The Effect of Lean Inventory and Policies during COVID-19

Stock serves as a buffer against volatility in demand (Waters, 2003). There is no magic distribution network that can diminish the effect of volatility the way stock on a shelf can; and, it should be noted, stock is expensive to keep on those shelves. This strategic juxtaposition began to be evaluated earnestly in the latter portion of the 20th century by the Toyota Motor Corporation in Japan (Lander & Liker, 2007). Their success in eliminating waste through systematic evaluation, in turn, was quickly taken and developed by other large organizations; however, it being easier to create an efficient Logistics network, rather than changing an existing, lethargic network, new companies in the 1990s found success with Lean inventory systems. The most famous of these systems, Just-in-Time (JIT), is a distribution model that was ultimately developed by Toyota and later adopted – and, optimized for maximum revenue – by Amazon (Carlson, 2019).

???????????As Amazon unleashed, optimized and utilized JIT to become the second leading retailer in the world (as of 2021, with international revenue just shy of $75B) (National Retail Federation, 2021), JIT concepts began to be ‘copy-and-pasted’ as other retailers attempted to compete/catch-up. What many of those retailers did not understand was that they could not simply adopt such a methodology without the capability to manage data in real-time. Transaction Processing Systems (TPS), Management Information Systems (MIS), and Enterprise Resource Planning (ERP) functions all needed to be supercharged, as well as aligned and working under the same protocols. The resulting lack of stock, and the lack of agility (through technology) to quickly direct stock within the network to counter volatility was the fear; but, no event challenged these weak defenses until COVID-19.

Background

???????????The development of the Toyota Production System began following World War II by Taiichi Ohno and Eiji Toyoda (Shmula, 2017). Ultimately, the concept centered around eliminating waste, reducing burden of work, and balancing/right-sizing workloads to facilitate a system that operated under the greatest possible efficiency – or, a Lean system. The originators identified three categories of potential improvement: muda (waste), mura (burden), and muri (unevenness) – each encapsulating one of the prioritized areas of focus (MudaMasters, 2013).

Muda, Mura, Muri. The originators of the Toyota Production System were interested in cultural cures to waste, as well as tangible, practical methodologies to save money. So, while right-sizing the workforce and balancing workload (mura and muri, respectfully) are key opportunities for efficiency in any organization, certain variables exists when discussing material waste. In fact, one of the core concepts of Ohno and Toyoda was the JIT manufacturing process; however, a key difference between the Amazon distribution methodology and the Toyota Production System at its onset is that the Toyota system served internal customers (Toyota Motor Company production workers, assemblers, etc.), as opposed to the external customers Amazon is expected to serve. While Toyota, of course, only puts their vehicles (a generally high unit cost, low demand good), the qualitative aspects of a shortage cost were never considered by Ohno and Toyoda – there would be no reason for it, as all of their customers were internal to the Company.

Methodology

Advent of Stock/Inventory Optimization. Conceptually (and, continuing with Amazon as the contemporary example for JIT distribution), Amzaon did not need to reach far to understand that lower inventory levels result in lower holding costs (Waters, 2003). However, what Amazon could not do immediately was understand the demand of their customers for the width of their current supply chain. In fact, Amazon began selling one type of item: books. While there are many different types of books, it is apparent that the information gathering technology and management systems operated by Amazon (now, “AWS”) were honed during this time. Relying upon that demand data to conduct linear and multi-linear regression testing for cause and effect forecasting, as well as trend smoothing over times of greater volatility (Murphy Jr. & Knemeyer, 2018), allowed Amazon to accurately dictate where their stock should be placed (e.g. which warehouses, during which times of the year, and when to re-order from wholesalers and suppliers), and how much to have on hand to sustain the variability of demand. Amazon provided the blueprint for success; and, nearly every major company in the world took note and saw some value in moving toward Lean initiatives (to include Lean material management). As a result, the world’s retail markets were ill-prepared for the volatility generated by COVID-19.

Case Study: The “Aisin Fire”. Generally seen as a success-in-the-face-of-adversity story for the Toyota Production System, the Aisin fire occurred in February, 1997 at one of Aisin’s production facilities (Nishiguchi & Beaudet, 1998). This facility crafted the entirety of Toyota Motor Company’s proportional valves (“P-valve”); a part that is necessary in any, and every Toyota vehicle. Additionally, production was running full-tilt and resources had been expended by Toyota to push production to the greatest extent possible. Remarkably, and with great effort by Toyota and their supply chain partners, P-valves began to be manufactured by other facilities and companies; production was only affected for a few days (Nishiguchi & Beaudet, 1998).

However, the Aisin fire also serves as a case study for catastrophic events that effect a supply chain (notwithstanding the risk of using a sole supplier for a keystone part/component), such as what has occurred during COVID-19; if Toyota had kept a stock of P-valves – as it is such a critical part – production could have continued while the stakeholders found a solution. Specifically, with the resources Toyota had expended (cost) to increase production past normal levels, the shortage cost would have been significant. With the buffer of stock on the shelf, that shortage cost would not have been necessary (Waters, 2003); and would have assuredly been higher than the holding cost for a nominal amount of P-valves (e.g. amount for 1-week of full/overtime production).

COVID-19. Much like the Aisin fire, COVID-19 disrupted production; of course, unlike the Aisin fire which affected markets vertically (by industry), the pandemic affected global markets horizontally (by location). Certain months the case load of the disease was lower in particular regions; while other months yielded higher numbers (relative to other geographic areas) in those same areas. However, regardless of how COVID-19 affected markets, the thing the pandemic generated more than anything is/was volatility. Volatility creates stress, stress causes failure at points in the supply chain that do not have the stock as a buffer against that stress. This concept is not limited to stock on the shelf, but other resources as well.

As an example of this concept seen outside of JIT or Lean material and distribution management is the stress that trade infrastructure has felt as a result of low performance due to safety concerns regarding the virus. Specifically, the Port of Los Angeles is traditionally a poor performing port, recently ranking 328th out of 351 global ports (Reuters, 2021). However, even with performance at low levels, the organization could still keep shipping containers transiting the U.S. and container ships moving to their next destination; all on 12-hour/day operations. Unsurprisingly, COVID-19 wrecked any semblance of trade capability by the Port – i.e. less workers than before, with less touch labor writ large, and expectations for service unwavering.?There was no buffer, even the world’s best ports became clogged and inefficient; however, when volatility hits an already stressed area of a supply chain, the effect is catastrophic. With regard to the example, the volatility provided by COVID-19 to the Port of L.A. resulted in the shipping container shortage at the heart of the present global supply chain issues.

Findings

???????????Resting on the concept and theory that extreme/radical Lean management of a supply chain can produce catastrophic failure when volatility is introduced at the strategic level, one can look at the following areas as lessons-learned during the COVID-19 pandemic:

1.????Stock is a Necessary Evil

2.????What Should Optimization Should Focus On?

3.????Just-in-Time vs. Just-in-Case

The findings support the basic human decision cycle: caution-weariness-risk-reward/reject; and, that cycle is funded through times of stability and invariability in demand – certainly nothing to the effect that COVID-19 manifested.

???????????Stock is a Buffer against Demand. It costs money to hold stock; and, unlike many calculations in Logistics management, this concept is based on a linear equation: the more stock you hold, the higher the cost (on a general, linear scale). The spectrum then is juxtaposed against the relative safety/resilience requirements of the supply chain; the more money spent on stock directly results in a higher service level to internal or external customers (Simchi-Levi, Simchi-Levi, & Kaminsky, 2008). This concept is not a secret; but, it has been exploited by organizations hoping to achieve an Amazon-ian level of success in terms of revenue and profit. Unfortunately, the corresponding truth to this effort is that removing stock from systems that are vital to security and safety interests endangers societies at large. As an example, NATO has stated that COVID-19 affected them greatly because they adopted a JIT distribution model, and did not have the stock to effectively sustain their normal operations (Lundquist, 2021). Such a decision, if additional stress (e.g. a regional conflict) were introduced, could lead to the inability of NATO to execute the function of their system – protecting NATO allies from harm.

???????????The Many Faces of Optimization. Since the arrival of JIT in the United States, revenue has been at the heart of why organizations introduce Lean (and Leaner) concepts into their systems. In their mind, by eliminating waste, their system grows stronger as a result. Unfortunately, waste is defined differently industry-to-industry and the service level required to be met is categorically different as well. So, why are companies optimizing only for revenue? Logic points to the fact that the Lean concepts deployed by U.S. companies in the early 2000s that were derived from Amazon and the core of the Toyota Production System. Amazon – after years of honing the craft of data collection and analysis – were at the point of being able to rely upon historical data to determine volatility, thereby allowing them to reduce stock and optimize “holding cost” for maximum revenue.

???????????Of course, there are systemic problems with applying a Lean optimization theory like peanut butter over every industry, company or system in operation. As noted, Amazon was in the position to predict volatility; not every system has the resources to collect and analyze that amount of data – so, without the capacity to predict volatility, the optimization is generally a “best guess”. Additionally, not every system can afford the shortage cost that Amazon can absorb. Further, some systems (such as NATO) should not focus at all on Lean theory and should instead bolster their supply chains with stock, making them more Resilient to volatility. The priorities and failure tolerance of the system should drive what type of theory to employ (e.g. Resilient-Lean spectrum). While Amazon has the capacity to absorb shortage cost occasionally, systems like NATO cannot afford to hold the risk associated with Lean theories and policy.

???????????Lesson Learned: Moving from Just-in-Time to Just-in-Case. Undoubtedly, the United States (and the World) will grow to understand the importance of optimizing supply chains based on the Resilient-Lean spectrum mentioned earlier, as well as the nominal tolerance for failure. Moreover, business leaders and decision makers within systems optimizing for revenue should understand that predicting the future is not an easy, or perfect solution. Even when spending resources to create a tool such as Amazon Warehouse Services (AWS) that allows data to be autonomously captured and analyzed to predict future events, volatility will still be present. It cannot be avoided; and so, those with equity in a given system must base their risk-tolerance on the Resilient-Lean spectrum and make policy and inventory management decisions based on that area of risk. Moving from the extremes of bastardized JIT distribution to the Resilient-Lean spectrum will allow optimization to occur inherently through priority-aligned decision making.

Analysis

???????????It is undeniable that the world is becoming interconnected in ways that have never been seen before. The introduction of the Internet, digital networks, file sharing and virtual collaboration allow for cross-functional opportunities to be identified and capitalized. There is great benefit here; but, often the cost of connecting, collaborating and creating is overlooked. The cost, beyond the normal cost of system operation, is also found in dependency on an external partner. While trust must be the most prized attribute of any partner, there is no guarantee that a partner will base their decision making in the same area on the Resilient-Lean spectrum. The dependency (cost) that is required to generate the benefit of connection, can lead to situations like the Aisin fire and COVID-19 Logistics catastrophes. And, while the world (vis-à-vis COVID-19) may not have realized the dependency required to keep global supply chains operable before the pandemic, an evaluation of the resiliency of those strategic systems (e.g. food, medicine, building materials, electronic components, etc.) is certainly required ex post facto.

???????????In the same fashion as the United States military keeps war-reserve material (WRM) on hand, maintained and ready to go, so too should critical supply chains be required to promote stock at stress points in their systems. COVID-19 has shown the world that the interdependency of its inhabitants on one another requires oversight and regulation in these key areas. Utilizing NATO as the case study, it is quite easy to see how absurd and radicalized Lean theory has become. NATO employed a JIT distribution model (an aggressive, Lean inventory approach) by a system that is responsible for force protection – as in, people die if the system is not supported. The decision to employ that model within that particular system borders between criminal and negligence.

Conclusion

???????????Cataclysmic and extraordinary events occur regularly around the world; however, when only a region or a particular area of the globe is affected, these events often only matter to stock traders and international security consultants. Of course, the opposite is also true, when the entire world is affected by an extraordinary event (and the subsequent protective measures put in place – e.g. travel bans, curfews, masks and vaccine mandates) the result is stress on supply chains through increased and omnipotent volatility. Supply chains and systems that have moved to a radically Lean policy position are much more vulnerable (less resilient) to any amount of volatility. Worse still, vital, no-fail systems (such as NATO) took on policies and models within their operations that promoted risk and degraded resilience to volatility.

???????????COVID-19 shined a light on these risky practices, and began the conversation: which industries must be more resilient, due to the interdependency of cross-functional/cross-industry partnerships. Regulators should take note of the effects that global volatility wrought on store shelves, and continues to be felt in the shipping container crisis. Discerning and identifying those critical supply chains and systems, and then federally supplementing the cost of the stock necessary to defend against a certain amount of volatility could generate a reserve capability similar to the military WRM. Regardless of the solution, COVID-19 produced an amount of volatility to crack and degrade those supply chains operating under Lean policies and radically Lean inventory levels; and, in fact, showed an as-yet unseen cost (dependency) to the theory of Globalization and a more interconnected world.

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