For chemical companies in Europe, supply chain management (SCM) offers untapped opportunities to differentiate themselves from the competition. In part two of our series, we take a look at the role digital solutions play in this.
Digital solutions for the supply chain
The strategic business unit model and differentiated supply chains require supply chain management (SCM) to develop business-specific approaches. On the other hand, growing customer and product portfolio complexity and increasing cost pressure seem to call for the harmonization of processes and the exploitation of synergies. How can SCM manage this dilemma? This is exactly where digital innovations, which can aid the achievement of previously contradictory goals, come into play. However, these can only be successfully applied if the importance of the SCM function in the chemical industry is reappraised and new ways of transforming supply chains are explored.
Managers in chemical companies definitely sense the potential offered by digitization, especially in the supply chain. A survey of chemical managers carried out by CAMELOT found that 92 percent of them believe digital solutions will have the greatest impact in supply chain and logistics. The potential in sales was rated about the same, while all other company processes were far behind.
Digital solutions can help strike the balance between synergies and managing increasing complexity, giving chemical companies a competitive edge. They can cope with ever increasing amounts of data, meaning results can be delivered ever faster and can be presented transparently and visually. This empowers targeted decisions of a strategic, tactical and operational nature, despite growing complexity in customer and product portfolios and increasing uncertainty. We will illustrate this with three examples.
AI-enabled Demand-Driven Supply Chain Planning: Due to its position in the value chain, the chemical industry is particularly exposed to what is called the ‘bullwhip effect’, i.e., the overlapping of fluctuations in demand at all subsequent stages of the value chain. Since capacity utilization is a priority due to capital intensity, lead times to customers are usually non-negotiable, and forecasts do not anticipate or even exacerbate the bullwhip effect, which ultimately results in excessive inventory levels. This is also a reason why chemical companies exhibit often unsatisfactory working capital ratios.
In Demand-Driven Supply Chain Planning, planning is no longer driven by (unreliable) forecasts; instead, it responds to actual demand signals from the market, using inventory levels at certain points in the value chain as an indicator and buffer. This requires innovative planning solutions that go beyond the MRP used to date (e.g., through AI support) and are now also available in standard solutions. Again, experience confirms the benefits for companies to which this concept can be applied: A maximum of 60% lead time reduction was achieved with 52% lower inventories – an extreme example of how the balancing act mentioned at the beginning was achieved.
Demand analysis with artificial intelligence or demand sensing: Demand-Driven Planning cannot be introduced in all chemical companies, for example where circumstances of production or market form entail a push model. In this case, forecasts continue to retain their full significance. But even in a Demand-Driven model, forecasts still play a role in determining planning parameters. Adopting Demand-Driven Planning is a major transformation, before which smaller steps must be taken. These include, for example, an estimation of what forecast accuracy is possible at all. Using innovative AI-powered analysis tools (e.g., the CAMELOT ) that identify patterns in the data, demand can be divided into segments to determine optimal forecast accuracy for each segment and subsequently determine planning strategies.
Using this as a basis, the forecast quality can be improved using artificial intelligence, both on a tactical and strategic level (demand sensing).
Value chain analytics: Many potentials can only be leveraged when information from the supply chain (such as capacities) is linked with market, customer or supplier data. Managers in chemical companies have to make a multitude of portfolio, allocation and pricing decisions along the value chain in order to optimize the overall result in terms of EBITDA margin or ROCE/ROIC. Which products really give me a profitable result with which customers? How does this change if certain input factors (e.g., intermediate prices) fluctuate? Which product should I produce at which location in my network in order to best supply customers in terms of costs and lead times? Which use is more profitable for intermediate products, further processing into downstream products in your own company or selling on the external market? How will profitability change if I change pricing models and formulas and expect customers to react to them in certain ways?
Today, these decisions are either not made systematically, but rather on the basis of ad-hoc collected, often controversially discussed and prepared data from different sources, in time-consuming coordination of various corporate functions. Or there are isolated, sometimes ingenious IT tools, developed over years of work by an expert in a business area, which even give the individual business a competitive advantage, but whose scope ends at the boundaries of the business area. They are often not known across the entire company and expire with the professional life cycle of their creator.
There is enormous scope for application for advanced simulation techniques in the chemical industry. The necessary data is also for the most part available. In the final state, digital supply chain models can be developed into a digital twin or avatar of the supply chain, opening up completely new perspectives. In our opinion, value chain analytics will prevail in the chemical industry, because the potential is enormous, and the cost-benefit ratio is extremely favorable. For example, a solution for margin management in a complex product flow (Verbundstruktur) enabled a margin improvement of 0.5 percent (in absolute terms) at a cost of less than 500,000 euros. Such solutions exceed the limits of conventional SCM, but precisely because of this they force a better dovetailing with other corporate functions.
Digital solutions pose the organizational question
What all the described solutions have in common is that they can only be implemented with difficulty in the supply chain management of a larger chemical company, which is traditionally structured according to business areas, or that they do not develop their full effect:
- Both value chain analytics and Demand-Driven Planning require skills and resources that a single business unit will struggle to build.
- Demand analysis also often requires looking at upstream products that are produced by another business unit.
- Value chain analytics only unfolds its full potential when an entire value chain is covered across business unit boundaries, enabling decisions to be made for the benefit of the company as a whole.
- In order to improve the cost-benefit ratio, the know-how gained from the implementation of an innovative technology in one business unit should be made available to other units in the chemical group.
- All innovative technologies also require a changed understanding of the role of supply chain management in chemical companies: The focus must shift from short-term elimination of disruptions in the supply chain (“firefighting”) to the planned development of improvement potential.
It will therefore not be possible to successfully implement supply chain innovations without a change in the organization of SCM, for example by forming company-wide competence centers or by grouping business units with similar supply chain requirements into divisions or segments. But to what extent will these changes alter the fundamentally decentralized nature of supply chains themselves, which have dominated for over 20 years? This will be the topic of the third part of the series.