The strategic edge of network modelling & footprint rationalisation
Stephen Ottley , head of Chemicals & Energy at SGS Maine Pointe , looks at two key ways chemical companies can reduce costs in their supply chains
The chemical industry finds itself at a critical juncture . While the appetite for its products continues to surge , traditional powerhouses like the US and Europe are witnessing a decline in market share , yielding ground to emerging competitors , particularly in China . Indeed , 75 % of the largest listed chemical companies have shown year-on-year revenue declines , 65 % by 10 % or more in the past two years ( Figure 1 ).
As the industry navigates this evolving demand landscape , the imperative for strategic adaptation has never been clearer . Proactive capacity planning and operational agility emerge as linchpins in ensuring that companies not only survive but thrive in this dynamic environment .
Maintaining profit , EBITDA and margin depends on actively adjusting operations as demand rises and falls . By leveraging two critical strategies , network modelling and footprint rationalisation , chemical companies can ensure an efficient end-to-end supply chain that is both agile and resistant to the risks posed by manmade and natural disasters .
In a revamp of its supply chain network , a fertiliser producer optimised its distribution channels and upgraded its go-to-market strategies . The company established a single source of truth for end-toend visibility of the supply chain and gained real-time and long-term insight into inventory , capacity and logistical planning . Footprint rationalisation scenarios , including spatial analytics , showed it how to improve efficiency and reduce landed costs , including the costs of material handling , warehousing and shipping .
Network modelling
From raw material sourcing to lastmile delivery , chemical products should move smoothly through the supply chain . However , keeping supply chains both secure and adaptable has become harder . Like most industries today , legacy chemical industry supply chains face a host of procurement , operational and logistics disruptions and risks , including increased competition for raw materials , changing regulations , rapidly changing technology , market fluctuations and labour shortages .
Network optimisation requires visibility into what is and is not working . Often the data necessary to gain that visibility is hidden by incompatible systems , siloed
Figure 1 – Reduction in selected chemical companies ’ earnings 2022-3
Note : Source - Chemical & Engineering News functions or plants , or inconsistent key performance indicators ( KPIs ) and metrics .
Data mining and analytics can unearth that hidden information and address incompatibilities and inconsistencies . Deep learning artificial intelligence can take that information to build insight into spend , customer behaviours and profit drivers , among other important factors influencing decisions around supply and demand .
Once consistent , timely , reliable data is available , network modelling creates a near real-time digital twin of the end-to-end supply chain that reveals bottlenecks . Companies use that digital twin to understand the true cost-to-serve , optimise their warehouse and logistics network to reduce costs , improve services and optimise inventories .
The network model allows them to test out changes before they are
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