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Big Role for Big Data in the warehouse

Big Role for Big Data in the warehouse

Five examples of how big data can be used in a WMS to offer new insights and innovation to provide competitive advantage

Big data offers potential benefits for warehouse operators through the analysis of information available from their warehouse management software (WMS). This is one of the five key trends for WMS that we discussed in a recent article. What are the key areas where this data can add insight and value to improve business performance, productivity, and efficiency?

First, a bit of background. Many early WMS operated in isolation in the sense that the data they used was typically generated and retained within the application itself. There was little or no interaction with other applications because those did not exist. Even information about incoming goods was often entered manually because it was not available in electronic format. Importing information from spreadsheets, technologies such as EDI, and direct links to other applications made it easier to get information into the WMS and this led to more efficiencies.

Later, and especially after integration with other systems became commonplace, application suppliers and users came to realise that the WMS itself was also generating vast amounts of information or “meta data” as part of its everyday operations that might reveal something useful with the proper analysis. This is the role of big data and data analytics. Here are five examples of how big data can be used in a WMS to offer new insights and innovation to provide competitive advantage.

Demand Forecasting: data can help warehouse operators predict customer demand and optimise inventory levels. By analysing historical sales data, market trends, weather patterns, and customer preferences, warehouse operators can anticipate future demand and adjust their stock accordingly. For example, retailers increase stock of items such as beer and barbeque supplies when good weather is expected for the coming weekend. Demand will be even higher if this coincides with a public holiday or major sporting event. Some of this is predictable but with retailers increasingly relying on one- or two-day lead times, their supply chains have to be agile enough to align orders to anticipated sales volumes within very short timeframes. Overstocking and understocking are equally inefficient but using all available information helps to reduce the risk while improving customer satisfaction and increasing sales revenue.

Quality Control: data can help warehouse operators monitor and improve the quality of their products and processes. By collecting and analysing data from sensors, cameras, scanners, and RFID tags, warehouse operators can detect and prevent defects, errors, and damages. Among other things this is critical to ensuring the maximum number of orders are delivered in full, on time, and in the optimum condition to maximise the customer experience. Information about unsold items and returns can also reveal insights and patterns that help improve performance. This can enhance product quality, reduce waste and rework, and comply with safety and regulatory standards.

Performance Management: data can help warehouse operators measure and improve the performance of their employees and equipment. For example, analytics can help identify optimum pick routes based on location of items and the frequency they are picked. This can help determine whether it is more efficient to pick orders one at a time in sequence or multiple orders simultaneously during a single pass through the picking area. By tracking and analysing data on productivity, efficiency, accuracy, and safety, warehouse operators can identify and reward high performers, provide feedback and training, and optimise workflows and schedules. When done correctly this can boost employee morale, motivation, and retention, as well as reduce downtime and maintenance costs.

Customer Service: data can help warehouse operators deliver better customer service and loyalty. By integrating and analysing data from various sources, such as CRM, ERP, social media, and web analytics, warehouse operators can gain a 360-degree view of their customers and their needs. This can enable personalised recommendations, promotions, and discounts, as well as faster and more reliable delivery and returns. A typical example of this is when a website presents customers with information about products “other customers bought” or “you might also like” and so on. Retail and business customers generally prefer to buy multiple products from the same supplier if they can because it makes their lives simpler. They are also more likely to give good recommendations and ratings if they have a good experience.

Supply Chain Visibility: data can help warehouse operators gain more visibility and control over their supply chain partners and processes. By sharing and analysing data across the supply chain network, warehouse operators can collaborate and coordinate with suppliers, logistics providers, and retailers. This can improve supply chain efficiency, agility, and resilience, as well as reduce risks and costs. It also enhances and strengthens relationships because other supply chain stakeholders will value the warehouse operator’s ability to share information. After all, they too are likely to be pursuing similar improvement objectives in their own operations.

Big data can help warehouse operators gain insights, make better decisions, and create value for their customers and stakeholders. Leading WMS incorporate many of the tools required to manage the data and complete this analysis. ProWMS BI from Principal Logistics Technologies, for example, utilises Artificial Intelligence (AI) in its Business Intelligence (BI) feature to identify and reveal hitherto unseen patterns or insights in real-time. This is an area that is evolving rapidly and which will no doubt lead to exciting and as yet unforeseen opportunities and innovations in the supply chain.

http://www.principalsystems.com/

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