In postwar Japan, Taiichi Ohno was tasked with rebuilding the Japanese car market from scratch. Known as the father of the Japanese Toyota Production System (TPS), Ohno inherited limited raw materials in a fledgling automotive market—the behemoth Fordesque assembly lines employed in the U.S. were too sprawling for Japan’s compact approach.

Kaizen & Kanban

Rewind to the 1980s when much of the world experienced a Kaizen craze—the modern way to manufacture, ship, and manage employees. Kaizen is a Japanese term meaning “change for the better or continuous improvement,” yet more in the spirit of “every day, everyone, everywhere”—a true companywide continuous effort. Kaizen involves making the work environment more efficient and effective by creating a team atmosphere, improving everyday procedures, ensuring employee engagement, and making jobs more fulfilling, less tiring, and safer. And to paraphrase Ohno, “If you improve a product, see if you can improve that product further until it can no longer be improved.”

Kanban (Just-in-Time) is an inventory control system that began with using various colored cards which prompted visual cues for employees. If an employee is boxing product from a conveyor belt, a kanban is placed near the bottom of the stack of boxes. When the worker pulls out the kanban, he gives the floor runner (or perhaps a programmed robot) a sign or command to restock more boxes. Kanbans are typically placed in multiple stations to act as “cue cards” for restocking. The kanbans ensure product is continually boxed at the end of the line.

After TPS took hold, it wasn’t long before automakers around the world began emulating Ohno’s system which offered less inventory, less material waste, and the elimination of nonproductive tasks while reassigning new ones. TPS employees cross-trained at multiple stations on the assembly floor while management welcomed their innovative suggestions, especially from assembly-line workers who had hands-on experience with the various manufacturing processes of the automobiles as well as what bottlenecks to eliminate. Once TPS (and its hybrids) spread to Western countries, TPS became Just-in-Time (JIT), yet many TPS processes such as inventory already leaned heavily on JIT strategies. The acronym stuck in Western business culture, and from 1977 to present day, JIT has remained one of the most popular manufacturing/warehousing inventory systems in the world thanks in part to Henry Ford’s economical assembly line in the early 1900s, and Taiichi Ohno’s approach to kaizen in the mid-1900s.

In 2021 and 2022, JIT took its share of hits. Offering little in the way of inventory, JIT customers often waited several months—even a year or longer—for products due to raw material shortages and supply chain chaos. Add port lockdowns, seaport union strikes, and onerous state and federal regulations, and JITs had to adjust to more of a Just-in-case (JIC) system or a hybrid thereof. Granted, there was no fail-safe transportation strategy from late 2020 to the end of 2023, and the forecast for transportation in 2024 wends down a similar path.

Just-in-Case (JIC)

The JIC model provides manufacturers and distributors with enough inventory of products to keep customers on schedule from several weeks to several months, depending on how much raw material they can purchase in advance. While not immune to worldwide supply chain gridlock, JIC shipping may continue to flourish in the U.S intra- and interstate-wide supply chain—Mexico and Canada are America’s number one and two trading partner respectively, so logistically the U.S. likely avoids driving off the transportation cliff, as steep as it may be. JIC also allows a variance of cash flow—it doesn’t tie up a year or two worth of capital in inventory.

What are the Advantages of Using a JIT Strategy?

JIT allows companies to manufacture and ship at a high level without too much capital sitting dormant on warehouse shelves. JIT thrives in a competitive free market, is cost and waste conscious, and runs like clockwork when world supply chains are fluid while keeping inventory at a bare minimum. Also, warehousing costs are lower due to less inventory.

What are the Disadvantages of Using a JIT Strategy?

A chaotic supply chain and lack of raw materials can wreck JIT unless one has a niche industry where supplies and shipping are readily available. When supply chains hit gridlock, JITs must adapt and find new ways to ship or receive products—they are built for times when raw materials are available, and transportation is fluid.

What are the Advantages of Using a JIC Strategy?

As alluded to earlier, JIC inventory is advantageous during supply chain chaos, and when raw materials are in short supply, or when prices are inflated—you have inventory from which to draw.

What are the Disadvantages of Using a JIC Strategy?

When demand slows, supplies may be wasted in the JIC model as companies using this strategy have funding tied up in inventory. While obvious advantages are inherent to a JIC strategy, the strategy is less effective when incoming orders drop off.

So, Which Do We Use in An Ever-changing Market?

The decision between JIT and JIC depends on market conditions, supply chain reliability, and the availability of the raw materials. In the simplest of terms, inventory strategies largely depend on the current transportation environment—how fast can we get our raw materials? Or how fast will our customers get our products? These questions are all dependent on whether there’s enough raw material supply to meet demand.

Ultimately, learning how to be flexible in volatile economies and supply chains may require complex algorithms extracted from the data kerneled in your Excel spreadsheets to avoid feast or famine operating trends and return manufacturing, purchasing, and shipping departments to some semblance of sanity. Python programming is reported to work well for automating analytics, but it may be cost prohibitive for smaller companies. That is, unless you combine the new school with the old.

Smaller companies looking for better supply chain visibility would likely have to hire a programmer or a supply chain quant to input the spreadsheet and convert that data to a database to expand analytical capabilities. Or, companies could download Python analytics into Excel, e.g., Microsoft 365 Python in Excel. In the familiar landscape of Excel, Python’s machine-learning language capabilities can offer predictive analytics for visualizations—computer algorithms proven to provide greater supply chain visibility. The bottom line is how many pallets of product do you expect to sell each month, and how many should you stock . . . just in case.

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