Companies who operate in the rapidly digitizing supply chain need game-changing technology that will make their operations more efficient, faster, and smarter.
The pace of change in freight is heating up. A renewed wave of competition — perhaps the most profound since trucking was deregulated in 1980 — is forcing companies in the space to learn, pivot, and adapt in order to survive.
In our view, competitive pressure is coming from two sources: an incredible amount of venture capital and private equity interest in transportation and logistics, and new supply chain strategies from legacy incumbents that threaten to change the rules of the game. Venture capital investment in freight technology startups hockey-sticked by 24x from 2014 to 2018, and so far this year, the pace of investment has doubled again. Meanwhile, Amazon stealthily took its digital freight brokerage platform live, and there are rumors that a certain very large Arkansas-based retailer will soon follow suit.
“Supply chain companies whose IT spend was historically focused on the maintenance of on-site systems and software will now need to make strategic investments and pivot to being technology-first organizations,” said Chris Kirchner, chief executive officer at Slync. Slync is a freight tech startup providing a cloud-based, AI-assisted integration layer that pools data from supply chain partners, enabling multi-party visibility and automated exception management.
Venture-funded startups in the space include visibility solution providers, data aggregators, matching algorithms, blockchain-based document processors, sensor companies, and autonomous vehicle — whether drone, truck, ship, or warehouse robot — builders.
Yet the paradox of the supply chain is that so much innovation coexists with truly archaic legacy incumbents. A leading global freight forwarder employs whole office floors of workers handling slips of paper like a Victorian-era counting house. Too many bills-of-lading are transmitted by fax machine, or worse, by post. And slow, error-prone human decision-making based on a vague sense of historical data — ‘experience’ — dominates nearly every company in the industry.
Legacy enterprise resource planning (ERP) systems focused on realizing improvements in a few core areas of the supply chain business function: streamlining transactions, warehouse management, and pushing decision-making to be increasing data-driven.
A recent report by McKinsey titled “Digital transformation: raising supply chain performance to new levels” addressed the shortcomings of legacy ERPs.
“What these technologies didn’t yet provide, though, were transformative capabilities for supply-chain management: linking and combining cross-functional data (for example, inventory, shipments, and schedules) from internal and external sources; uncovering the origins of performance problems by delving into ERP, warehouse-management, advance-planning, and other systems all at once; or forecasting demand and performance with advanced analytics, so planning can become more precise and problems can be anticipated and prevented,” McKinsey analysts Enis Gezgin, Xin Huang, Prakash Samal, and Ildefonso Silva found.
The first step in achieving the digital transformation of the supply chain is tearing down the silos, both within organizations and between organizations, that have isolated a lot of very useful data. Modern, cloud-based enterprise software makes that step easier and less expensive than ever.
The next step is to deploy advanced analytics to leverage that data for the benefit of all collaborators in a particular supply chain. Those analytics can include data science, machine learning, and artificial intelligence. A simple way to think about the difference between those three categories is this: data science produces insights (eight percent of shipments to this customer are late); machine learning produces predictions (because Port of Los Angeles dwell times are rising x amount, this shipment will be late); artificial intelligence produces actions (automatically emailing the customer with a revised estimated time of arrival).
Slync’s platform does just that, allowing multiple parties and multiple business units of an organization to evaluate root causes of inefficiencies. Best-in-class artificial intelligence generates recommendations to several organizations based on analysis of un-siloed, horizontal data sets that cut across companies.
“Customer forecasts for product demand are updated continuously as sales, pricing, business intelligence, and macroeconomic data comes in,” Kirchner said. “Why shouldn’t their suppliers’ manufacturing facilities and distribution centers make shipping decisions based on the same data, generated at the same cadence? Slync allows partners to push demand forecasts all the way upstream, blurring the lines between inventory management and procurement and eliminating friction in the supply chain.”
The McKinsey analysts divided potential investments in supply chain digital transformation into three categories: no regrets, forced bet, and active waiting. The definition of ‘no regrets’ is “clear financial or operational benefits, technology exists, and practical to make right away.”
“Slync’s platform has proven to be a ‘no regrets’ solution for our customers,” Kirchner told FreightWaves. “Our technology generates value for our customers within weeks of implementation.”
