VACCINE LOGISTICS SUPPORT
Context
A vaccine manufacturer experienced explosive growth and struggled to keep up with production. The main bottleneck was the physical limitations of the manufacturing plant itself.
They asked IPOL GmbH to help them improve processes and maximize throughput within the physical limitations.
They asked IPOL GmbH to help them improve processes and maximize throughput within the physical limitations.
Approach
IPOL GmbH envisioned a generic simulation tool that allows automated creation of any client physical layout: sites, buildings, elevators, etc. These would be filled with pedestrians navigating with custom route finding.
We co-developed a powerful AnyLogic model that creates such models from data.
Client impact
IPOL experts learned advanced AnyLogic modelling techniques while serving their client at the same time.
IPOL plans to re-use the setup for future clients as it is generic enough to be applied for different industries. eduling.
PROJECT PORTFOLIO DIGITAL TWIN
Context
Goldratt Research Labs is a thought leader on “Theory of Constraints”.
They decided to apply their expertise not only in manufacturing and supply chains but bring it to project management, specifically critical-chain project management.
They decided to apply their expertise not only in manufacturing and supply chains but bring it to project management, specifically critical-chain project management.
Approach
We designed a highly functional, yet intuitive tool to load any project portfolio.
Users can analyze their current situation and simulate the future, testing different policies with real-world uncertainties.
Client impact
This tool is the most comprehensive way to plan and test your company’s projects. It has features not seen in comparable tools, specifically its ability to actually simulate future uncertainty.
SEMI-CONDUCTOR SCHEDULING OPTIMIZATION
Context
Manufacturing semi-conductors is incredibly complex, requiring dozens of steps on just as many machines.
The client relied on traditional Excel-based scheduling driven by human experience alone.
The client relied on traditional Excel-based scheduling driven by human experience alone.
Approach
I developed a highly detailed digital twin of client operations while retaining high-performance to enable optimization of schedules.
This required very advanced modelling techniques with low-level coding and assemblies.
Client impact
The initial prototype convinced the client to invest into a very large study to turn this into the new scheduling tool.
I pulled in additional resources to kickstart full development. The product is being used every day since it has been developed.
I pulled in additional resources to kickstart full development. The product is being used every day since it has been developed.
COVID-19 FACILITY SIMULATION TOOL
Context
During the Covid-19 pandemic of 2020, many offices and factories were forced to re-structure their processes around social-distancing.
The Silicon-Valley startup Yogeo asked me to create a generic tool to help solve those issues.
Approach
I created a “quasi” library of typical blocks like spaces, desks, toilets or cafeterias.
Users can drag these in themselves and link them with spatial elements to re-create any real setup within minutes.
Users can drag these in themselves and link them with spatial elements to re-create any real setup within minutes.
This is a unique approach combining logic and animation in AnyLogic.
Client impact
Yogeo applied the tool to discuss implementations with clients.
They also used the library elements to adjust my demo layout for their own client needs, proving the simplicity it provides.
To this day, the model is one of the most successful models on the AnyLogic Cloud.
They also used the library elements to adjust my demo layout for their own client needs, proving the simplicity it provides.
To this day, the model is one of the most successful models on the AnyLogic Cloud.
PRODUCT PORTFOLIO DIGITAL TWIN
Context
- Aerospace super-alloy manufacturer with complex product portfolio
- Client wanted to evaluate impact of future (simplified) product portfolio on cost, revenue and machine utilization
- 10000+ different SKUs with very complex flows across several plants
Approach
- Data-driven, self-configuring model of all plants and machines
- SKUs follow required production flows and accrue costs and revenue
- Easy setup of new product portfolio scenarios
- Innovative data visualization
Client impact
- First time client was able to see actual product flows for any group across any machine
- Recommended several future portfolios with respective impact on cost and revenues
MATERIAL DESIGN: UI LIBRARY FOR ANYLOGIC
Context
The AnyLogic simulation tool offers great simulation capabilties. However, the default model user interface (UI) is often old-fashioned and not intuitive.
Why not move to UI that we are all familiar with, i.e. modern app design?
Why not move to UI that we are all familiar with, i.e. modern app design?
Approach
Together with Goldratt Research Labs, we decided that great models benefit from good UI.
We developed the first commercial AnyLogic library where users can create amazing UI within minutes that look and feel like modern apps.
We developed the first commercial AnyLogic library where users can create amazing UI within minutes that look and feel like modern apps.
Client impact
Goldratt Research Labs now develops all their simulation tools with our Material Design library.
Other customers around the world use it every day and shorten their model design time while improving visuals massively.
INVENTORY MANAGEMENT DIGITAL TWIN
Context
Goldratt Research Labs is a leading provider in helping clients to make “better faster decisions when it really matters”.
For their clients, they use simulation models extensively to train insights into inventory management policies. The goal is always to reduce shortages and surpluses in retail.
For their clients, they use simulation models extensively to train insights into inventory management policies. The goal is always to reduce shortages and surpluses in retail.
Approach
Based on Eli Goldratt’s famous book “Isn’t it obvious”, we built a digital twin of a retail store and a distribution center.
Uniquely, the simulation “tells a story” and takes the user through a learning journey, also applying intuitive user interfaces and charts.
Uniquely, the simulation “tells a story” and takes the user through a learning journey, also applying intuitive user interfaces and charts.
Client impact
The model is used extensively in client training to educate about the advantages of “theory-of-constraints” inventory management over traditional approaches.
Recently, the model was extended to include DDMPR policies as well. Moreover, it is now a full digital-twin that clients can apply to real data and situations.
ALUMINIUM PLANT DIGITAL TWINS
Context
Two client flat-rolled aluminium plants faced dynamic bottlenecks in North America and Europe. This was caused by several issues that could often not be identified.
Several improvement ideas existed but it was impossible to analyse which one would be the best.
Several improvement ideas existed but it was impossible to analyse which one would be the best.
Approach
I designed a full “digital twin” simulation model of each plant, modelling individual coil flow through all machines.
After calibrating for 95% accuracy on historic data, we tested several scenarios to improve throughput and OTIF.
After calibrating for 95% accuracy on historic data, we tested several scenarios to improve throughput and OTIF.
Client impact
The simulation model was the key tool to decide between scenarios.
The chosen scenarios lead to an increase of throughput by 10%, a WIP reduction of over 5% and a lead-time improvement of over 1 day.
The chosen scenarios lead to an increase of throughput by 10%, a WIP reduction of over 5% and a lead-time improvement of over 1 day.
STEEL-PIPE SUPPLY CHAIN
Context
- Client manufacturer has several sites across Europe, each managing production steps for steel pipes (rolling, heat treatment, threading…)
- Current shipping is planned ad-hoc, creating large overhead
- Impossible to optimize due to complexity
- Simulation could help test new planning rules
Approach
- Create AnyLogic model re-creating production sites, current shipping strategies and cost structure
- “Train the trainer” approach: taught client resource in simulation modelling so he could help with model development from week 3
- Initial results created within 2 weeks, refined iteratively
Client impact
- Tested 8 policies and ranked by various goals
- Ranking led to further refinement of initial policy ideas. Those were retested in the model again
- Left client capable of further model work and developing new models for different problems
GOLD MINE DIGITAL TWIN
Context
- Sub-optimal performance due to shifting bottlenecks
- Client did not understand why bottlenecks appear. Classic OEE analysis cannot help here
- Client developed improvement ideas but could not predict impact
Approach
- A highly accurate mine model was designed using AnyLogic
- Including all assets (trucks, drivers, drills, crushers…), failure modes
- Uniquely, we modelled the ore and leachpad operations as well using the fluid library and chemical reactions
Client impact
- 12 man-week effort led to a highly accurate model (reproduced the past within 5%)
- Identified up to 1m tons of material loss reduction
- Predicted unexpected future bottlenecks
- Daily operational use due to self-explanatory UI design
S & OP PLANNING
Context
- S&OP (Sales and operations planning) is often done without a full understanding of the system dynamics
- Educating S&OP professionals about unexpected problems is very important
- “Serious games” can help by experiencing a virtual S&OP environment
Approach
- We built a “digital twin” of a lemonade business with suppliers, factories and DCs across Europe
- Uniquely, the model recreates individual factory dynamics down to individual bottles
- Data-driven setup that users can easily adjust at runtime
Client impact
- Used regularly for training of clients of a large management consulting firm
- Extended existing (real) “serious game” of a lemonade factory with a virtual S&OP component
- Users report on the unexpected and fun aspect of training for S&OP end-to-end
AIRCRAFT-ENGINE-FLEET DIGITAL TWIN
Context
Our client leases a big chunk of their engines to airlines, instead of selling them. All risk on downtimes and repairs is on the manufacturer.
Therefore, predicting downtimes and managing repair shop availability is crucial to their business model. A strategic and live digital twin of the aircraft fleet can help.
Therefore, predicting downtimes and managing repair shop availability is crucial to their business model. A strategic and live digital twin of the aircraft fleet can help.
Approach
We created one of the most complex AnyLogic models at the time, modelling aircraft engines down to individual screws. The model was fed from live and historic data.
Part failures could cause cascades of errors and predictive maintenance became feasible for the first time
Client impact
The tool became an integrated part of the manufacturer’s live operations.
It is still applied every day to predict on short- and long-term fleet performance.
It is still applied every day to predict on short- and long-term fleet performance.
It has helped to shape major investment decisions on overhaul facilities.
REME MAINTENANCE PLANNING
Context
The UK Ministry of Defense (MoD) sends helicopter maintenance personnel to crisis areas along with the pilots. Naturally, this creates a lot of stress, leading to a high rate of people leaving out of frustration.
Given the high cost of training, the MoD wanted to see if better crew handling could improve morale.
Approach
We created an agent-based model of maintenance personnel, helicopters, training exercises and deployments.
Personnel agents had an internal “psychology” state model of their mood, influenced by their day-to-day activities.
Client impact
We showed that rate of personnel leaving could be reduced substantially by slicing “nights away from home” into shorter segments.
The client applied the model to investigate real training schedules and upcoming deployments to apply improved scheduling.
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