Vince Lombardi, the late, great football coach, once stated, “Perfection is not attainable, but if we chase perfection we can catch excellence.” Logistics professionals could adopt that statement as their motto. Half-a-decade ago, Apptricity analysts wrote, “In the world of supply chain and logistics management, perfection remains an elusive goal. Even when companies have been generally successful in reducing the number of imperfectly fulfilled orders, the high cost of the very small percentage of orders that aren’t filled completely, on time, damage-free and with all proper documentation and invoicing remains a vexing problem.”[1] Between then and now, logistics has become a focal point in today’s e-commerce environment; but, the perfect order remains the elusive ideal. “For years,” writes the staff at SupplyChainBrain (SCB), “logistics professionals have used ‘perfect order’ as an indicator of performance. Perfect order remains a key goal, but is there a perfect logistics network to meet the expectations of digital commerce?”[2] In pursuit of perfection, logisticians are turning to technology to achieve excellence. Daniel Gagnon, Vice President of Marketing for Global Logistics and Distribution at UPS, explains, “This fast-paced, new world of change in logistics is happening because businesses and consumers are demanding better (faster) service at a lower-cost-to-serve. More than any other time in history, technology and innovation are here to make it happen.”[3]

Room to improve

Apptricity analysts note that being “nearly perfect” still leaves room for perfection. They explain, “If a company scores an impressive 99 percent in all four categories measured by the standard Perfect Order Metric (POM) — complete, on time, damage-free, proper documentation with correct invoicing — the result would be a POM score of just 96 percent. That’s because scores are calculated by multiplying the scores in all four categories. In our example, that’s .99 x .99 x .99 x .99 = .96. So a company with these near-perfect scores in all POM categories still has considerable room for improvement.” They add, “In reality, most companies don’t have anything close to scores like that.” Since there is room to improve, Gagnon insists there has never been a better (or more crucial) time to innovate. “Logistics companies cannot afford not to be innovative,” he explains. “Fail to innovate, and customers will view your organization as providing no value. Standing still is not an option. Technology is getting lighter. Cloud-based systems are eliminating a key barrier to entry into logistics. Nontraditional competitors have entered the market delivering better analytics and creating value with minimal investment.” The SCB staff insists the pursuit of perfection begins with design. They explain:

“Experts define the ‘optimal’ distribution network as one that meets the stated delivery expectations at the lowest cost. Comprehensive designs use sophisticated models. These tools aid in determining the ‘sweet spot’ solution (number, location, role, and size of facilities — manufacturing, DCs, cross-docks, etc., transportation modes, inventory deployment, and policies) by balancing the trade-offs between customer service requirements, assets and costs. Most models stop short at minimizing costs. However, now logistics professionals are using modeling to plan and optimize networks to maximize profit, speed to customer, sustainability, and/or other strategic goals. Minimizing is no longer sufficient. The secret sauce is designing the perfect network to optimize profitable growth.”

Supply chains are getting more complex and anyone familiar with networks knows complexity only increases as the number of stakeholders (aka nodes) grows. Nevertheless, SCB analysts rightfully insist all stakeholders must be involved in order to design the perfect logistics network. They also assert, to develop the perfect network, logistics organizations need to follow these critical steps:

• Define requirements and network alternatives
• Identify current and future service demands
• Capture transportation, distribution, and service costs
• Identify alternative networks
• Determine value-ranking criteria
• Select candidate networks to model

Clearly, designing the perfect logistics network is not a “paper” project. Organizations need a sophisticated cognitive computing platform capable of handling the numerous variables involved in a complex model. Organizations also need to ensure they have the right connections and access to the right data. SCB analysts note, “The data is critical, especially when combined with critical thinking of all the implications of each scenario.”

Leveraging cognitive technology in pursuit of logistics perfection

The Forbes Insights Team notes, “Forbes Insights research shows that 65% of senior transportation-focused executives believe logistics, supply chain and transportation processes are in the midst of a renaissance — an era of profound transformation. But of the most visible forces of change, perhaps none carries more potential for innovation and even disruption than the evolution of artificial intelligence (AI), machine learning (ML) and related technologies.”[4] Logistics is well situated to enter the digital age and embrace digitization. The Forbes team explains, “Decades ago, trucking, rail and sea cargo began being tracked by satellite via telematics, and versions of electronic driver logs have been around for nearly 20 years. The industry has also for many years now applied high-level decision theory to optimize the costs and transit times associated with high-value vehicles and often even higher-value cargoes. The difference today, however, is not only more data but also vastly more powerful computing power and algorithms to sort, evaluate and accelerate understanding and action.” The team offers three examples of how cognitive technology can advance the pursuit of perfection. They are:

• Augmented real-time decision making: “Logistics teams often handle a wide range of complex but repeatable tasks that require large amounts of input data in order to make the best choices. Optimal carrier selection, for example, means combing through thousands of possible candidates, routes and schedules. In practice, workers often require 10 minutes or more to gather the needed information. But with AI and associated tools, supply chain professionals can automate the analysis and narrow their selections to just two or three within a matter of seconds. Human intuition then closes the deal.”
• Predictive analysis: “When will customers be ready to order? Of course the sales team wants to know, but this is also vital information for logistics, supply chain and transportation planning — an example where an AI platform could collaborate closely with sales and marketing. Looking specifically at transportation needs, telematics/IoT can help determine when a vehicle might need preventative maintenance, thus avoiding breakdowns and reducing the risk of failing to meet customer needs and expectations.”
• Strategic optimization: “Where, when and how? Leaders in these disciplines are learning how to gather and comb information to make the best decisions regarding the deployment of not only inventories but also the transportation assets needed to connect all the dots from origin to customer location. Where are the drivers? Where are the vehicles? What commitments have been made? Where are the customers? These and related variables can be fed to AI and machine learning engines that can crunch the data and then present a range of scenarios for optimization. With sophisticated tools that continuously learn and improve, industry professionals are able to make better, up-to-the-minute decisions as well as more informed longer-term, strategic choices, such as warehouse locations, fleet size/specifications, etc.”

Perfection in logistics remains aspirational — excellence is not only achievable it’s expected.

Footnotes
[1] Staff, “Is Technology the Answer to Logistics Perfection?” Apptricity Blog, 25 November 2014.
[2] Staff, “Six Steps to a Perfect Logistics Network,” SupplyChainBrain, 24 March 2017.
[3] Daniel Gagnon, “4 Myths About Innovation in Logistics,” Longitudes, 13 August 2017.
[4] Forbes Insights Team, “How Artificial Intelligence And Machine Learning Are Revolutionizing Logistics, Supply Chain And Transportation,” Forbes, 4 September 2018.