Strategic sourcing is a significant challenge for the world’s largest companies. The right suppliers can speed up supply lines, reduce carbon emissions, improve service levels, and generate higher profits. Make the wrong sourcing choices, however, and not only will parts and products not arrive when needed, they’ll be more expensive, more carbon intensive, and unlikely to meet the service demands of customers.
Complex local and international supply chains demand investments in capacity but choosing the right investments can mean the difference between a sustainable, resilient business and a business that struggles to meet its own key performance targets.
Recently Cosmo Tech was approached by a Fortune 500 company with a complex strategic sourcing challenge. This global industrial actor had annual revenues in excess of €20 billion, nearly 70 production plants worldwide, and operations in more than 170 countries. The company had adopted a digital transformation strategy some years before and, as part of that strategy, had identified strategic sourcing and optimal capacity investment as key levers for change.
With an enormous global corporate footprint, the level of complexity of the strategic sourcing challenge was high. The company wanted the capability to test different strategies and scenarios against each other to identify their best options, as well as having the capability to ask ‘how-to’ questions in order to automatically generate plans to optimize three concurrent KPIs: cost, quality of service, and stock levels.
To meet the aftermarket demand in one of their main markets, the company produces both locally as well as importing from 3 other regions. The challenge was to elaborate an optimal capacity investment (minimal costs and stocks for maximal service) based on a volatile demand composed of a high diversity of products, within existing and constrained production facilities, and Cosmo Tech’s Prescriptive Simulation Twin with its capacity to holistically model and simulate the most complex systems was the obvious choice.
The first step for the company was to import their existing data into the Prescriptive Simulation Twin. Cosmo Tech’s Prescriptive Simulation Twins are capable of integrating data in different formats and from heterogeneous sources, including spreadsheets, IoT sensors, finance and budgeting applications, and more.
With the data imported, the next step was to visualize the current strategic sourcing workflow. More than just a map or web of the strategic suppliers of the company, this visualization included current suppliers in the different locations, plus the logistics links that connect them with the company, production plants, warehouses and distribution centers, local and international procurement policies, and contracting rules.
The visualization completed, configured, and verified, a baseline scenario was created. This baseline scenario could be used to simulate the ‘business as usual’ case for the company and act as a reference point for judging alternative scenarios.
With this baseline outcome identified, the company could then test alternative scenarios against the ‘business as usual’ result. Cosmo Tech’s Prescriptive Simulation Twins enable users to test an unlimited number of scenarios against each other, comparing and contrasting the results of unlimited scenario simulations. The company was particularly interested in testing what they termed ‘local preference’ scenarios, with these sourcing strategies biased towards local producers to minimize transport and logistics costs and complexity.
The company determined which KPIs were most important to them in order to judge which of the competing scenarios was the optimal one. While total operating cost was a concern, the company also identified KPIs for profitability, service (OTIF), and environmental impact. Once determined, the company could run a ‘how-to’ simulation that would identify the optimal strategic sourcing approach to meet those KPIs.
Cosmo Tech’s Prescriptive Simulation Twin allowed the user to run more than 80,000 simulations, each with 20,000 different and dynamic decision variables. Built-in optimization algorithms identified the best strategy, and then the company could compare that strategy with their proposed ‘local preference’ scenario, and against the results of the baseline scenario simulation.
These comparisons helped the company to demonstrate to internal stakeholders why their optimal solution was the best approach to adopt. The improvement over the baseline approach was clear, and the KPIs realized under the ‘local preference’ scenario could be compared with the optimal strategy that the Prescriptive Simulation Twin algorithms had identified. Transparency in how the results were found brought clarity and confidence in decisions taken for stakeholders and for the company’s leadership team.
Thanks to Cosmo Tech’s Prescriptive Simulation Twin, the team was able to identify an actionable strategic sourcing plan for the next five years. This plan would reduce their logistics costs by 10M€/year.
Find out more about how Cosmo Tech’s Prescriptive Simulation Twins are revolutionizing industry here.
