Manufacturing cost optimization is a top priority for many companies, following months of lower demand as communities, regions and countries come out of lockdown. In a recent blog post from AspenTech partner Profit Point, Dr. Alan Kosansky explains why manufacturing network optimization models are extremely effective at identifying immediate (yet sometimes counterintuitive) ways to reduce costs and boost profits. According to McKinsey analysis, a diversified chemical producer boosted earnings before interest and taxes (EBIT) by 55% in some of their historically low-margin commodity segments as a result of manufacturing network optimization.

One chemical engineer who uses AspenTech process engineering tools told me he has come to know first-hand how incredibly powerful and valuable manufacturing network optimization models can be. I wanted to share his story with you as an example.

 

The Goal: Maximize Business Unit Profitability via an Integrated Operations Strategy

One of our customers operates large integrated chemical complexes. The company produces a wide variety of end-use chemicals and intermediates made from common and limited material inputs. The company’s product managers have commercial responsibility for specific salable products; this leads to internal negotiations and struggles to obtain bigger allocations of the limited inputs. At a higher level in the organization, business directors with commercial responsibility for a group of product families want to maximize their individual revenue goals. At an even higher level, an executive strives to optimize the manufacturing business unit’s overall P&L.

The chemical engineer in our story was given a highly visible project that was sponsored at the highest level in the organization. The objective was to optimize the business’ integrated manufacturing strategy in order to increase cash flow and margins and lower operating costs over a strategic business horizon. 

As the expression goes, nothing makes an engineer more productive than an exciting new challenge!

 

An Undiscovered Modeling Solution for Chemical Engineers

The chemical engineer was quite proficient with process simulation tools. Chemical engineering degree programs teach students to use process simulation tools like Aspen Plus^® or Aspen HYSYS^®. These modeling tools use engineering first principles to predict an outcome for a given set of input variables — they are useful across a scope of process unit operations. 

The engineer knew that this specific project required a different type of model. Optimizing the broader manufacturing network called for the company to examine part of a business system. Therefore, the model needed to consider financial incentives (spot prices, contracted prices, variable costs, etc.) and interrelated manufacturing constraints.

Fortunately, the engineer attended AspenTech’s OPTIMIZE™ global customer conference and learned about a successful network optimization project from another customer’s presentation. The presenter described how Aspen Supply Chain Planner™ could model and optimize all sorts of manufacturing and business complexities the customer never imagined possible. The presenter explained that their model could handle material tracking according to source of manufacturing, campaigned production, determining optimal shutdown and start-up decisions for key plants or units, and many other capabilities. The engineer was convinced that AspenTech had the right solution for the optimization project. 

 

A Quick Success!

The manufacturing network optimization model was configured in less than 3 months. The engineer was promoted to lead a dedicated Manufacturing Strategy group, which uses the model to run “what if” optimization studies for commercial and operations stakeholders. The manufacturing network optimization model contains close to 1.4 million variables and over 600,000 constraints; it employs mixed-integer programming (MIP) methods which enable this customer to economically optimize an overall business unit. 

The network optimization model includes the company’s integrated manufacturing facilities, as well as tolling (contract manufacturing) locations, product exchange agreements and contractual obligations. Most important, the model contains their key interrelated inputs and constraints including:

• Production capabilities and optionality including recycle streams

• Utilities (steam, electricity)

• Minimum production run quantities

• Key raw materials purchasing contract constraints

• Tolling agreement contract minimums

• Contract and spot pricing, etc.

The company can further analyze or chart manufacturing network optimization scenarios data using their business intelligence (BI) tool. It has been set up to display indicative financial pro formas and metrics such as net present value (NPV), EBITDA and contribution margin percent.

AspenTech remains committed to helping all our customers succeed by optimizing their businesses. If you would like to learn more about our manufacturing network optimization solution, you can contact us any time via our customer support site or email info@aspentech.com.

 

Learn more in the white paper “Alignment Between Supply Chain and Operations Execution: The Formula for Higher Profits in Chemicals."