Tag Archives: supply chains

Avoiding Dead-end Streets As We Build the Future of Supply Chain Planning – Sep 23, 2022

Lora Cecere, the Supply Chain Shaman, has given us once again something to think about– big time. She goes off on some of the current fads in supply chain that she thinks are not worth pursuing.

It’s always wise to listen to Lora. She has a wealth of experience and many years of consulting with top companies to inform her thinking.

Here are some of her dead-end streets that you should be avoiding.

  • Dashboards
  • Lights-out planning
  • Real-time planning
  • DDMRP (demand-driven material requirements planning)
  • Forecast Sharing
  • Sales Forecasting

She believes future applications are adaptive and distributed. Read the article to see what she means, and how those two keywords play out.

The SCOR methodology she claims is useful in devising a new approach to planning. Figure 3 shows an outside-in model of planning for a company.

I’m particularly intrigued by her comments on dashboards. I’ve felt they were more about visibility than improving processes. While some of that is necessary, the real value will be if you can make changes quickly. And most dashboards, I’m afraid, don’t let the operators do that. They look cool but don’t help making critical changes.

Her remarks about real-time planning are also on point. If you rerun too frequently, the plans thrash— they oscillate between one action and another completely different one. There is value in following a consistent pattern and making slow changes rather than swinging back and forth. We saw that in spades during the Covid epidemic. Look at the auto manufacturers who canceled their chip orders and then six months later could not place them again because the capacity had been diverted to other chips that were more profitable as it happened. Acting too quickly caused them anguish that has persisted for three years now. Reruns generate noise that affects partners and destroys relationships.

I also share her skepticism about DDMRP which bases an entire manufacturing progression on orders. It’s better than not looking at orders, but it fails to capture the richness of what might happen in the future. We can point to the same auto manufacturers and see that they have hundreds of cars in an ‘almost-complete’ state while they wait for certain electronics to be delivered.

And she provides some good references on checking how forecasting is improved by getting the customer’s forecast in advance. Statistical tests indicate that for the most part they are not helpful. What characterizes the few that are? That’s worth research.

And finally, salesmen are the worst forecasters. First of all, they’re liars. And the best ones are the worst liars. Why? Especially if they are paid on commission, or via a plan that pays for meeting targets or provides spiffs for beating them, they will try to get the target set low. And for commission sales often they are not guaranteed, as to size especially. So the salesman might predict low-ball, because he’s sure he will get that as a target. Then the big order comes as an extra. But for manufacturing planning, that is not reality. Often big orders do not come exactly when anticipated, so they might not fall at quarter end when the financials are due. Timing is harder to predict than size.

In the past at a mid-size manufacturer, we had better luck eyeballing a steady rate of production from history (moderated by some anticipation of the future market) and then trying to get salespeople to tell us about big orders and when they anticipated them. We laid the big order forecasts on top of the ‘run-rate’ forecast to determine the timing of production. A few days’ change in big order timing didn’t then blow our whole manufacturing plan.

Salespeople are a good source of info about products and customers, but as manufacturing forecasters they are awful. Yet you need to cultivate them to get that key information about their expectations which is valuable.

Here’s a PDF of her post.

Avoiding Dead-end Streets As We Build the Future of Supply Chain Planning

Port of Houston mulls dwell measures to cope with record-breaking volumes

The Port of Houston is quickly becoming a major container import location. but some congestion is occurring and the dwell time of containers is increasing to close to 6 days, causing slowdowns in the yards. The port has adopted a plan to apply a dwell time fee for containers left beyond 6 days. It has not been actually enabled yet.

The port has also extended gate hours to allow drivers to access the yard over a longer period. We will see how many want to use the extended hours. At Los Angeles, the extended gate hours were not so successful, even with reduced charges for the extra time periods.

September 20, 2022 By Margherita Bruno

Port of Houston mulls dwell measures to cope with record-breaking volumes – Port Technology International

IANA panel: Intermodal chassis squeeze easing, but it’s far from over

Chassis have always been an Achilles’ heel of container or intermodal transport.

Chassis utilization is now about 90%, a high figure. And a chassis is essential to move a container. So people are holding on to chassis so they can reuse them, say for a reverse load. But that means the dwell time for the chassis is higher than it should be.

Some of the holding is due to the shortages; it’s too hard to get another chassis, so I’ll hold onto the one I just got, and even pay the dwell fee to have it for my outbound load.

If containerized cargo goes down from its current heights, the chassis situation will improve. But that would mean a reduction in cargo, and probably a recession; certainly decreased demands. For demand at this level, we definitely need a larger buffer of chassis, so there is some liquidity in the system.

One interesting point mentioned is the pressure ‘gray pools’, which hold chassis from multiple vendors near a large port or logistics hub, are seeing. We’ll find out if the cooperative approach can hold up under this stress.

Essentially, the pools provide a single shared inventory to a number of users. If a user, or a group of them, holds chassis on their own without sending them back, they are separating from the coalition, and they’re probably doing it because they see it as more favorable than returning the units. The separating group sees that they can do better by separating rather than remaining in the pool.

It’s a classic example of a breakdown in a cooperative game from operations research. Inventory pools have been studied for quite a while, by me and many others. The success of the cooperative scheme requires a ‘fair’ allocation of the benefits. If an individual participant, or some group of participants, are not seeing a better allocation of the gains than they would get separating, they will stay apart. This definition of fairness is called the ‘core’ of the cooperative game for the inventory pool. Under some fairly generous assumptions, we find there is always a core set of allocations, in which every group does better with the allocation than it does separately.

And a core allocation can be computed (there may be many of them), which will be fair. However, it’s virtually impossible to define a core allocation by using a pricing scheme for the use of the chassis. It’s almost never fair. There have to be subsidies beyond the price to make the groups stay in the pool.

That’s what is happening when the pools need to badger firms to return chassis, or when they charge dwell fees.

This would make a good project for an operations researcher, to study the rewards of using a pool over time, and examine how disruptions would affect the reward schemes that are in use now.

To find a fair allocation for a given pool is a good task, but once found, it’s probably not readily explainable to the participants. So they would be skeptical of their rewards, and might still split off. Hard computational results don’t always get the job done; the adopted solution must be ‘explainable’ or ‘interpretable’. There’s a growing body of literature on interpretable results, but not much on interpretable results for cooperative inventory games like this.

John Kingston Friday, September 16, 2022

IANA panel: Intermodal chassis squeeze easing, but it’s far from over – FreightWaves