Over the past decade, supply chain complexity has increased substantially with the rising pressure on retailers to offer unlimited, fast and free shipping. Frequent disruptions, like damaged or delayed goods, are the most common symptoms of this trend.
In manufacturing, an asset-intensive sector, the cost of even small disruptions can add up quickly. Some manufacturers are already leveraging digital technologies like the Internet of Things (IoT), Big Data and advanced analytics to reduce the frequency of costly mistakes and streamline operations. Low-cost sensors and connected devices embedded into key areas in the supply chain are relaying critical real-time data on asset location, quality and status. For instance, smart sensors can detect an uncharacteristic temperature change to in-transit cargo the moment it happens, allowing for immediate corrective action and preventing a potentially catastrophic loss of goods.
A McKinsey study found that the average supply chain has incorporated technologies like Big Data analytics into 43% of processes; they call this “digitization.” This number is expected to rise as improvements in overall cost, agility, efficiency and customer satisfaction are recognized.
Before undergoing this digital transformation, companies must first take these five important steps:
1. Establish business criteria
Before manufacturers can begin to reap the benefits that come with supply chain digitization, they must establish metrics and benchmarks that look at:
- Real-time indoor and outdoor location of goods
- The treatment of goods (e.g. handling, temperature, moisture)
- Distance between destinations & travel time
This data can help identify areas of improvement and key performance indicators (KPIs) that align with business objectives.
Take, for example, a plastics supplier implementing an IoT solution to reduce turnaround time from its warehouse to shipping docks. Employees would first walk the entirety of the warehouse searching and gathering inventory packages for shipment using a barcode scanning process. This real-time visibility of the location and condition of packages on-premise and while in transit is critical to optimizing the solution’s efficiency.
2. Identify important data sources
Next, important data sources must be identified and outfitted with smart labels which have GPS, temperature, accelerometer, moisture and pressure sensors. Designating label s ahead of time is important because too many labels might generate excess data that wastes time to sift through, and too few might provide too little data to be of use.
A mid-sized seafood distributor concerned with the freshness of an assorted shipment of fish between the warehouse to a single retailer, for instance, may only need to include one smart label per variety of fish.
3. Accrue data, aggregate it and evaluate the right level of processing
With critical data sources identified and denoted with smart labels, all that real-time data needs to be aggregated into IoT gateways. Depending on business objectives, these gateways could be located in a warehouse, on vehicles or both. They analyze the sensor real-time data and the required data points like speed, temperature and pressure are extracted and stored.
More complex supply chains with additional business objectives will require more data processing. If sensors are sending data to IoT gateways spread across multiple warehouses and a fleet of vehicles, they will need to be pushed to and processed in a secure cloud, where they will then be transformed into actionable insights.
In the case of the plastics supplier, simply seeking to reduce the shelf-to-shipping dock turnaround time in one small warehouse with all computation performed on one or two on-premise gateways may suffice.
4. Develop applications for visual and actionable analysis
If a picture is worth 1,000 words, then an analytics dashboard is worth 1,000 terabytes of data. Computed and processed data is only valuable if it can be transformed into clear business insights. This is best achieved by developing applications with tools for visualization and reporting, like mobile and web dashboards.
These tools would allow the plastics supplier’s logistics team to compare incoming data against industry shelf-to-shipping dock best practices to identify any necessary corrective actions that could lead to increased speed.
5. Go preemptive with automation
Once the IoT asset management solution is up and running with reports and analytics beginning to materialize, many issues that may arise in a supply chain can be resolved automatically. This is where the power of IoT in the supply chain is made most evident. Automation significantly reduces manpower requirements and the margin for human error, especially in complex 24/7-365 operations.
If smart label sensors report that the temperature inside one of the seafood distributor’s trucks in transit exceeds average levels, an alert would be sent to the driver’s mobile device.
Finally, it is important to keep in mind that digitization is more of a steady run, not a sprint. It’s better to start modestly, and then grow after establishing which solutions and processes are most effective.
IoT solutions, like smart asset management, have already had a swift and dramatic positive impact on the success of the supply chain. According to a PwC study, “companies with highly digitized supply chains and operations can expect efficiency gains of 4.1 percent annually, while boosting revenue by 2.9 percent a year.”
Real-time insights and automated problem solving have resulted in more efficient and agile operations for asset-intensive businesses, allowing them to better address and even anticipate the ever-evolving needs of their customers. If digitization continues at its current pace, there is potentially a proactive, predictive, automated and personalized future for one of the most important parts of worldwide industry.