Contact Lanner

Complete the form below for a quick response from a Lanner representative or call us on +44 (0)1564 333 300. If you require support, please visit my.lanner.com

Thank you for your enquiry.
A member of our team will be in touch as soon as possible.

Submit

Ethical Principles in a Commercial Corporate World

A fundamental overriding challenge for Mars is driving efficiency throughout the business whilst maintaining a core corporate principle of manufacturing chocolate products in the local markets where they get consumed. This is often sub-optimal from a purely commercial perspective, thus it is even more critical to ensure the business performs in an optimal manner.

Mars have 29 product lines running across six sites in the USA, each with varied chocolate making capabilities and chocolate consuming requirements. Add in multiple chocolate types and varying product mix and demand, the internal supply chain for chocolate is a complex one.

Also, Mars’ focus on quality prevents it from bulk storing chocolate products long term, which presents a fulfilment challenge when demand peaks. Therefore accurate capacity planning is fundamental to ensuring the chocolate supply process is capable of meeting the demands of the business.

Thus when the Supply Chain team were asked to provide the business with assurances that significant future finishing line investments could be satisfied from a chocolate making perspective, an initiative was deemed necessary to assess and refine capacity levels across its network in order to achieve greater confidence in such business planning.

As part of the project, Mars quickly identified that the scale, complexities and interdependencies involved in capacity planning across the six sites would require modelling capabilities that were more sophisticated than those within its existing spreadsheet based capacity planning systems.


Predictive Simulation has an exceptionally high ROI and proves that you don’t have to engage the likes of McKinsey and spend $4 million a year to improve business performance.

Scaling up to the Challenge

Before turning to predictive simulation, Mars had limited detailed insight into the relationships and variability of the chocolate production and consumption processes, lacking truly reliable forward visibility of supply chain performance and quantifiable terms of reference for accurate capacity planning.

“Static modelling works well in certain areas, but we needed a tool which could explore high volumes of what-if scenarios and plot multiple answers / ranges to achieve an in-depth, contextualised understanding of our six sites, based on variable capacity, demand and product mix” 

“Having used WITNESS predictive simulation software from Lanner in a number of areas across our global operations – primarily for line level loading, supply chain and packaging process modelling - we had that WITNESS was the best solution to support this project.”

“WITNESS models complex scenarios and delivers exceptional levels of clarity. If your model is well designed, it can effectively be future-proofed to be used again and again over time as a key strategic asset, extending the ROI substantially.”

Paul Myler, Director of Supply Chain Strategy & Industrial Engineering, Mars Chocolate North America.

Model Behaviour

Recognising the need to partner with an experienced business modelling team, Mars worked closely with Lanner to create a WITNESS simulation model that predicted supply chain performance based on different chocolate producing and consuming configurations and product mixes across the different sites. A key focus of the model was understanding the impact of key strategic tradeoffs, for example whether it is better for the business to make all chocolate types at the location it is required, or build fewer larger chocolate making facilities and ship products across the country. Mars also holds a corporate principle that to deliver the best quality chocolate, it is important to manufacture as close as possible to its customers. The simulation model could quantify how best to work within such parameters.

The use of predictive simulation has provided Mars with insight into its existing and planned future operations, identifying current risks throughout the supply chain and highlighting opportunities for cost savings and performance improvements. In turn this has provided foresight to build business cases for new facilities and justify investments in chocolate making capacity, thus ensuring the right investments are made at the right time.

“The WITNESS model generated a five year view, and whilst the initial runs confirmed that capacity was fine for the next two years, after that point we would be likely to experience supply shortages if not addressed,” Myler continues. The model highlighted how to overcome the shortage with minimal additional investment, and is now run every six months and feeds into the company’s Capital Allocations Budget to assist business planning. “Essentially it means that we always have visibility over our long term planning timeframe of the timing and level of funding required for new chocolate capacity to meet the demand of current and future facilities.”

The best example of how the WITNESS model has assisted long term planning at Mars is at the new production site in Kansas scheduled to open in 2014. Clearly such a new facility represents major capital expenditure, so Mars robustly interrogated their WITNESS model to ensure that exactly the right level of investment was channelled into chocolate making assets to meet this new demand.


Loading blog comments..

Post a Comment

Thank you, your comment is awating approval
Submit

About

Headquartered in Hackettstown, New Jersey, United States, Mars Chocolate is one of the world’s leading chocolate manufacturers and employs more than 16,000 associates across 21 countries. The company owns many key brands, including five billion-dollar global brands - M&Ms, Snickers, Dove/Galaxy, Mars/Milky Way and Twix. Other leading brands include Maltesers, Revels, Bounty, Balisto and 3 Muskateers.

Latest News

Categories