Analysis
A global supplier of commercial explosives, primarily for the mining industry, prided itself on customer service, manufacturing and delivering more than 1.2 million tons of ammonium nitrate from multiple facilities on three continents. However, they lacked any real measure of the cost and efficiency of their logistics; they were unable to track inventory levels, production volumes, key raw material purchases, and sales. Where the data existed, it was buried in multiple siloed systems.
Because of their focus on fulfillment, they had never analyzed their complex logistics (rail, truck, 3PL suppliers) to create a logistics plan that might optimize performance and improve profitability. Major decisions, such as investments in a new facility, were made without regard to supporting or contradictory data, as they lacked the required technology tools.
Implementation
Increase Profitability: Advanced data analysis identifies cost savings through transportation spend optimization, more efficient fleet utilization, reduced demurrage, and network optimization, among other improvements.
Optimize SIOP: Upleveled sales, inventory, and operations planning (SIOP) keeps budgeting, forecasting, production planning, inventory levels, and raw material purchases in alignment.
Provide Visibility: An analytical platform extracts and transforms data and provides self-service reports; coupled with an interactive dashboard, these technologies give the company near real-time visibility into logistics, operations, and costs while also freeing up their IT.
Strengthen Decision Making: A digital twin—a simulation model that gathers data in real time—allows the company to work through various network optimization scenarios to evaluate investment decisions before committing.
Results
Consolidated and standardized siloed logistics data from multiple internal and external legacy systems to:
- Provide “One version of the truth.”
- Implement self-serve analytics dashboards providing near real-time visibility into logistics operations and financial metrics.
- Develop digital twin of complex multi-mode logistics network.
- Support strategic decision making with complex scenario modelling to improve costs, service, and profitability.
- Accelerate digital maturity by years.
Conclusion
The company moved from having no visibility into logistics and costs to having the tools they needed to improve performance, determine variance to the budget, save 10% of their current costs, and increase profitability, based on clear, actionable reports they can generate at will.
The technology implemented by SGS Maine Pointe not only optimized the company’s existing processes but it gave leadership new and improved insight into strategic decisions, refining their process for evaluating investments and expansions. Given the right tools and a technology roadmap for the future, the company now has sustainable analytic solutions.
Near real-time visibility into logistics, self-serve data analytics, and digital twin model
