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What is the Key to Perfect Supply Chain Orchestration? (Has Anyone Figured It Out?)
Some say that technology is set to significantly disrupt the current warehouse and transportation industry, creating a challenging landscape to navigate. However, we say that technology is what will enable supply chain organizations to avoid operational disruptions and capture a performance edge while providing amazing experiences to customers.
While many companies should be celebrating the fact that global e-commerce spending continues to grow, the reality is that most are scrambling to figure out how to successfully manage the increased shipment volumes of parcel, package and full pallet loads largely driven by omnichannel orders and subsequent inventory replenishment demands by customers and trading partners. Using the tools of the past, demand is becoming harder to predict, in part due to shifting geopolitical dynamics and environmental uncertainties. But also because of consumers’ fluid expectations – the increasing expectation that one can get what they want, when they want it and how they want it delivered to them.
At the same time, supply chain organizations are searching for sustainable ways to appease customers who say a 3-4-day delivery window is no longer “good enough.” Fast fulfillment is not just retailers’ burden to bear. Manufacturers, warehouse and distribution center (DC) operators and transport and logistics (T&L) companies are actually the ones who ultimately determine whether or not the accelerated delivery times requested of customers are feasible.
If a consumer packaged goods (CPG) manufacturer can’t get inventory for high-demand SKUs to the right place at the right time, then warehouse operators – and ultimately retailers – will find themselves facing frequent out-of-stocks or “backordered” notifications, neither of which go over well with customers who don’t even want to wait two hours for something, much less two days or more. And if warehouse workers can’t pick and pack incoming orders in essentially real-time then they increase their chances of missing shippers’ deadlines. Couriers can’t wait when warehouse or DC fulfillment actions are delayed as they – like retailers – are the face of “fast shipping” and often share the blame when goods don’t arrive on time.
But speed isn’t the only mandate of the on-demand economy.
Society, as a whole, has come to expect a hyper level of personalization. Manufacturers’ SKUs are multiplying almost as fast as order volumes are increasing due to the level of custom product configurability demanded by customers. Subsequently warehouse operators are struggling to expand shelf space quick enough to handle the expanding inventory requirements. In fact, 87 percent of decision makers surveyed in Zebra’s latest Warehousing Vision Study are in the process of or planning to expand the size of their warehouses by 2024. Eight two (82) percent also anticipate an increase in the number of warehousing facilities during this same time period.
Such physical expansion will only offer so much relief, though. In fact, it could actually contribute to some of companies’ top issues today, particularly widespread labor shortages.
So, which problem do you solve for first?
That’s a very personal question. Although, we believe (because we have seen it firsthand) that it’s quite possible to overcome many of the challenges just listed by addressing and focusing on one core problem: inefficient information management and under-utilization of enterprise intelligence across workflows.
Smart Supply Chains Start with Intelligent Enterprises
As the physical footprint of your manufacturing, warehousing or distribution operation increases, you will find that exponentially more data must be captured, analyzed and re-distributed to the edge of what is already a widely distributed enterprise. Therefore, you must expand your digital footprint to reach the broadening mobile edge. That is the only way to know what is happening across the supply chain at that moment and what needs to happen next in order to execute end-to-end fulfillment operations with complete precision.
However, the approach you need to take to digitalization today is quite different than what may have been sufficient even five years ago.
For decades, supply chain organizations (including retailers) relied on what we call “systems of record” to inform their decisions and guide workers’ actions. Digital innovation started at the enterprise core, with ERP systems that addressed many challenges and inefficiencies. For example, manufacturers were able to track the movement of inventory and raw materials. Centralized computing systems ran monolithic software packages, handling large amounts of information on static databases that formed these business-critical “systems of record.” Although very little of this was achieved with real-time information or contextual data, at that time it was considered breakthrough. And while that type of digitization is critical – it’s no longer enough to meet ever-rising customer expectations and to compete in the on-demand economy.
Supply chains now have incredible amounts of information available at their fingertips thanks to the prolific use of handheld mobile computers, tablets, scanners, sensors, RFID and other data-capture tools at the edge of the enterprise. However, this information is highly under-utilized – mainly because it is either not actively collected and correlated or it is simply dormant data – collected to be used at some point in the future.
Even though a great deal of enterprise intelligence is technically accessible, it is often up to (very busy) workers to extract and reconcile relevant insights from “reports” or, worse yet, colossal amounts of raw data in order to recommend the best course of action. The problem is that those workers at the edge of your operations – where real work is getting done – don’t have minutes to spare, much less the hours, days or even weeks required for such a time-intensive exercise. Even data scientists who spend their days solely focused on analytics can’t parse through it all fast enough. So, much of the data is shelved.
Analytics solutions can help with this, assuming they are prescriptive in nature. However, analytics solutions can only predict what may happen or prescribe what you should do next if they have a complete picture of what is happening right now, as well as solid historical details.
That is why every supply chain organization must prioritize the end-to-end orchestration of systems, devices and information from order to delivery, spanning cross-functional and geographical boundaries.
We believe that transforming current “systems of record” into “systems of reality” is the only way to attain the level of enterprise intelligence you need to improve the speed and effectiveness of every workflow and create a perfectly-orchestrated supply chain that can achieve frictionless fulfillment from the factory to customers’ front door (or your store).
Simple enough, right?
Joking aside, the truth is that you may be further along on the path to becoming an intelligent enterprise than you may realize.
There are ways to incrementally adapt or scale your technology systems to increase your enterprise intelligence and support the dramatic market growth that’s anticipated in the next several years, whether your goal is to achieve a fully-automated factory in the next couple of years or just leverage partial augmentation to help improve working conditions and attract/retain talent in the warehouse.
I encourage you to reach out to my team to discuss which combination of automation, connected IoT and mobile devices, software and data-driven solutions may best support your organization’s goals so that we can help you define and execute a more personalized modernization strategy.
Tom Bianculli
Tom Bianculli serves as the Chief Technology Officer of Zebra Technologies. In this role, he is responsible for the exploration of emerging opportunities, coordinating with product teams on advanced product development and Internet of Things (IoT) initiatives. The Chief Technology Office is comprised of engineering, business, customer research and design functions.
Tom began his career in the tech industry at Symbol Technologies, Inc. (later acquired by Motorola) in 1994 as part of the data capture solutions business. In the following years, he held positions of increased responsibility including architectural and director of engineering roles.
Tom has been granted over 20 U.S. patents and is a Zebra Distinguished Innovator and Science Advisory Board associate. He was recently named one of the Top 100 Leaders in Technology 2021 by Technology Magazine.
Tom holds bachelor of science and master of science degrees in electrical engineering from Polytechnic University, NYU and serves on the board of directors for the School of Engineering at the New York Institute of Technology.
