1. Inventory policies
An inventory policy is a repeatable rule for deciding when to replenish and how much to order. A useful policy must fit both the demand pattern and the way suppliers, warehouses, and planning systems actually operate.
2. How much to order
Chapter 1 introduced several inventory policies. This chapter asks a narrower question: when a policy uses a fixed order quantity, how large should that batch be? We will build a simple cost model, find the order quantity that minimizes it, and then examine what the result can and cannot tell us.
3. When to order
Chapter 2 used EOQ to estimate a useful batch size. Now we add the other half of the decision: when should a replenishment order be triggered? The answer depends on how long supply takes and, for periodic policies, how often inventory is reviewed.
4. Safety stocks
So far, the inventory models treated demand and supplier timing as predictable. Real demand varies, and suppliers do not always arrive exactly on schedule. A safety stock is a planned buffer against that uncertainty. It is not a substitute for diagnosing a poor forecast, unreliable process, or unrealistic lead-time assumption.
5. Policies with safety stock
Chapter 4 calculated a buffer for demand over a protection period. A safety-stock number alone is not an operating rule: a supply chain also needs to know when it may order, how much it orders, and which inventory measure controls the decision.
6. Simulation lab
The earlier chapters derived policy settings under clear assumptions. In this lab, turn those settings into executable Python and inspect a small, reproducible demand trace. The data is deliberately supplied rather than randomly sampled: that makes timing and expected outputs easy to verify before you experiment with random demand.
7. Beyond normality
Normal demand is a useful starting point, but it can be a poor description of some inventory items. Demand is often nonnegative and right-skewed: most periods are ordinary, with occasional unusually large orders. A Normal model is symmetric and assigns some probability to negative demand.
8. Multi-echelon inventory
The earlier inventory models treated one item at one stocking location. Real networks can have plants, regional hubs, distribution centers, and stores, each holding stock for the same customer flow. Optimizing every location independently can protect local targets while increasing total inventory, hiding shortages, or amplifying order swings upstream.
9. Distribution & simulation
The previous chapter introduced multi-echelon inventory optimization (MEIO) and the simplified guaranteed-service risk-period model for a serial chain. This lesson extends that toolkit in two directions that appear constantly in practice:
10. Newsvendor
The newsvendor (or newsboy) model answers a single-period question: how much should I produce or order before demand is known, when you cannot replenish during the selling period and leftover units are costly?
11. Discrete demand PMF
Earlier lessons often assumed Normal demand, then introduced gamma as a continuous alternative. Those parametric families are convenient, but they can misrepresent a custom sales history.
12. Simulation optimization
Closed-form inventory models need strong assumptions: Normal or gamma demand, fixed or Normal lead times, pure backorders, non-perishable stock. Real chains often violate several of those at once—custom demand shapes from the previous lesson, stochastic lead times, time-dependent backlog penalties, partial lost sales, or product aging.