End-to-end temperature and product movement monitoring.
Vehicle condition and location monitoring of assets.
Remote temperature monitoring for warehouses and cooling units.
As organizations operating in the cold chain seek out new ways to thrive and differentiate in a digital era, they’re quickly realizing how pivotal it is to incorporate new technologies into their business. Key technology that leverages the opportunities big data presents, including machine learning and artificial intelligence (AI), will continue to revolutionize key aspects of the cold chain, improve supply chain performance, and drive a better customer experience.
AI is an intelligence displayed by machines in which learning and action-based capabilities mimic autonomously without the need for human intervention. Machine learning refers to changes in systems that perform tasks associated with artificial intelligence, such as planning, diagnosis, robot control, and prediction. (Tiwari, 2017) Both rely on smart data aggregation. There is a growing recognition that machine learning will play a critical role in supply chain operations going forward, applying algorithms to help businesses turn large volumes of passive data into active business information and opportunities.
By estimate, supply chain businesses spend on average around 55 hours per week conducting manual, paper-based processes and checks, 39 hours chasing invoice exceptions, discrepancies and errors, and 23 hours responding to supplier inquiries. (mhlnews.com 2017). This loss alone adds up to around 6,500 hours per year that businesses are losing by manually processing papers, fixing orders, and replying to suppliers. This number doesn’t include time spent on examining shipment data as part of the quality review process if a temperature deviation or excursion occurs.
Today’s cold chains are increasingly gathering significant data as part of their day-to-day functionality. Data collection and inspection is a necessary part of the quality process. Learning from operational challenges enables quality managers and other professionals to build efficient and effective supply chains from well-formulated strategies, but big data and machine learning can shift the process from a passive one to an active, automated one.
Over the last decade, supply chains have shifted, with a greater focus on transparency and sustainability, which, in turn, has required businesses to embrace technology and innovation. Machine learning will continue to impact this paradigm shift.
In the future, not only will these technologies play an important role in businesses’ drive towards digital transformation, but by reshaping processes through the smart use of data, they’ll be better positioned to meet customer demands while driving a better return on investment.
With more and more cold chain products reaching the supply chain and an increasing number of stakeholders involved, this presents major challenges when tracking all products at all relevant locations and at any time. Next generation technology will help to improve procurement and logistics, driving the efficiency of order processing and on-time delivery by automating systems that can find the nearest products, get them to the right location in the shortest period of time, and processes can be reduced to just a few clicks, thus reducing the need for manual entry.
Today’s IoT connected devices in the cold chain can collect volumes of data from sensors and present opportunities to predict cold chain performance, identify risk areas in the supply chain, and provide better control over the cold chain. For instance, perishable goods traveling via a particular route may be more susceptible to spoilage during a particular time of the year due to seasonality of weather conditions. Data collected over time creates opportunities for continuous machine learning and optimization through predictive analytics that automate risk management and drive better forecasting. For instance, machine learning can help teams to determine which route to take and whether to ship via air or sea. Prescriptive analytics can take big data one step further and tell a user why something will likely happen, and not merely what will likely happen, which helps get to root cause issues and rectify them.
Supply chain professionals are constantly under the scrutiny of corporate management. The demand for accurate, efficient, and cost-effective logistics to grow the value chain is important for businesses operating in global supply chains. Through big data and machine learning, businesses reduce the amount of time needed to make decisions, which are more accurate. Data analytics and automation enable businesses to move their cold chain from cost center to competitive differentiator. Intelligence in cold chain logistics translates to faster and more accurate lead times and transportation expenses, increases global friendly operations, reduces labor costs, and results in a widened gap between competitors.
Businesses working with a wide range of partners, including suppliers and freight forwarders, can compile data over time to decide which relationships are mutually beneficial. The selection process for suppliers and other partners would be automated and the likelihood of success would be predictable and more reliable than a “decide, learn, iterate” approach. Selection would simply be intelligent instead of passive. Managing the actual partner relationship can be partly automated as they are provided with detailed information about their performance and what they need to do to meet customer needs. Sharing of data among stakeholders can help facilitate the best outcomes at every step of the supply chain in order to improve overall performance.
Businesses can leverage machine learning to improve the customer service experience, enabling customers to ask questions about the status of an order and receive a timely answer. Response time is lowered and resources can be shifted elsewhere. Retailers and food chains are employing AI technology to collect data on customer sentiment and buying behavior to help improve the buying experience while stocking the right products. This results in greater customer loyalty and brand integrity, which protects the bottom line.
To stay ahead of the competition, meet consumer demands, and improve customer service, it will be imperative for businesses operating in the cold chain to seek out innovation and strategize for the years ahead. Along with the right digital strategy and technology, these companies will be positioned to be more successful than ever.