Decision Making Made Easy—Optimize Supply Chain Allocations Using Anaplan Optimizer

Global supply chains in the modern business world have become more complex, and a lack of supply chain visibility can affect profitability. It becomes even more challenging when supply chain managers have to make a decision and match supply-demand, considering constraints and profitability. When it comes to supply chain allocations, strategically it becomes exceedingly difficult to decide how much volume/quantity to allocate, from which source to allocate, and where to allocate considering capacity and other constraints.

Linear programming is a scientific technique that can help businesses with decision making and optimizing allocations.

The general form of linear programming is as follows:

Maximize - A1X1 + …AnXn

Subject to - B1X1+…BnXn  = < C1

D2X1+…DnXn   =< C2

X1>= 0, Xn >= 0

In the above example, A1, An, B1, Bn, D2, Dn, C1, and C2 are given numbers, and X1 and Xn are variables. Similarly, linear programming can be used for minimization objectives as well.

The Anaplan Optimizer aids business planning and decision making by solving complex problems involving millions of combinations quickly to provide a feasible solution. Refer to the following supply chain network diagram. It has multiple distribution centers, multiple stores, and multiple SKUs. With the help of Anaplan Optimizer, supply chain managers can simply click a button and allocate demand by setting relevant constraints and objectives.

SUPPLY CHAIN NETWORK

Set the following prerequisites in Anaplan Optimizer:

• Input: Define inputs
• Objective: Define Maximization/Minimization
• Constraints: Define constraints
• Variable

Let’s refer to the following example:

1. INPUT: We have the following inputs:

1.1 – Input represents SKU-DC level stocks.

1.2 – Input represents SKU-Stores level demand

1.3 – Input represents SKU-Stores-DC level profitability =ASP per SKU – Cost per SKU

1.4 – Input represents DC wise maximum throughput/capacity

1.5 – Input represents DC to Stores supply feasibility metric /constraint.

1. OBJECTIVE

Set up an objective before running an optimizer (in this example—Maximize Profitability).

Maximum profitability after running Optimizer

1. CONSTRAINTS

Set up all constraints example—the value should be >0 and integer, allocation <=capacity etc.

1. VARIABLE

After Optimizer run, the following allocations are done based on maximum profitability and considering all constraints.

Supply chain managers can also set an objective to perform allocations based on product profitability, customer profitability, product-customer profitability, and customer priority. Additionally, supply chain managers can create multiple scenarios. Save and compare scenarios on the Anaplan platform to select the best fit scenario for the business.