Inventory Planning Technologies and How Optimization Theory Remains a Key Element.
As this year ends, it’s time to reflect on the advancements you made in your inventory planning. Did you
change processes and procedures? Did you add a more advanced software solution to help you plan and
service customers better?
What is your position about planning in the Cloud? What are your competitors doing or likely to do? Are
they staying the course on-premise or are they adopting new methods and technologies to improve
their business performance? Are they already in the Cloud? Are you already in the Cloud?
Inventory Optimization Overview
Optimization techniques are used to find a set of design parameters, x = {x 1 ,x 2 ,...,x n }, that can in some way be defined as optimal. In a simple case this might be the minimization or maximization of some
system characteristic that is dependent on x. In a more advanced formulation the objective function,
f(x), to be minimized or maximized, might be subject to constraints in the form of equality constraints,
G i (x) = 0 ( i = 1,...,m e ); inequality constraints, G i ( x) ≤ 0 (i = m e + 1,...,m); and/or parameter bounds, x l , x u .
A General Problem (GP) description is stated as subject to where x is the vector of length n design parameters, f(x) is the objective function, which returns a scalar value, and the vector function G(x) returns a vector of length m containing the values of the equality and inequality constraints evaluated at x.
An efficient and accurate solution to this problem depends not only on the size of the problem in terms
of the number of constraints and design variables but also on characteristics of the objective function
and constraints. When both the objective function and the constraints are linear functions of the design
variable, the problem is known as a Linear Programming (LP) problem. Quadratic Programming (QP)
concerns the minimization or maximization of a quadratic objective function that is linearly constrained.
For both the LP and QP problems, reliable solution procedures are readily available.
More difficult to solve is the Nonlinear Programming (NP) problem in which the objective function and
constraints can be nonlinear functions of the design variables. A solution of the NP problem generally
requires an iterative procedure to establish a direction of search at each major iteration. This is usually
achieved by the solution of an LP, a QP, or an unconstrained sub-problem.
Basically, what inventory optimization does is balance the investment in an inventory with the fill-rate
(service level) goals of a company. There are also financial considerations, constraints that can also be
applied in the development of the algorithm. These highly complex algorithms work behind the scene so
as not to confuse the user or make them fearful of using optimization to help manage their inventory.
We have determined that you can dramatically improve the effectiveness of the optimization algorithms
by adding heuristics into the overall process equation. Heuristic refers to experience-based techniques
for problem solving, learning, and discovery. Heuristic methods are used to speed up the process of
finding a satisfactory solution, where an exhaustive search is impractical.
Valogix’ optimization process defines the optimal stocking quantity (SQ) for every item at every location.
Plus, it goes one step further and not only calculates the SQ to reach the service level goal, but an
enhanced algorithm actually calculates the full coverage SQ as well. This full coverage SQ is based on the
probability of stocking out and determines how best to avoid that situation.
Valogix provides the critical tools for planning and executing your supply chain and customer service
strategies in the Cloud. These automated, intelligent tools help to improve your competitive advantage
by keeping inventory costs low and availability high, so you can focus on customer service. These tools
allow any employee to use them anywhere, anytime freeing up resources to do what you do best - sell
and service your products. This combination of technologies allows you to work smarter by easily
adapting all aspects of inventory management into one advanced, simple-to-employ action plan.
- Reduced inventory by 20% to 40% or more usually within 6 months or less
- Reduced expediting and emergency shipments by 35% or more
- Improved productivity by reducing planning time by 60% to 80% or more
- Controlled and reduced replenishment spending by 15% or more
- Improved service levels by having the right items available when your customers want them
by 5% to 15% or greater - Reduced stockouts by 15% - 30% or higher
- Increased sales of 15% to 15% or more
- Reduction in cost of goods by 5% to 10% due to improved vendor relations
Make sure you review the inventory solutions carefully and find out if they include true inventory
optimization algorithms. You owe it to yourself, your company and your customers to choose the right
solution to help solve your inventory planning needs affordably.
Want to learn more? Contact Valogix today for your free demo to see how much you could be
saving.