Business Case Studies
 

Optimal Business Case Studies

Our tailored approach to production scheduling, supply chain planning, and process optimization applies across a broad and diverse set of industries and manufacturing processes.  Building on our experience from multiple implementations at OptTek Systems, we offer proven solutions, trusted by some of the world's most reputable companies.

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Global Automobile Seat Manufacturer

  • Reduced tool changes by 62% 

  • Reduced overtime costs

  • Guaranteed 100% on-time shipments.

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High Volume E-Commerce Printing Operation

  • Improved on-time shipping average from 75% to 90+%

  • Reduced order cycle time from 7 days to 4 days

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Pipe Insulation Manufacturing Operation

  • Increased throughput by 9%

  • Eliminated need for overtime

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Leading Flour Milling Operation

  • Improved wheat mix value by 5%

  • $700K to $1.5M in savings per year per mill

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Large Oil & Gas Refinery and Distribution Company: Inventory Routing Optimization

  • Savings of more than $2MM per month on delivery of crude derivatives from refineries to collection centers

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Global Dairy and Plant-Based Food Operation

  • Estimated annual savings of over $16 million through the reduction in product waste and product shortages.

White Papers

Our know-how is based on decades of research by a team of world-renown mathematicians and decision scientists.

01

Optimal Production Scheduling (OPS)

Commercial software offerings for production scheduling are rife with terms and labels suggesting that there is a one-size-fits-all approach to production scheduling. Nothing could be farther from the truth. Learn how the technology behind OptPro can help truly optimize your production schedules by using models and approaches designed to optimize your specific operation.

02

Digital Twins for Optimal Production Scheduling

Digital Twins have garnered much attention lately, as trends like Industry 4.0, the Industrial Internet of Things (IIoT), and Smart Factories, are coming into greater focus. However, the general perception is that having a digital twin is expensive, usually entails large investments in equipment sensors and controls, and requires more time and effort than it is worth.

In this paper, we describe how OptPro uses a digital twin to aid in optimal production scheduling (OPS), while we debunk a couple of myths:

  1. That digital twins are too expensive for anyone other than the largest manufacturers to afford.

  2. That only those with the most sophisticated ERP/MES systems – including equipment sensors and integrated process control systems – are able to make effective use of digital twins.

03

Complex Production Scheduling: Models, Methods, and Case Studies

Manufacturing companies often employ processes that exhibit high levels of complexity, creating a need to identify optimal production schedules that can drive competitive advantage. These companies typically operate in an environment where production costs represent a significant portion of the total product price, multiple products share manufacturing infrastructure and resources, and production schedules are required on a timely basis.

Seeing a gap as to solutions currently available in the marketplace, our scientists developed OptPro, a sophisticated production scheduling solution approach that combines mathematical programming, metaheuristic optimization, and simulation to craft optimal or near-optimal production schedules in a reliable and effective manner.

04

Optimization in Supply Chain Planning

The latest advancements in the integration of optimization technology with evaluation techniques that model the complex supply chain environment have contributed to improved and more focused decisions to extract the most value from the supply chain. BBA offers state-of-the-art optimization solutions to address the most complex supply chain problems.

05

MetaHeuristics

A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed-integer optimization such as branch and bound and dynamic programming.