Approaches for choosing a "risk" factor in an Inventory Optimization problem?

Question

I'm working on an Inventory Optimization (Allocation) problem.

The decision variable is the amount of inventory budget to allocate for each product, from a set of products. My objective is to maximize my profit. My constraint is a total budget which limits how much inventory I can purchase (I can't satisfy demand for all products - otherwise my allocations would simply be my forecasts).

The inputs to the problem are the following:

• Future sales forecast for each product.
• Forecast intervals (analogous to standard deviation) for each product: For example, for product 1, the forecast is 190 Units +/- 50 units, while the forecast for product 2 is 200 +/- 20 units.
• A desired in-stock rate for each product.
• A profit margin for each product.

If I only use point forecasts (i.e. I discard the second input), then maximizing my total profit across all products becomes pretty straightforward. I would just allocate as much as possible to products that will get me the most profit based on the margin × forecast sales. Basically allocate to products in order of decreasing profitability.

But if I take into account the forecast intervals, then the problem becomes more complex, and a "risk" factor seems to come into play: Product 1 might be the most profitable if I consider the upper bound of the forecast interval, but product 2 might be the most profitable if I consider the median of the forecast interval.

The only way to optimally allocate my budget is if I define a risk factor (similar to portfolio optimization).

How do I go about deciding this risk factor? What the approaches for doing so? Does service level/in stock rate come into play?

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To clarify based on LarrySnyder610♦ 's comments.

By risk I do not mean risk of running out of stock. I mean the risk that comes from the variance in the product forecasts. To take the example I mentioned above:

Say we have just two products, where all is equal: margins, cost, etc...and lets say that I am forced to choose between one or the other for space or logistics constraints.

Product 1 one has a mean forecast of 190 units and a forecast interval of 50 units, so my forecast can be thought of as most likely falling between 140 and 240.

Product 2 has a mean forecast of 200 units and a forecast interval of 20 units, so my forecast can be thought of as most likely falling between 180 and 220.

If I disregard my forecast intervals, then obviously should go with product 2, since that means I would sell 200 units instead of 190.

However, if I take into account the forecast intervals, then there is a possibility that I will make more money if I go with product 1 (I'm lucky and I sell 240 units, compared to 220 units for product 2). But there is also a chance that I sell only 140 units, hence making less the lower bound of 180 for product 2.

So there is this idea that there is a "risk" factor involved: If I am willing to take the risk, then my optimal solution is to allocate to product 1. If I want to play it safe, then my optimal solution is to go with product 2.

To reiterate the main question: How do I quantify the risk? And what are the approaches for deciding whether I should take a risk or not? And does this risk factor somehow tie into service levels/in-stock rates (I don't think it does, but I might be wrong) ?

Most of all how do I communicate this to business users and product managers who have no math or stats knowledge?

Answer 1

Here's an approach that might be close to what you are looking for. Suppose that we have $n$ products, and for each product $i$ we know:

• $c_i$ = purchase cost per unit (i.e., cost to order inventory from the supplier)
• $\pi_i$ = profit margin per unit sold
• $f_i$, $F_i$ = probability distribution (pdf, cdf) of demand per period

If there is only 1 product, then this is just a newsvendor problem. With multiple products, it's just multiple newsvendor problems subject to a budget constraint. The decision variables are:

• $S_i$ = order quantity for product $i$

(You didn't specify whether this is a single- or multi-period model. I'm assuming single period, but it's not too hard to adapt this to the multi-period case. In that case we assume we follow a base-stock policy for each product, and the $S_i$ variables are interpreted as base-stock levels.)

For each product $i$, the expected profit function is

\begin{align*} \Pi_i(S_i) & = \pi_i\mathbb{E}[\min\{S_i,D_i\}] - c_iS_i \\ & = \pi_i\left[\mathbb{E}[D_i] - n_i(S_i)\right] - c_iS_i, \end{align*} where $n_i(x)$ is the loss function for distribution $f_i$, i.e.,

$$n_i(x) = \int_x^\infty (y-x)f_i(y)dy.$$

(The second $=$ follows from the fact that $\min\{S,D\} = D - (D-S)^+$.) The loss function $n_i(x)$ is convex (its derivative is $F_i(x)-1>0$), so $\Pi_i(S_i)$ is concave.

Then we want to solve the following optimization problem:

\begin{align*} \text{maximize} \quad & \sum_{i=1}^n \Pi_i(S_i) \\ \text{subject to} \quad & \sum_{i=1}^n c_iS_i \le B \\ & S_i \ge 0 \quad \forall i=1,\ldots,n, \end{align*} where $B$ is the budget. This is a convex problem (concave maximization), and should be easy to solve.