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Optimizing vs. Maximizing: Obtaining a Bird's-Eye View Requires Digital Transformation


I recently joined IDC as an analyst covering global supply chain execution and fulfillment strategies, and began by reporting on the difference between maximization and optimization in the end-to-end supply chain: how it’s important to move from the former to the latter, how optimization can only be achieved by viewing the whole ecosystem, and how difficult this has been to do historically. It also explores how a digital transformation (DX) and the promise of interconnectivity and visibility it offers, as well as the intelligence it enables when paired with analytics or AI, make the optimization of large, multi-enterprise, multi-country, multi-industry business networks a viable – and necessary – goal.

My view into the supply chain is informed primarily by my research and writing as editor in chief of Apparel magazine, where I spent the previous 24 years and where I witnessed the evolution of the industry's concept-to-consumer supply chain and the impact of those changes on retailers, brands, manufacturers and other suppliers and providers, not to mention how many of those previously discrete entities moved into each other's spaces, blurring the lines among the various players.

Supply chains in apparel, as in all industries, are comprised of many moving parts, people, places, and systems around the globe. All of these are continually changing and evolving based on a virtually endless number of factors, ranging from raw material and labor costs in various locales to the costs involved in designing, planning for, producing, and moving those products – all of which, in turn, are based on other factors, such as production, trade agreements and tariffs, in-country wages, politics, security risks, and road conditions. Enterprises must select factories by evaluating their quality, capacity, skillsets, sustainability, social compliance, and surrounding infrastructure, and review these factors on an ongoing basis. Ditto for freight and logistics companies. Equipment must be fixed, repaired and replaced. Shop floors and warehouses must be reengineered, repurposed, expanded. Some enterprises own their own factories; some don't; many manage in a hybrid environment.

Everything must be moved from here to there. Raw materials and components must be moved to various factories around the world. Finished products must be moved to warehouses, seaports, or airports, crossing land and ocean. They must pass through customs and be moved to holding warehouses or DCs, or else shipped directly to retailers or consumers, via a wide variety of modes of transportation, including less-than-truckload (LTL), full truckload (FTL), consolidation, and small-package shipping. Goods must be tracked in bulk as they move through the supply chain and, ideally, with the advent of e-commerce, on an item-level basis. To keep inventories as low, quickly turning, and speedy as possible, to delight the customer while remaining profitable, companies require one view of visibility across their supply chains to know where the product is, when and where will it be available, and how it can be moved to each customer in the most optimal way.

This doesn't even begin to cover the intricacy of details that comprise the creation and movement of goods through a supply chain. Further, as supply chains have grown more global, as personalization and SKU counts have proliferated, and as e-commerce and smartphones have completely changed the way the world shops, the amount of data available to process, track, and analyze has grown exponentially. The entire digital universe is expected to reach 44 zettabytes by the end of 2020, which will mean there are 40 times more bytes than there are stars in the observable universe. (A zettabyte is 1,0007 bytes).

Because the supply chain is vast and, until recently, because no one party or group could have a real-time view into its entirety, it was only natural for each unit to maximize its own operations and functions; in other words, the "whole" that comprised its own particular universe, whether the warehouse, store, or other segment of the supply chain. Maximization of a part versus optimization of the whole occurs for a wide variety of reasons: it requires the consideration of far fewer components; because of the narrow boundaries of its space, it's more efficient; and it's often the natural outcome of incentives that measure results only in that narrow space rather than that align with the whole enterprise. A warehouse worker whose bonus hinges on the number of orders packaged per day will likely reach for the closest box versus the one that most closely matches the size of the product being shipped. That adds up to a lot of expensive shipped air, and more transportation, gasoline, and packaging to handle more boxes. It is maximization working at cross purposes to the optimization of the whole enterprise.

Lack of visibility isn't even the only problem, to be honest. We often see maximization where there's clear visibility into how a practice offers no benefits and actually detracts from the optimal operation of the whole.

Consider a traditional batch-run sewing line at an apparel factory where each employee is in charge of one single operation. One person sews a placket onto a shirt front, another the sleeves, and yet another the shoulder tape. This maximizes the efficiency of each station and, depending on your labor resources, due dates, and size of operation, it may be a smart way to design your production lines. But it can also create a scenario where partially sewn apparel parts stack up at one workstation at a much faster rate than at another. At the end of the shift, there is a lot of work in progress but not much finished product due to the bottleneck. This is an example of sacrificing the optimization of the whole to the maximization of a part. The more advantageous approach would be to train the employee with the skyscraper stack of shirtfronts to also handle the operation where progress is slowing.

Now imagine this type of bottlenecking across the supply chain. Boxes ready to ship are piling up on the loading dock, but there's a delay with the freight company's trucks. Or perhaps an item has started selling like hotcakes on the internet, while another languishes. You're maximizing your warehouse efficiency by moving products out in the sequence of the orders when you should be putting the hot item into the truck ahead of the slow movers (and maybe stopping production on the latter, more quickly than you'd planned). But first you need the visibility or connectivity to make changes that would optimize the whole of your business rather than maximizing a predetermined flow of warehouse operations.

Now imagine that instead of these functions operating in their own segregated universes, you have visibility into all of them at once. You can see what is moving, to which locations, and where problems or opportunities have arisen to address them in real-time. This is the promise and outcome of advanced digitalization, where data is no longer sequestered in individual software systems that do not integrate well and do not share one version of data.

In the modern economy, the ability to optimize relies on the quick, seamless, and transparent data sharing across the multi-enterprise network, and the ability to understand it and act upon it. Most companies have not achieved this. According to IDC's 2019 Digital Transformation Leaders Survey, on a spectrum ranging from tactical digital transformation (DX) initiatives that are disconnected from enterprise strategy to a long-term investment plan with an enterprise strategy to use DX to transform markets and customers by creating new business models, product, and service experiences, 11.5% of manufacturers and 9.2% of retailers identify themselves as in the former stage, while 9.9% of manufacturers and 12.1% of retailers identity themselves as at the latter, with the remainder of respondents spread fairly equally among various stages of DX advancement between those two extremes.

Without continued, quick progress toward advanced DX across the end-to-end supply chain, today's businesses will not be able to carry on. Indeed, by 2025, IDC predicts that 25% of the retail companies with an IDC Digital Index of less than 100 in 2020 will close all stores. Companies that survive will be moving toward developing the resilience and agility that DX enables, with half of all manufacturing supply chains expected to have invested in supply chain resiliency and artificial intelligence by the end of 2021, resulting in productivity improvements of 15%.

What does that look like? Today's technology platforms and cloud architecture enable quick, integrated, globally connected and transparent supply chains. Generally speaking, companies are not operating on one system across their ecosystems from design ideation to customer fulfillment, and it's unlikely that all needs can ever be fulfilled by one system. But increasingly, the scope, transparency, and ability of systems to integrate quickly and effectively with each other is growing, enabling visibility and quick decision making.

One example that comes with its own great, built-in metaphor is supply chain Control Tower systems. Imagine trying to orchestrate thousands of flights coming in and out of airports without a view into where all planes are at all times. That would be impossible, and disastrous. Similarly, businesses handling logistics operations without real-time visibility into increasingly fragmented, multi-enterprise, multi-node, multi-carrier, multi-warehouse movements, will lack speed and efficiency and encounter costly delays and disruptions. Like air traffic controllers getting the bird's eye view from above, businesses working from a supply chain Control Tower can manage all movements from one central location to make real-time decisions and changes when unforeseen problems arise, such as, for example, a carrier breakdown or delay. That ensures that the whole of the end-to-end supply chain is not held hostage by a single problem.

To thrive, all businesses require the ability to gain this type of bird's eye view into the individual "wholes" and ideally the entire whole of their end-to-end supply chain. Visibility is the first step toward optimization. That's only possible with a digitally transformed enterprise with insight into the real-time activities across it and the ability to harness the data it generates to improve business models and provide better customer experiences.



Jordan.K.Speer-1Jordan K. Speer is a Research Manager, Global Supply Chain, at IDC. Her work focuses on retailers’ and manufacturers' initiatives, best practices, trends, market conditions, and the competitive market landscape for supply chain execution systems. Her upcoming work in the beginning of 2020 will include reports on end-to-end fulfillment, returns management and reverse logistics, AI in transportation management, PLM, and recycling and sustainability in the supply chain. At her previous position as Editor in Chief of Apparel, Jordan’s work included such highly anticipated annual reports as Technology Trends, the Apparel Top Innovators, the Apparel Top 50, and the Digital Store Report.


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