drop off or pick up orders.

Conclusions

Data analytics has become indispensable for logistics, enhancing foresight and reliability in this industry constantly challenged by the need for speed and precision, as well as by sudden changes in market demand. Not only does data offer insights that allow companies to become more adaptable and competitive in the market, but also paves the way for a smarter and more sustainable future for the logistics industry. 

It is therefore essential to equip your company with the right tools to gather vast amount of data produced daily and analyse it adequately.

Explore ShippyPro’s analytics tools – user friendly analytics tools designed for ease of use! Get a 360° view of your shipping operations, effortlessly read data, and generate reports in just a few clicks to improve your business’ decision-making and performance.