The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
As the world holds its breathe to see how the new corona virus (2019-nCOV) will play out, we cross our fingers for all those currently in quarantine or under treatment and pray that health authorities around the world will soon get the upper hand.
This short note is about one way your business can develop a plan to adjust to one of the likely fallouts from the virus: sudden increases in the time it takes to get inventory replenishment from suppliers. Supply chains around the world are being disrupted. If this happens to you, how can you react in a systematic way?
Reacting to Longer Lead Times
This is a problem that can be solved using advanced supply chain analytics. Presumably, you may have already used this technology to make good choices for the control parameters used in managing all your inventory items, e.g., values for Min and Max or Reorder Point and Order Quantity. The specific technical question addressed here is how to convert an increase in replenishment lead time to changes in those control parameters.
In general, longer lead times require fatter inventories if you want to maintain a high level of customer service. This general rule translates into larger values of Min and/or Max. How much larger depends critically on what new, longer lead time values will appear and their probabilities of occurring.
While many planning software systems assume a fixed lead time, the reality is that almost all lead times have some degree of randomness. Typically, ignoring that randomness increases stockout risk, so having a good estimate of the probability distribution of lead times is important. In normal times, your transactional data can be used to estimate that relationship. But sudden disruptions like 2019-nCOV create unprecedented situations in which you have to make educated guesses about what new delays you will see and how likely they are. We will assume here that you can imagine some such scenarios and want to figure out how to best respond to them.
An Example using Advanced Software
To illustrate this type of prospective planning, consider a hypothetical example. One item, a spare part, has an established pattern of replenishment lead times, with delays of 5, 10 and 15 days occurring with 15%, 70% and 15% probabilities, respectively. Given this distribution and a random demand averaging one unit every 5 days, values of Min = 5 and Max = 10 do a good job. Figure 1 shows a simulation of 10 years of daily operation under this scenario. Fill rate and service level are high, and stockouts are infrequent.
Now suppose that disruptions in the supply chain create a less favorable distribution of lead time, with a 50:50 mix of 15 and 30 days. Figure 2 shows how badly the current values of Min and Max perform in this new scenario. Fill rate and service level plummet due to frequent stockouts. Operating costs more than triple due to penalties for backorders. Only inventory investment (the average dollar value of stock on the shelf) seems to get better, but this happens only because so often there are backorders with nothing left on the shelf. The shift to longer lead times clearly requires new higher values of Min and Max.
Figure 3 shows how the system performs when the Min is increased from 5 to 10 and the Max from 10 to 15. This change compensates for the longer lead times, restoring the previous high levels of fill rate and service level. Inventory investment is necessarily greater, but operating costs are actually lower than before.
Changes in normal operating conditions require adjustments in the way inventory items are managed. One such change looming large on this date is the potential impact of the 2019-nCOV Corona virus on supply chains, with anticipated increases in replenishment lead times.
Changes in lead times require changes in inventory control parameters such as Min’s and Max’s. These changes are difficult to make with any confidence using pure guesswork. But with some estimate of the increase in lead times, you can use advanced software to learn how to make these adjustments with some confidence.
This note illustrates this point using simulations of the daily operation of an inventory control system.
Figure 1 Simulation of normal operations using current replenishment lead times, Min and Max
Figure 2 Simulation of abnormal operations using longer lead times and current Min and Max
Figure 3 Simulation of abnormal operations using longer lead times and revised Min and Max
For most small-to-medium manufacturers and distributors, single-level or single-echelon inventory optimization is at the cutting edge of logistics practice. Multi-echelon inventory optimization (“MEIO”) involves playing the game at an even higher level and is therefore much less common.
You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.
Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.