Supply chain optimization - it’s not magic, it’s mathematics Companies with complex supply chains must make real-time, data-driven

decisions to ensure efficient resource utilization and first-class customer

service. Yet, many organizations continue to rely on outdated planning

tools such as spreadsheets and MRP.

Supply chain planning based on mathematical optimization is

fundamentally different from traditional methods and allows companies

to maximize overall supply chain performance.

This whitepaper uses common planning scenarios to explain how supply

chain optimization technology works and why companies looking to

streamline operations and maximize profits should consider switching.

WHITEPAPER

Optimization – Not just for big business

Supply chain optimization software has been around for decades, but it’s only recently that the

technology has become a viable alternative for small and mid-size businesses.

Today, the processing power required to run the optimization algorithms comes at a fraction of the

cost. At the same time, supply chain optimization software has become more user-friendly and easier

to maintain. Thanks to these advances, smaller organizations with limited IT budgets and resources can

now benefit from this powerful technology.

Why do I need optimization?

Optimization software transforms the way companies plan and manage their supply chains.

Because the optimization engine takes supply chain constraints and costs into account, the

system-generated plans are validated upfront. The software can therefore provide the planner

with more accurate decision support.

The result is better customer service and resource utilization with less inventory - all helping to

boost the bottom line. These instant benefits are why optimization implementations offer such an

attractive ROI.

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How does supply chain optimization work?

As humans, we trust the familiar and what we understand. Therefore, it’s essential for companies that

rely on spreadsheets and MRP software to take the time to understand the fundamental differences

between traditional planning tools and modern solutions powered by mathematical optimization.

While there’s no need for planners and business users to understand the inner workings of

mathematical modeling and optimization techniques, a good grasp of what underpins the planning

approach is needed. To help with this understanding, let’s take a closer look at the three core

elements of a supply chain optimization solution:

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1. The supply chain model (the “Digital Twin”)

The Digital Twin is an electronic representation of your physical supply chain.

It’s a detailed model of your processes and resources, with their associated costs

and constraints. The model provides the blueprint for your planning solution.

Warehouses, production lines, machines, vehicles, tooling, and staff are all

represented here.

2. System interfaces

The planning tool needs accurate, up-to-date information from your existing

business systems. Depending on your setup, direct interfaces with ERP, PLM,

CRM, and Transportation & Warehouse Management systems provide access

to the necessary input data. Examples of required information are sales forecasts,

customer orders, product information, and stock levels.

3. The solver

The solver is the brain of the optimization solution and where the magic happens.

It contains mathematical algorithms that work together to solve the supply chain

problem described by the digital twin and the input data.

The solver optimizes one of the two available “objective functions”:

Maximum Profit

Max Delivery Service at Lowest Cost

If revenue is represented in the model, the solver will determine the plan that

maximizes your overall profitability. However, if revenue is not included in the

model, the solver will maximize customer service at the lowest possible cost.

It is important to be aware of the difference between these two objective

functions. However, in both cases the solver will find the best overall solution

for the business with respect to the defined business rules and constraints.

2

1

“To find the optimal plan, we first

need to define the planning

problem - including all the

parameters framing it. Once a

digital model of the supply chain

is in place, the mathematical

optimization algorithms can go

to work.”

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Optimization Examples

To get a better feel for the difference between optimization technology and traditional planning

approaches, we can look at some common planning problems.

Example 1 – Stock Build

Let’s start with a stock build, essential for businesses dealing with seasonality or big promotions that

cause peaks in demand.

We have two basic options; to gradually build up the required stock over time or to add extra capacity

closer to the peak.

Gradually building up stock allows us to maintain a steady, cost-efficient production and to make use

of any spare capacity. But it also requires us to hold stock for longer with higher storage and handling

costs and an increased risk of expiry and obsolescence.

The diagrams below show how we gradually build stock by maintaining a steady utilization of the line

(40 hours per week) ahead of the peak in demand during week 29 – 32.

Stock build over longer time period

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Base Capacity Additional Capacity

Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

Weekly production

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Stockbuild Demand

Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

Inventory level by week

Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

The other alternative is adding more production capacity closer to the peak in demand by adding extra

in-house capacity or outsourcing. This approach keeps inventory levels and inventory-related costs low,

but the additional production capacity usually comes at a premium, if at all available.

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Stock build using extra capacity closer to peak in demand

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Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

Inventory level by week

Stockbuild Demand

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Production by week

Base Capacity Additional Capacity

Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

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Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

Stockbuild Demand

Inventory level by week

Stockbuild Demand

Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32Week 20 Week 21 Week 22 Week 23 Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Week 30 Week 31 Week 32

The above graphics show the difference between the two, both potentially viable, alternatives. But

which one is our best option? It could even be a combination of the two.

If we only look at one product in isolation, this is not a difficult problem. But in a more realistic scenario,

with hundreds or even thousands of products competing for production and distribution capacity

across multiple sites, the optimal solution is often anything but obvious, sometimes even surprising.

Planning the stock-build with optimization

Planning always involves tradeoffs. A supply chain optimization solution automatically determines the

stock-build strategy that best satisfies your overall business goals – be it to maximize overall profits or

meet customer service targets at the lowest possible cost. Crucially, when determining the stock-build

strategy, the mathematical solver takes the entire supply chain into account. If the focus is solely on the

stock-build product, the business as a whole is likely to suffer.

To perform the mathematical optimization, the solver needs access to the data that frames the planning

problem. In our example, the supply chain model defines the costs and constraints relating to overtime,

cost of capital, product expiry, batch sizing, product mix, and more. And with direct access to demand

and on-hand stock information from 3rd party systems, the solver has everything it needs to calculate

the optimal plan.

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Planning the stock-build without optimization

Planning the same stock build using the MRP and DRP functionality of an ERP system is an entirely

different challenge. With tools that don’t consider production and inventory constraints, the planner

is left with a lot of work to validate the plan’s basic feasibility.

Unable to recognize planning constraints, MRP and DRP must make a series of assumptions, including

infinite capacity and fixed production and distribution lead times. However, capacity is rarely unlimited,

and lead times are, per definition, not fixed. Instead, they depend on the availability of materials and

production, storage, and distribution capacity. As a result, MRP-generated plans are of much

lower quality.

MRP’s inability to deliver a plan that can be executed “out-of-the-box” is only part of the problem.

Because MRP doesn’t consider the plan’s overall profitability, it is difficult to determine if it meets the

company’s business goals.

Example 2 – Product Mix

In our second example, we take a closer look at how the system uses mathematical modelling to

optimize a planning problem. The planning scenario is a bakery that wants to determine an optimal

product mix. To make things simple, we only consider two products, bread and brownies, and two

ingredients – flour and sugar.

Optimizing the product mix with optimization

1) The flour supply is limited to 50kg for the period. The planning system accesses the product

BoMs and sees that a bread requires 1kg of flour and a brownie ½ kg. The graph below

shows the flour constraint.

Available Flour

50kg

Flour Needed

1kg for Bread0.5kg for Brownies

100

50

50 100

Bread Qty

Brownie Qty

Flour

Possible product mix considering flour availability

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2) After adding the supply constraint for sugar, we get the following picture. Our possible product

mix is now reduced.

Demand for Brownies

25

Demand for Bread

45

Demand for Brownies

100

50

50 100

Bread Qty

Brownie Qty

Demand for Bread

Flour

Sugar

Possible product mix considering flour and sugar availability and demand

3) Next, the demand forecasts for the two products, bread (45) & brownies (25), are imported

from the demand planner. As we don’t want to produce more than what we have demand for,

the possible product mix is further reduced.

Available Sugar

60kg

Sugar Needed

2kg for Brownies0.5kg for Bread

100

50

50 100

Bread Qty

Brownie Qty

Flour

Sugar

Possible product mix considering flour and sugar availability

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4) The optimal product mix is where the bakery achieves maximum profit. With a profit

contribution of $1.50 for bread and $2.00 for brownies, the system has all the information

it needs to determine the optimal mix.

5) In this example, a Linear Programming (LP) algorithm is used to determine the optimal

product mix. The system establishes the profit contribution line and finds the optimum.

Remember - all this happens automatically!

Profit Contribution

$1.50

Profit Contribution

$2.00

Demand for Brownies

100

50

50 100

Bread Qty

Brownie Qty

Demand for Bread

Flour

Sugar

We now shift the profit contribution line towards the green area. Our optimal product mix (max profit) is where the line first hits the green area.

100

50

50 100

Demand for Brownies

Sugar

Bread Qty

Brownie Qty

Demand for Bread

Flour

100

50

50 100

Bread Qty

Brownie Qty

Demand for Bread

Flour

Sugar

Demand for Brownies

Profit Contribution Line: Any product mix on this line will generate the same overall profit

“There are several different algorithms available depending on the mathematical structure of the

planning problem. In this example, the system chooses an optimization technique called Linear

Programming. Crucially, the planner doesn’t need to be a mathematician to use the

system, as this is all taken care of automatically.”

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The above example is very simple, but the beauty of mathematical optimization is that we can

achieve the same optimized results with thousands of products, limited supply materials, and

capacity-constrained production, storage and transportation resources.

Optimizing the product mix without optimization

Without optimization, the pattern from our first example will repeat itself. An MRP-generated plan will

produce the total demand quantity of 45 breads and 25 brownies, despite the limited supply of flour.

While the system will recognize the resulting shortage of flour, this will only be clear to the planner

after the fact. Thus, it is down to the planner to resolve the problem.

Demand for Brownies

100

50

50 100

Bread Qty

Brownie Qty

Demand for Bread

Flour

Sugar

Max Profit

40Bread

20Brownies

6) The optimal product mix is 40 breads and 20 brownies.

Video - Supply Chain Optimization Explained - Optimity Software

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Changing the role of the planner

Working with an MRP system (without mathematical optimization) is very different from a supply chain

optimization solution.

With MRP, planners are notified when there’s an issue but left to figure out how to fix the problem

themselves. This is time-consuming and invariably leads to sub-optimal decisions based on gut feel and

bias rather than real-time data and facts.

With supply chain optimization, planning becomes much more proactive and strategic to the

organization. With a system that models and optimizes the entire supply chain, planners and managers

have complete visibility of all activities and how they interrelate. When someone makes a change to the

plan, any ripple effects are immediately visible.

How optimization impacts the role of a planner

Summary

Supply chain planning based on mathematical optimization has the power to transform your business.

The technology is mature and proven to add significant value and it is now available and affordable to

companies of all sizes.

Any manufacturing or distribution organization not yet using supply chain optimization is strongly

encouraged to find out what it would mean to their customer service and bottom line.

Improved planning automation means more time to focus on exceptions

The plans can be executed “out-of-the-box” with little or no need for manual adjustment

The planning function will become more strategic when trust continues to grow

The speed of the optimization algorithms allows planners to run multiple what-if analyses before

making important decisions

The planner will maintain the “digital twin”, keeping business rules, costs, and constraints up

to date.

www.optimitysoftware.com

About Optimity

Optimity helps companies achieve better supply chain planning by leveraging

tools that unify the business and drive growth and success through the power

of true supply chain optimization technology. With offices in the US, Europe,

Asia, Australia, and New Zealand, the company offers a suite of modules that

work seamlessly together or independently to suit precise supply chain planning

requirements and challenges, including silos and uncertainty. Their solutions are

supportive of overall business strategies across all time frames and offer

unprecedented control, accuracy, and visibility across supply chain channels.

As a result, supply chain planning becomes a driver for growth and

competitive differentiation.

North America

United States, Head Office

Optimity Inc.

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CO 80302

USA

Tel: +1-720-726-8031

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