In a logistics market full of unpredictable disruptions, supply chain data analytics provide a proactive solution to potential supply chain interruptions. Even during a “normal” marketplace environment, data can enhance supply chain logistics and increase customer satisfaction. According to a recent Gartner survey, supply chain executives rated advanced analytics as the second most important emerging technology in their field. Learning how to harness the power of applied data analytics can improve supply chain management, increase warehouse efficiency, and enable companies to make better-informed decisions.
Demand Forecasting and Process Optimization
Supply chain data analytics can uncover supply chain inefficiencies, such as delays, bottlenecks, or redundant activities. Access to accurate supply chain data about customer demand patterns, historical sales, and market trends, accompanied by a team of dedicated supply chain data analysts can help companies avoid these inefficiencies and outperform their competition. For instance, companies can use historical data to optimize transportation routes, which in turn enables them to better schedule labor for off-loading the arriving vehicle. The result is increased overall efficiency resulting in increased productivity, reduced costs and enhanced customer satisfaction, or simply put, process optimization.
Supplier Performance and Risk Management
The unpredictability of supplier performance can potentially hurt a distributor's reputation. A distributor can, however, evaluate supplier performance by analyzing data such as delivery times, product quality, and compliance with contractual agreements. By equipping a company with this data, they are positioned to make better decisions relating to supplier selection, negotiation of favorable terms, and identification of potential risks. Limiting the risk associated with supplier performance via data analytics simultaneously increases a company’s reputation while limiting fiscal liabilities.
Improved Warehouse Operations
The logistical improvements found in data analytics are most obviously demonstrated in the warehouse. Data on inventory levels, lead times, and supplier performance allows companies to optimize their inventory management. For instance, if analysts find that their company has slow-moving or obsolete stock, the company can then move that stock toward the back of their warehouse. Conversely, by analyzing the customer and historical trends mentioned above, a warehouse manager can move “hot” items toward more accessible parts of their warehouse. This reorientation can lead to more efficient replenishment strategies and an optimized warehouse. Additionally, a company can use data analytics to decide if they need to implement new or refine existing warehouse technology to meet the needs of their supply chain operations.
Enhancing Customer Service
A company's success is limited by its reputation. Data-driven insights, when applied, can boost a company’s standing by improving customer service, and therefore customer satisfaction. A 2022 study found that companies with superior supply chain management competency exhibit higher levels of customer satisfaction and shareholder value than their industry averages. Data analytics enables companies to proactively communicate with customers, manage expectations, and resolve issues promptly. The result is increased customer satisfaction and loyalty.
Associated Risks of Data Analytics
An important factor to note, simply collecting and hoarding data does not achieve any of these desired goals. In fact, data overload can lead to a litany of unnecessary and potentially dangerous outcomes:
An excessive amount of data with no clear purpose can lead to information overload which clouds the ability to identify relevant insights. Companies should focus on collecting meaningful data that align with their strategic objectives and can drive actionable decisions.
Collecting and storing large amounts of data increases the risk of data breaches or unauthorized access.
The collection and management of vast quantities of supply chain data require significant resources, including storage, processing power, and skilled personnel. If the data collected does not provide tangible benefits or align with the company's goals, it can result in unnecessary expenses and divert resources away from more pressing objectives.
Attempting to mitigate these concerns can prove risky for an inexperienced organization. Few organizations have the data foundation-meaning the relevant talent and skills, the technological infrastructure, and the access to ample resources- to implement data-driven insights on their own. Failure to use data analytics at the industry standard can deplete financial resources, increase the likelihood of security breaches, and decrease customer satisfaction throughout.
Outsourcing data analytics and data-driven insights to a third-party logistics (3PL) provider can alleviate a company from these costly risks. It can also enable a company to focus on the core goals of its strategy while collaborating with the third-party provider to optimize that strategy. Finding the right supply chain solutions provider can improve supply chain management, optimize warehouse technology performance, uncover supply chain inefficiencies, and enable a company to make better-informed decisions. All these forces, individually and working together, decrease costs and improve customer satisfaction.