{"id":12196,"date":"2023-12-05T11:01:06","date_gmt":"2023-12-05T15:01:06","guid":{"rendered":"https:\/\/smartcorp.com\/forecasting\/how-to-forecast-inventory-requirements-copy\/"},"modified":"2024-04-08T09:13:24","modified_gmt":"2024-04-08T13:13:24","slug":"encontrar-su-lugar-en-la-curva-de-compensacion-gestion-de-inventario","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/es\/blog\/finding-your-spot-on-the-tradeoff-curve-inventory-management\/","title":{"rendered":"Encontrar su lugar en la curva de compensaci\u00f3n"},"content":{"rendered":"

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Balancing Act<\/strong><\/p>\n

Managing inventory, like managing anything, involves balancing competing priorities. Do you want a lean inventory? Yes! Do you want to be able to say \u201cIt\u2019s in stock\u201d when a customer wants to buy something? Yes!<\/p>\n

But can you have it both ways? Only to a degree. If you lean into leaning your inventory too aggressively, you risk stockouts. If you stamp out stockouts, you create inventory bloat. You are forced to find a satisfactory balance between the two competing goals of lean inventory and high item availability.<\/p>\n

Striking a Balance<\/strong><\/p>\n

How do you strike that balance? Too many inventory planners \u201cguestimate\u201d their way to some kind of answer. Or they work out a smart answer once and hope that it has a distant sell-by date and keep using it while they focus on other problems. Unfortunately, shifts in demand and\/or changes in supplier performance and\/or shifts in your own company\u2019s priorities will obsolete old inventory plans and put you right back where you started.<\/p>\n

It is inevitable that every plan has a shelf life and has to be updated. However, it is definitely not best practice to replace one guess with another. Instead, each planning cycle should exploit modern supply chain software to replace guesswork with fact-based analysis using probability math.<\/p>\n

Know Thyself<\/strong><\/p>\n

The one thing that software cannot do is compute a best answer without knowing your priorities. How much do you prioritize lean inventory over item availability? Software will predict the levels of inventory and availability caused by any decisions you make about how to manage each item in your inventory, but only you can decide whether any given set of key performance indicators is consistent with what you want.<\/p>\n

Knowing what you want in a general sense is easy: you want it all. But knowing what you prefer when comparing specific scenarios is more difficult. It helps to be able to see a range of realizable possibilities and mull over which seems best when they are laid out side by side.<\/p>\n

See What\u2019s Next<\/strong><\/p>\n

Supply chain software can give you a view of the tradeoff curve<\/em>. You know in general that lean inventory and high item availability trade off against each other, but seeing item-specific tradeoff curves sharpens your focus.<\/p>\n

Why is there a curve? Because you have choices about how to manage each item. For instance, if you check inventory status continuously, what values will you assign to the Min and Max <\/em>values that govern when to order replenishments and how much to order. The tradeoff curve arises because choosing different Min and Max values leads to different levels of on hand inventory<\/em> and different levels of item availability, e.g., as measured by fill rate<\/em>.<\/p>\n

\u00a0<\/strong><\/p>\n

A Scenario for Analysis<\/strong><\/p>\n

To illustrate these ideas, I used a digital twin<\/a>\u00a0 to estimate how various values of Min and Max would perform in a particular scenario. The scenario focused on a notional spare part with purely random demand having a moderately high level of intermittency <\/em>(37% of days having zero demand). Replenishment lead times were a coin flip between 7 and 14 days. The Min and Max values were systematically varied: Min from 20 to 40 units, Max from Min+1 units to 2xMin units. Each (Min,Max) pair was simulated for 365 days of operation a total of 1,000 times, then the results averaged to estimate both the average number of on hand units and the fill rate, i.e., percentage of daily demands that were satisfied immediately from stock. If stock was not available, it was backordered.<\/p>\n

 <\/p>\n

Results<\/strong><\/p>\n

The experiment produced two types of results:<\/p>\n