Those of you around in 2015 may recall that
I opened a series on what was happening in the US healthcare system with a description of a business simulator game developed at MIT, called The Beer Game.
The Beer Game is an in-person supply chain simulator, where players play in teams of four roles: beer retailer, wholesaler, distributor, and manufacturer. The goal is to minimize costs, with the team who has the lowest costs winning.
The game is incredibly simple, even crude:
The game is played on a board that portrays the production and distribution of beer (figures 1-2). Each team consists of four sectors: Retailer, Wholesaler, Distributor, and Factory (R, W, D, F) arranged in a linear distribution chain. One or two people manage each sector. Pennies stand for cases of beer. A deck of cards represents customer demand.
Each player can make one order a week (in game time); the retailers get told what customer demand was (i.e. how much beer they sold last week) and can order from their wholesaler, the wholesalers can order from their distributor, the distributors order from their factory.
At each stage there are shipping delays and order processing delays. The players' objective is to minimize total team costs. Inventory holding costs are $.50/case/week. Backlog costs are $1.00/case/week, to capture both the lost revenue and the ill will a stockout causes among customers. Costs are assessed at each link of the distribution chain.
[...] The game is initialized in equilibrium. Each inventory contains 12 cases and initial throughput is four cases per week. In the first few weeks of the game the players learn the mechanics of filling orders, recording inventory, etc. During this time customer demand remains constant at four cases per week, and each player is directed to order four cases, maintaining the equilibrium. Beginning with week four the players are allowed to order any quantity they wish, and are told that customer demand may vary; one of their jobs is to forecast demand. Players are told the game will run for 50 simulated weeks, but play is actually halted after 36 weeks to avoid horizon effects.
Each player has good local information but severely limited global information. Players keep records of their inventory, backlog and orders placed with their supplier each week. However, people are directed not to communicate with one another; information is passed through orders and shipments. Customer demand is not known to any of the players in advance. Only the retailers discover customer demand as the game proceeds. The others learn only what their own customer orders.
[...]
The game is deceptively simple compared to real life. All you have to do is meet customer demand and order enough from your own supplier to keep your inventory low while avoiding costly backlogs. There are no machine breakdowns or other random events, no labor problems, no capacity limits or financial constraints.
That description is from "
Teaching Takes Off: Flight Simulators for Management Education" in
OR/MS Today Oct 1992, by John Sterman of the Systems Dynamics Group at the MIT Sloan School of Management, which developed the Beer Game. He goes on to explain:
Yet the results are shocking.
Consistently, every time the game is played, whether with undergrads or with seasoned CEOs, there wind up being massive boom-bust oscillations in inventory.
Though individual games differ quantitatively, they always exhibit the same patterns of behavior:
1. Oscillation: Orders and inventories are dominated by large amplitude fluctuations, with an average period of about 20 weeks.
2. Amplification: The amplitude and variance of orders increases steadily from customer to retailer to factory. The peak order rate at the factory is on average more than double the peak order rate at retail.
3. Phase lag: The order rate tends to peak later as one moves from the retailer to the factory.
He explains that:
Average team costs are about $2000, though it is not uncommon for costs to exceed $10,000; few ever go below $1000. Optimal performance (calculated using only the information actually available to players themselves) is about $200. Average costs are ten times greater than optimal!
So why is this? Is it because businesses can't handle wild swings in consumer demand?
Nope.
Wait for it....
After the game I ask the players to sketch their best estimate of the pattern of customer demand, that is, the contents of the customer order deck. Only the retailers have direct knowledge of that demand. The vast majority invariably draw a fluctuating pattern for customer demand, rising from the initial rate of 4 to a peak around 20 cases per week, then plunging.
"After all, it isn't my fault", people tell me, "if a huge surge in demand wiped out my stock and forced me to run a backlog. Then you tricked me - just when the tap began to flow, you made the customers go on the wagon, so I got stuck with all this excess inventory." Blaming the customer for the cycle is plausible. It is psychologically safe.
But
And it is dead wrong. In fact, customer demand begins at four cases per week, then rises to eight cases per week in week five and remains completely constant ever after.
I want to hammer this home, and make sure you get the full import of this: a single, one-time persistent bump-up in customer demand plunges the system into wild boom-bust oscillations. Consistently.
Consistently. Always."A single one-time persistent bump-up in customer demand"
is an accurate description of what just happened in the market for household supplies anywhere there's a stay-at-home order.
Every household now buying twice as much toilet paper because they are simply home twice as much and using twice as much. Every household now buying as much more food from grocery stores as they used to buy from restaurants they no longer patronize. Every household now buying sanitizers they didn't use to need in such quantity because they have to clean more things and more frequently.
These changes represent a single, one-time persistent bump-up in consumer demand for these things. Demand for certain consumer goods just went up, and will stay at that new heightened level, indefinitely.
We are in the Beer Game.And what the Beer Game teaches us is that the supply chain
will start showing massive fluctuations. There will be shortages and then gluts, in repeating cycles.
This is not because people are hoarding. This is not because of fickle consumer demand. This is not because of consumers doing anything wrong. Supply chains being massively disrupted doesn't require any of these things. Sterman goes on:
This revelation is often greeted by disbelief. How could the wild oscillations arise when the environment is virtually constant? Since the cycle isn't a consequence of fickle customers, players realize their own actions must have created the cycle. Though each player was free to make their own decisions, the same patterns of behavior emerge in every game, vividly demonstrating the powerful role of the system in shaping our behavior.
Research reported in Sterman (1989) shows how this occurs. Most people do not account well for the impact of their own decisions on their teammates - on the system as a whole. In particular, people have great difficulty appreciating the multiple feedback loops, time delays and nonlinearities in the system, using instead a very simple heuristic to place orders. When customer orders increase unexpectedly, retail inventories fall, since the shipment delays mean deliveries continue for several weeks at the old, lower rate. Faced with a growing backlog, people must order more than demand, often trying to fix the problem quickly by placing huge orders. If there were no time delays, this strategy would work well. But in the game, these large orders stock out the wholesaler. Retailers don't receive the beer they ordered, and grow increasingly anxious as their backlog worsens, leading them to order still more, even though the supply pipe line contains more than enough. Thus the small step in demand from four to eight is amplified and distorted as it is passed to the wholesaler, who reacting in kind, further amplifies the signal as it goes up the chain to the factory. Eventually, of course, the beer is brewed. The players cut orders as inventory builds up, but too late - the beer in the supply line continues to arrive. Inventories always overshoot, peaking at an average of about forty cases.
Faced with what William James called the "bloomin', buzzin' confusion" of events, most people forget they are part of a larger whole. Under pressure, we focus on managing our own piece of the system, trying to keep our own costs low. And when the long-term effects of our short-sighted actions hit home, we blame our customer for ordering erratically, and our supplier for delivering late. Understanding how well intentioned, intelligent people can create an outcome no one expected and no one wants is one of the profound lessons of the game. It is a lesson no lecture can convey.
Now, the point of the Beer Game is to teach business executives how
not to get nailed by this phenomenon.
Focusing on external events leads people to seek better forecasts rather than redesigning the system to be robust in the face of the inevitable forecast errors. [...] The game highlights the importance of coordination among levels in an organization, the role of information systems in controlling complex systems, and the implications of different production paradigms such as Just-In-Time inventory management.
Show of hands: who thinks the US's system for getting toilet paper onto grocery shelves has ever been "redeisgned to be robust in the face of the inevitable forecast errors?"
More critically, insofar as a supply chain
isn't different levels in
an organization, there's little or no chance of coordination. Like I explained later in the same series about healthcare, "
The Proliferation of Organizations", when business functions are outsourced or spun off into separate companies, or were never part of the same company to begin with
[...] because there is no shared decision-making hierarchy over the various independent business and governmental organizations, there is nothing – nothing – stopping them from acting in their own best interest at the expense of the other organizations in the health care system.
And I mean “expense” entirely literally.
The proliferation of organizations sets up an inadvertently antagonistic system, where each entity in it attempts to optimize for its own function, and there are no incentives for minimizing the expenses to others. [...]
And that is precisely the problem with the proliferation of organizations: because nobody oversees the big picture, nobody is in a position to tell any one organization in the health care system, “No, you aren’t allowed to reduce your own costs in this way which multiplies the costs of another organization so extremely.”
It’s crabs in a bucket: all of these organizations in straitened economic circumstances, trying to minimize their expenses in ways which advance their own ledgers one cent by shoving others’ back two cents – or ten cents.
[...]
In game theory, this is what is known as a Nash equilibrium: a situation among vying, non-cooperating parties, where if each pursues its own best strategy for maximizing their own position, everybody winds up worse off than if they’d been able to cooperate.
You know, like in the Prisoner’s Dilemma.
Nash equilibria are states that systems get stuck in, states that are suboptimal for all the parties in them, because the only way out is for at least one party to unilaterally do something disadvantageous to themselves. Some can only be resolved by all parties unilaterally acting to their own disadvantage.
[...]
It may be that payers (insurers) literally have no idea what the consequences of their policies are. Why would they? Those consequences all play out on the other side of an opaque organizational boundary.
All of which is the next thing not to love about the proliferation of organizations. If the purpose of organ-ization is to manage the costs of coordinative communication by routinizing it and limiting it, then necessarily organizational boundaries are opaque boundaries, through which little stray information passes. If one organization multiplies the expenses of another, it may not ever know. It’s happening to people they do not talk to and whose affairs they really aren’t party to.
(For more on information opacity as an essential part of organizational oundaries, see my "
Massless Ropes, Frictionless Pulleys: Coordinative Communication".)
And where Sterman says "the game highlights [...] implications of different production paradigms such as Just-In-Time inventory management", yeah, well, the implication it illuminates in Just-In-Time inventory management is that JIT saves money on inventory storage at the expense of making systems extremely brittle in the face of modest demand fluctuations and vulnerable to exactly this failure mode. Or put more generously, JIT only
actually works out at the bottom line if you have some way to manage demand forecast errors (i.e. a crystal ball) and reduce to negligible the hysteresis in the system (i.e. a teleporter) otherwise modest demand fluctuations will be so costly they'll wipe out all the savings of not paying for inventory storage space, which is the point of JIT. A topic for another post.
JIT came in (IIRC) in the 1980s, Sterman's article is from 1992, and since then, American industry has only doubled down on JIT. There's a story there, I'm not going to tell it here. A topic for another post.
Consequently, the American market for groceries and consumer goods tends to be very JIT-based and not very vertically integrated, which, in my estimation, drops to zero the chance that anyone is going to actively manage this situation. Organizations at different levels in the supply chain - and
discussed here for meat, and see
this comment – have antagonistic relationships so will not be cooperating, certainly not at the expense of their own immediate advantage, and probably have no idea what is going on with one another.
Which suggests to me we are in an unmitigated Beer Game, and will be experiencing oscillations between oversupply and undersupply for groceries and similar.
The period of those oscillations is mostly a function of the delay between
placing an order and
receiving the order placed, at every step of the way. In the Beer Game, orders are only placed weekly; I don't know how rapidly they were fullfilled, that the result was observed to be typically 20 week cycles.
If you happen to know what the turn-around typically is for a grocery store ordering, say, meat, or toilet paper, or Lysol, I would be keen to know. Likewise for wholesalers ordering from
their suppliers, and on up the chain.
It seems to me that with that knowledge some canny people might be able to model our present boom-bust cycle pretty easily. I wonder where I put my STELLA disks....
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