Lindsay Olive Company Thrives on Simulation

Due to a large projected increase in sales, the Lindsay Olive Company is planning to increase production at its olive processing plant in Lafayette, California. Lindsay used SDI Industry to determine which measures would increase capacity enough to accommodate the expected increase in volume. SDI Industry, powered by Extend, models the high speed/bulk flows that are typical in food industries.

Plant configuration, scheduling, and capacity planning problems are common issues in the food, beverage, consumer products, and pharmaceutical industries. In these companies, operations are usually separated into "making" and "packing" areas, where multiple products run rapidly through numerous pieces of equipment and the end product is packed at high speeds into small containers for consumer purchase.

In such complex operations, where large amounts of material undergo many process variations, the modeling approach employed is critical. Process simulation tools address the continuous nature of bulk flows, but are unable to capture the important details of the discrete products and processes. Classic discrete event applications are too slow for modeling bulk flows or the high speed of packaging operations. Thus a specialized high speed/bulk flow simulation tool such as SDI Industry, which models the flow of materials as rates rather than as discrete items, is the approach of choice.

So many olives... so many processes

The Lindsay olive plant produces consumer-size packages of olives. Before packaging, olives are processed, pitted, sorted, and filled into cans. The cans are then seamed and cooked.

While the operation sounds straightforward, these processes are actually quite complex. There are many varieties of olives and many types of equipment and processes. Each olive variety has a different set of characteristics which mandate which specific pieces of equipment will process it. Equipment requirements determine the crewing configuration and shift patterns, which in turn affect how the plant operates. Even though huge quantities of olives are processed in bulk, individual characteristics cause operational changes: some varieties of olives can be pitted faster than others; the filling operation is affected by the size of the olives as well as the can size; olive variety determines how the seaming and cooking processes work; and so forth.

A unique element of the olive industry is that all olives are harvested during a six-week period in the fall, then stored and processed throughout the year. This additional complication means inventory management and production scheduling are also critical factors.

Using simulation to deal with complexity

The simulation project manager wanted to determine current plant capacity and be able to test the effects of various changes. The models that were built examined the balance between the "making" and "packing" systems during the annual production cycle and helped Lindsay:

• Determine the impact of scheduling alternatives and crewing policies
• Explore variations in product mix
• Identify bottlenecks and under-utilized operations
• Evaluate proposed short and long-term capital expenditures

To verify that the model represented the processing plant accurately, plant engineers and the production scheduler analyzed model logic, input, and output. The logic in the pitting area was reviewed and refined based on input. The data contained in the database was reviewed extensively. As output, the model generated a custom report; this was compared with Lindsay's annual schedule to verify that the model was running as intended.

Harvesting the benefits

According to Robert Rugeroni, IS Manager at Lindsay Olive Company, "The simulation gave us an understandable representation of the large, complex plant. It allowed our managers to evaluate critical factors about our facility while discarding irrelevant details." Results of this simulation have enabled production engineers and plant managers to understand how their current system would respond to the forecasted sales. It also gave Lindsay an effective tool to evaluate scheduling changes and determine where future capital investments should be made.

The simulation has been used and accepted by all levels of management. As a result, Lindsay plans to increase the scope of the model to simulate its entire enterprise and to incorporate capital purchases.