- What are a few of the key supply chain challenges the chemicals industry faces?
- What is the impact of globalization on chemical supply chains?
- What are some of the steps companies we work with are taking to be more resilient and agile in the face of these challenges?
- What does the future hold for the chemicals supply chain?
In the chemicals industry, complexity is ubiquitous. To help shed some light on the current state of the chemicals supply chain and gain insights into the steps leaders in this space are taking, we recently spoke with Matt Hoffman, VP Customer Success at John Galt Solutions. In this Q&A, Matt talks about the challenges chemical supply chains face today and shares what he sees as future trends shaping supply chain planning strategies.
A big challenge in the chemicals industry is that a lot of the raw materials are commodity-driven, and sudden fluctuations in the price and demand of these commodities have a considerable effect on a company’s bottom line. Greater visibility and the capability to predict demand for those raw materials helps planners make smarter, data-driven decisions. Then, these changes can be integrated into their resource planning to make the required adjustments in their supply chain.
Demand volatility is just one side of the equation. Companies must also ensure visibility of sudden changes in prices of raw materials and other commodities. Timely visibility means, for example, the team can re-negotiate contracts. With a holistic view of the supply chain, teams can stay ahead of rising costs and sustain a healthier bottom line.
Chemical companies, similar to the pharmaceutical sector, require a solid understanding of how much raw material, and the grade, they need to purchase in order to meet demand. When it comes to selling their products, the cost also impacts the demand. Traditional time-series forecasting won’t cut it here, and subjective approaches fail to capture key drivers, especially at the granularity needed for purchasing and production. Instead, causal forecasting, such as taking into account macroeconomic factors and actual price point data from commodities, is critical for their efficient demand and supply planning. The use of causal forecasting also supports demand-sensing, allowing best-in-class companies to pivot supply and raw material hedging strategies to optimize profitability.
Many chemical companies have far-reaching networks with multiple divisions and business units across several countries. We often see vertical integration is a very important strategy; they manufacture and sell products / materials to other divisions. Multi-enterprise integration and the ability to share data across different tiers of the supply chain is critical.
Unfortunately, many chemical businesses operate multiple systems across divisions or rely on their ERP to drive visibility. Both approaches create silos that inhibit the ability to bring everything into one coherent plan, where changes in one area propagate across the supply chain and collaboration is streamlined among internal and external stakeholders.
These global networks deal with massive amounts of data, which must be carefully and quickly analyzed to drive smart supply chain decisions. A siloed ecosystem creates barriers to handle all that complex and varied data – including information on pricing of raw materials and other commodities, agreements with clients and customers, manufacturing contracts, regulations and compliance requirements from government and different regulatory bodies, and more.
An intelligent, digital ecosystem ensures critical information is shared across the supply chain, and that every piece of data is appropriately placed, processed, and leveraged for valuable business outcomes. This helps companies, for example, make the most of accurate data to determine end-to-end costs, from manufacturing to procurement, and delivery of their products.
What are some of the steps companies we work with are taking to be more resilient and agile in the face of these challenges?
Drivers for demand as well as supply-side constraints are often seasonal and/or weather-related. The challenge is determining the impact of these changes. Let’s take a forecast for example. Traditionally, a planner might use regression at an aggregate level to develop a forecast. You get a higher-level view of demand for a given product at the global or regional level. The most effective supply chains get much more granular. Using machine learning they can process that data and find the insights necessary to achieve very granular levels of detail – look at each location or node across the network. In another example, we work with companies with hundreds of warehouse locations across the globe. By getting very granular, they are not only planning at each location but also looking at how each location impacts the rest of the network and finding patterns that aren’t apparent. That is a lot of data to crunch.
Taking the warehouse example a step further, with inventory, planners should be moving beyond simple ABC / XYZ stratification to set time-phased inventory targets based on seasonal ramp-in/ ramp-out, as well as product lifecycle. Focus on leveraging statistical safety stock that accounts for demand and supply volatility. Now, they can flex that safety stock throughout the year and the season, by location to strategically look at the form and function of inventory at every node of the network – also known as multi-echelon inventory optimization (MEIO).
There will continue to be a push for more integration across the digital ecosystem, especially across manufacturers that use chemical companies’ products. We are seeing some of our customers bringing in production schedules from their downstream customers, so they can assess and plan production to drive the usage of their products. We’re also seeing more users that are providing their suppliers with raw material needs to shorten the overall lead time. For example, one of our customers is providing forecasts to its vendors for six-month demand, allowing suppliers to purchase the raw materials that they need and have a quicker turnaround for making the product. Having the stock from purchased orders, and the planned capacity to meet demand, users are able to shorten the overall lead time across the supply chain.
In addition, there is a strong focus on sustainability. Companies are striving to understand how their supply chains can become more efficient in areas such as manufacturing, transportation, product logistics and operations – so they can track and reduce their carbon footprint. At John Galt Solutions, we're helping leading companies with their sustainability targets by gaining visibility and translating data, for instance, from transfers between locations to understand their carbon emissions and optimize to help lower their carbon footprint. Our platform also assists users with more sustainable sourcing practices; they can, for example, identify preferred suppliers that can provide better grade chemicals to decrease the generation of pollutants. Our ability to provide visibility to hundreds of KPIs allows complete control and tracking of sustainable sourcing rates, carbon footprint, etc.
We can also see that the ability to create digital models, a.k.a. digital supply chain twins, will become more prevalent for supply chain management teams. We’re seeing more and more customers taking advantage of the Atlas Planning Platform’s digital models across their end-to-end supply chain to gain greater visibility across the network. This allows them to create a digital replica of each location, including storage units, warehouses, manufacturing facilities, transportation channels, etc., to quickly model constraints, changes in demand, supplier disruptions, and more – all crucial for planning.
Finally, advanced capabilities in workflows, automation and machine learning will take an increasing role in forecasting for more robust planning. Some of our customers are already making the most of machine learning for getting localized analytics for prices of materials, which can impact tremendously their operations – and through automation, they can correlate data with local regulations and gain valuable insights for more effective demand planning strategies.