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Flow-Works Manufacturing Software


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Flow-Works software supports several critical functions for effective management of continuous flow (lean), mixed-model manufacturing. Mid-volume products with customer options that cause assembly crew balance variations are especially good applications.

Flow-Works enables your manufacturing staff to precisely and quickly plan complex product and process flows. It replaces imprecise spreadsheet programs you have used to augment ERP or MRP — a complex maze of simultaneous linear equations and semi-automated work routines which generate schedules that only roughly approximate reality.

Flow-Works enables you to make immediate changes to plant floor schedules in response to order changes, material supply shortages or other external demands.

Flow-Works enables you to real-time reallocate labor or task assignments in response to internal process variations such as material problems, equipment failures/substitutions, etc.


Flow-Works streamlines the planning activities for development of continuous flow manufacturing.

Flow-Works’ powerful routine planning and management tools optimize productivity in flow manufacturing operations.

Flow-Works simplifies the inter-system connections (to ERP, etc.) This allows your staff to focus on the value-added process steps, not on maintaining complex support systems.


10% to 30% lower labor cost due to reductions in daily demand variations and improvements in crewing accuracy.

5% to 15% lower labor cost from Corrective Action Tools that perform on-the-fly line rebalancing during the daily run.

15% to 30% reduction of scheduling, expediting and materials control overhead costs.


Flow-Works functions under an ERP or MRP umbrella system with MRP performing the critical role of managing the materials supply to manufacturing operations. Then Flow-Works is used to plan and manage the fast-paced transactions within flow manufacturing lines where real-time tools such as Kanban visual signals are needed to keep pace. Flow-Works linkages to the ERP system are limited to an essential few (e.g. BOM and order file downloads) thus avoiding redundancy and simplifying implementation and maintenance.


Flow-Works supports three key decision-making phases of mixed-model flow manufacturing:

Process Design Tools and Data Formats

Process Characteristics Charts (routings, sequence of events)

Bill of materials linked to work stations

Line balancing with real-time simulation

Line materials supply plans (Kanbans, picked to order…)

Operator instructions including work steps by product type

Routine Operations Planning

Demand accumulation

Demand smoothing to optimize efficiency

Work station load reports (increments as small as necessary)

Production order sequencing and synchronization

Performance Measurement and Management

Real time performance measurement

On-demand crewing recalculation for on-the-fly corrections

Balanced scorecard of key process indicators and results

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What Makes It Unique?

Flow-Works uses the power of next generation autonomous agent software to manage the millions of naturally complex product and process combinations that occur in mixed-model flow manufacturing. Its beyond object-oriented architecture provides real-time answers to management problems that conventional systems cannot supply.

Simply-stated, Flow-Works is a data manipulation tool set that uses the power of autonomous-agent parallel processing for accurate simulation and management of the millions of product and process combinations in mass-customization manufacturing operations. It is complexity theory used to simplify every-day manufacturing problems.

Next Generation Software for Complex Systems ("Chaos Theory")

Researchers who developed complexity theory (sometimes called chaos theory) learned that:

1) large scale, integrated computer systems become less effective with increases in size, and

2) decentralized systems of autonomous agents have inherent performance advantages over centrally-controlled systems.

Putting those two concepts together led to the autonomous agent computer systems concept, a leading-edge development that overcomes the limitations of conventional software to manage complex systems.

Central Systems vs. Taxi Driver "Agents"

To illustrate the agent systems concept, we can use a real-world example. Consider the taxi cab system in Chicago. The primary objectives of the taxi system are to maximize passenger utilization of available cab time thereby maximizing revenues while minimizing overall taxi system costs of operations.

Now suppose we want to install a planning system to improve overall taxi system performance. We could take one of two approaches. A traditional computer system would look at where taxicabs picked up passengers last year, perhaps take into account hotel meetings and other events, and then tell all of the cabs where to go. An alternative would be a central dispatch function with 100s of operators taking calls and then sending the cabs to a specific location.

You can guess the probability of success of both scenarios.

To be effective, the computer system would have to accurately simulate and plan for the aggregate of millions of interacting behaviors of taxis, passengers, public events, weather and other factors. But a few simultaneous equations in conventional software systems cannot accurately simulate those collective behaviors. At best, the results can be only rough approximations of the taxi system.

People have tried central systems to manage taxi operations with marginal results. In the end, taxi systems work best when governed by a few simple rules for the taxi drivers (1. Drive passenger to destination as quickly as possible. 2. If idle, drive to nearest taxi queue. 3. If the wait at the current queue is too long, drive to the next one. Etc.). Some drivers will elect to work through dispatch, some will just focus on airport runs and others will coast around town. The end result is a system of "autonomous agent" drivers that largely works.

Ants and Other Complex Systems

Many natural systems operate under "autonomous agent" rule sets that are aligned with the goals of the overall system. Ant colonies, birds, fish, human families, and community groups operate under rule sets that guide the behaviors of the individual agents for the common goals. Like the taxi system, modeling of most natural, complex systems is beyond the capability of conventional software.

Conventional ERP and MRP Systems

ERP and MRP use elaborate mazes of simultaneous equations running in fourth-generation and object-oriented languages. In spite of their complexity they are able to produce only rough approximations of the complex operations they attempt to simulate for planning purposes. Inherent limitations prevent them from accurately simulating the millions of product and process combinations that are necessary for seamless operation of continuous-flow, mixed-model production operations.

Future Management Systems for Complex Processes

"Autonomous Agent" software takes advantage of the massive power of computer hardware now available to simulate complex manufacturing processes, at relatively low costs. It uses a structure of autonomous agents, operating in-parallel to real-time simulate the inter-related behaviors of the operators, machines, conveyors, materials, orders and other significant "agents" within a mixed-model manufacturing process.

"Autonomous agent" software allows accurate simulation of real-world complex systems. With an agent-based system it is possible to test agent rule set changes quickly, and input changes on-the-fly — like, for example, adding a rule for all cabs in a certain radius and time to move during idle times in the general direction of a particular public event site.

Evolution of Computer Systems Beyond Year 2000

Conventional software suppliers have been slow to adopt autonomous agent technologies, probably because of the huge investment in their current systems. Looking ahead, and considering the pressures of global competition, autonomous agent software offers significant improvement opportunities for effective management of complex systems.


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2009, OmniCom Solutions Group