David J. Parsons

SDI Supply Chain Builder: simulation from atoms to the enterprise

The SDI Supply Chain Builder Product Suite is a high-level simulation toolset that provides solutions to enterprise problems. The product suite contains four specific elements for enterprise modeling: SDI Supply Chain Builder for supply/distribution chains, SDI Plant Builder for multi-stage plants driven by schedules, Extend+Industry for high-speed, high-volume production line modeling, and the SDI Data-Framework for high-speed data import and export. These elements can be expanded for use in modeling of smaller projects such as a single packaging line to large-scale projects such as a worldwide supply chain.

The SDI Industry Product Suite: simulation from the production line to the supply chain

The SDI Industry(R)Product Suite is a versatile, high-level simulation toolset for solving problems of whole enterprises. It adds important capabilities to an existing simulation package, Extend/sup TM/, which provides a robust simulation architecture and a wealth of existing building blocks. The SDI Industry Product Suite contains 5 specific elements for modeling the enterprise: SDI Database for high-speed data import/export; SDI Industry for high-speed, high-volume production line modeling; SDI Plant Builder for multi-stage plants driven by schedules; and Supply Chain Builder for supply/distribution chains. Each of these elements can be built upon to model everything from a single packline to a worldwide supply chain.

A supply chain case study of a food manufacturing merger

A large food manufacturer recently decided to merge with another food manufacturer of similar size. The companies anticipated dealing with complex issues of combining their operations and supply chains. The companies decided to use simulation as an analysis tool for the merging of their supply chains. This paper presents a case study of the simulation study and the results.

Non-item based discrete-event simulation tools

Discrete event simulation has traditionally been defined by items (or entities). This modeling paradigm has served the simulation industry well, but falls far short for many industries in which the parts/pieces mindset simply does not accurately portray their particular processes. For the last ten years Simulation Dynamics has been working with industries where the item paradigm falls short as a descriptive tool. This work has led to the development of a revolutionary set of simulation tools built on the Extend simulation engine.

Tactical logistics and distribution systems (TLoaDS) simulation

In response to changing threats from opposing forces that often result from use of enhanced technology, US forces must adopt new tactics, employing appropriately upgraded delivery equipment to deliver rations, fuel, ammunition, personnel, and repair parts to forces in forward areas. In the face of sharply reduced R&D budgets, the opportunity to explore new tactics and to test and evaluate new logistics material delivery equipment is correspondingly diminished. In addition, the evaluation of new tactics through trial maneuvers employing seagoing forces and simulated troop landings is often frustrated by noncooperative weather, the high operational expense of mounting a full-blown sea force and the typically inconclusive nature of the data collected. However, through the use of simulation, inexpensive, innovative force deployment and positioning schemes are tested. New supply distribution techniques employing a wide variety of equipment combinations both existing and experimental are also tested. The simulation output data is used to grade distribution schemes. This provides ranges of vehicle engineering data which may impact and support subsequent equipment design parameters.

Production scheduling validity in high level supply chain models

Although they focus on the big picture, high level supply chain models cannot gloss over the capacity of production nodes to meet production allocations. Capacity is not simply a reflection of equipment production rates. Short runs drive down utilization by increasing total time lost to changeovers. Multistage plants require coordination of capacities at the several production stages. In short, production capacity is crucially affected by the way production runs are scheduled through plants. Modeling actual scheduling practice is often unrealistic, since methods vary from plant to plant, and involve a blend between planned schedules and on-the-fly adjustments. This paper suggests that there is a range of approaches to modeling production scheduling. In the modeling of supply chains, modeling alternatives must be assessed in terms of cost of development and implementation versus validity.

Impact of production run length on supply chain performance

This paper documents an experiment designed to show the value of simulation in understanding the relationship between production run lengths and overall supply chain performance. Current production practices and supply chain policies of an existing company provided the starting point for the experiment. The experiment consisted of two deployment scenarios and a range of run length multipliers that vary the companys actual run length rules. Minimum cost run lengths were determined for twelve combinations of cost assumptions for changeovers and inventories.