Mega Alimentos is a major Mexican manufacturer and distributor of savoury sauces, condiments, beverages and salad dressings, including very popular brands such as La Botanera, Pikolín, Chamoy Mega and Star Value. In total, about 250 individual products are shipped from a single plant location in Monterrey, Nuevo Leon, through 6 regional distribution centers to approximately 650 customer locations in Mexico and parts of the US.
The company has experienced strong sales growth in both the U.S. and Mexican markets and this trend is expected to continue, with US sales increasing quite sharply. The management team felt that these future growth projections warranted a serious review of their current supply chain configuration, including the possibility of adding manufacturing capacity at a second location outside their base in Monterrey. Technologix conducted the study supported by our local associates at Celogis, modeling and analyzing different scenarios with Opti-NetTM, our supply chain modeling and optimization platform.
The Mega Alimentos study followed a very similar pattern to most supply chain design and optimization projects we have conducted over the past 25+ years:
1. Getting the data into shape is the toughest task
Supply chain design and optimization models are voracious consumers of input data – production costs, forecasted demand, all the relevant capacities (production, storage, transportation), sourcing, freight and so on. You typically find numerous data elements to be extracted from various sources and in different formats, units of measure, currencies and levels of aggregation. This data invariably has to be manipulated and organized to reflect both the computational needs of the model and the level of aggregation of the scenarios to be run (time units, product groupings, demand, etc.) .
It doesn’t matter where you are physically located, the industry you are in or the size of your operation: Depending on the complexity of the data transformations required, getting your data ready will consume between 50% and 70% of the time spent on your project. Running and analyzing the scenarios is the fun part!
2. You will always find SOMETHING unexpected ‘under the hood’
It rarely fails. It could be an unforeseen gap in the input data set or a surprising finding coming out of the scenario runs, but chances are something unexpected will come up at some point in your study. Since Opti-NetTM is configured for each individual client and project, we are easily able to adapt and adjust and therefore this becomes less of an issue for us. But it surely makes things more interesting for everyone involved!
At Mega Alimentos detailed transactional shipment and freight data was extracted from different sources and matching customers, volumes and prices with the corresponding freight data took some effort. Once the data was ready, we ran a number of scenarios to evaluate different plant and warehouse configurations. We chose to analyze the various scenarios and configurations from a profitability (instead of the typical cost) perspective. This uncovered some interesting results in several areas of the country. The results suggested that a new plant in central Mexico could generate significant cost reductions. We are about to embark on a second, much more detailed phase of the study to validate these findings with a specific new plant location in mind.