Big Data in Manufacturing: How Digital Transformation Weaponizes the Supply Chain

Change doesn’t disrupt the system. It’s part of the system.”-- Susan Berfield and Manuel Baigorri describing Zara’s supply chain innovation in “Zara’s Fast Fashion Edge

Clothing retailer and manufacturer Zara introduced the concept of fast fashion two decades ago. Instead of purchasing classic pieces built to last, Zara builds trendy, affordable clothes that last as long as the trends themselves. The supply chain is its competitive advantage. While other retailers order 80 percent of their garments at the beginning of the season, Zara averages only 50, relying on high-tech equipment to make sudden changes in production. Information about what’s selling is fed back to the designers, when store managers make twice-weekly orders for new shipments. Unsold products make up less than 10 percent of inventory— compared to an industry average of 17 to 20 percent.

Logistics management in the digital age builds upon an interconnected network, streamlined remote workforce communication, and flexibility in the system. Connecting each major player in the supply chain with the visibility and data they need to be more effective generates serious ROI. Hybrid supply chains, relying on a mix of digital and traditional techniques, lose data at each step in the process, along with the key insights that lead to serious transformation.

 

Hybrid supply chains Digital supply chains
Combination of physical and digital information Completely digitized
Information kept in silos from other plants, teams and managers Complete visibility into all aspects of the product lifecycle
Inaccurate forecasting and demand planning Data-driven, real-time inputs into all planning activities

Key enablers of the supply chain transformation

  • Cloud management. Tracking data on where materials are in the process depends on shared data in the cloud. Cloud-based availability of real-time data powers the digital transformation of the supply chain, as well as the connection of disparate IT systems throughout the network.
  • Geolocation. RFID tracking, sensors, beacons are all generating new sources of information as to where products are in the supply chain. Making indicators also tracked, such as temperature or pressure, also makes the operations more effective.
  • Data analytics. Using predictive analytics and modeling techniques, companies can make gain crucial insights into production changes, inefficiency bottlenecks and potential shutdowns. Investing in big data with a first investment in data maintenance and cleanup, reaps serious benefits when it also comes with a corresponding investment in the user experience of the tools to visualize and manipulate the data. Demand forecasting and capacity planning are two major issues to solve here.

Holistically, these enablers solve the most pressing problem with the supply chain: visibility. Too often, everyone from the C-suite to the operators themselves are in the dark about product and raw material location, and lose out on the potential ROI with more effective decisions.

Case in point: dynamic scheduling. Nearly every customer will want their delivery at the same time. Instead of overbooking the busy shifts and leaving trucks and drivers idle during non-peak times, using the mobilized data of customer inventory levels to power a scheduling process that also pulls in driver shift and truck availability will increase efficiency of the entire process and cut down on the hard costs related to scheduling. Working with suppliers and customers to offer incentives to book less popular times will help even out labor costs and avoid backlogs.

One estimate says that senior manufacturing personnel spend 40 percent of their time away from their desks. This time is frequently spent on “safety walks” and equipment checks as part of overseeing the factory floor’s overall efficiency and safety. Imagine how much more powerful these walks would be if along the walk, BLE beacons generated performance reports to the manager’s mobile device, exception alerts were sent via push notification to the entire team, and the data was able to predict the asset issues that will cause lower quality. The paperwork pile two inches thick can come from a quality assurance specialists’ mobile devices, which have forms auto-populated with identification data and tagged for easy sorting. For regulations and audits, the real-time traceability of parts and performance data can provide validation of compliance and support a firm’s commitment to vigorous adherence to regulatory structures.
The most effective manufacturer-distributor operations cross dock with digital tools, using data analytics and predictive modeling to move inventory quickly from its drop-off truck to the store delivery truck, minimizing the cost of inventory as much as possible. This becomes so much more effective when predictive modeling using inputs on the elements of the materials (weight, shape, surface area) is utilized. According to Forrester inquiries, about 30 percent of items in grocery manufacturing have at least one wrong element that throws off loading trucks. How much do those errors cost? It's inputs like those that create the ROI calculation for a digital solution.

Find out how digital transformation is catalyzing major changes in the manufacturing industry with our latest industry research report.

digital transformation roadmap for the manufacturing industry