Material and Production Planning in Chemistry
Why Excel Reaches Its Limits with Conflicting Goals
A high-priority order comes in at short notice. The desired product is important to the customer, but the ongoing campaign in the plant has already started. Should production be brought forward, even though this threatens additional changeovers, more off-grade material, and new gaps in the remaining schedule? Or is it better to first deliver from stock, thereby depleting safety stocks more quickly?
It is precisely in situations like these that it becomes clear why inventory and production planning in chemistry is not just a calculation problem. Service level, stock levels, plant utilization, and capital commitment are closely related. What might appear as a series of individual decisions in a table is, in practice, a cohesive conflict of objectives.
Inventory often shows a coordination problem
In planning, larger inventories are often understood as a safety net. However, in many chemical companies, they are more of a sign that decisions about production, supply, and service are not cleanly aligned. If safety stocks are increased in isolation, it improves delivery capability in the short term. However, at the same time, capital commitment increases, and the actual cause of uncertainty remains.
For CFO-related issues, this is precisely what's crucial: More inventory doesn't automatically create more robustness. It can also be an expression of planning that doesn't resolve objective conflicts but merely buffers them. This then ties up capital without making operational control truly more stable.
„What looks simple in Excel collides with conversions, off-grade, and bottlenecks in the plant.“
The apparent freedom in Excel does not exist in production reality.
In chemistry, planning is rarely freely movable. Before a plan is adjusted, fixed or preferred batch sizes, sequence-dependent changeovers, off-grade losses during product changes, tank and storage limits, bottleneck equipment, raw material availability, and delivery windows already have an effect. Any change in one area causes consequences in several others.
This is especially true when an ongoing campaign is to be interrupted to pull a product forward. In Excel, this decision can often be quickly visualized: pull forward a quantity, reduce inventory, secure a deadline. The consequential costs become less visible. Additional retooling consumes capacity, changeovers create scrap or off-grade material, downstream products face new risks, and the inventory impact shifts over several periods. The table then shows a decision, but not necessarily its full consequence.
Disruptions make separate planning particularly vulnerable.
As soon as a facility fails in the short term or a raw material is delayed, the coordination effort increases exponentially. Then inventories, production quantities, delivery commitments, and often cross-site flows must be re-coordinated. In separate tables, this is not only slow. It also easily creates version conflicts and a false sense of security, because individual tables appear consistent, but the overall plan no longer fits together.
Especially in industries with limited leeway, the quality of coordination itself becomes the crucial lever. Where buffers in plants and tanks are scarce, it becomes particularly apparent whether planning was truly conceived cohesively or just maintained in parallel.
Further interesting content
Thinking about inventory and production planning together instead of separately
The crucial step is not to plan inventory, production, and service using separate logic. Instead, they are evaluated jointly. The question then is no longer just whether an additional inventory increases delivery capability or whether a campaign can be technically postponed. What matters is which combination of safety stocks, production quantities, and service targets is feasible under real-world constraints.
Mathematical optimization accurately models this joint decision-making situation. It doesn't evaluate individual key figures one after another, but rather the objective conflict as a whole. This makes it clear when more inventory makes sense, when a campaign change becomes too expensive, and when a lower service level for individual products can be the more robust overall decision.
„Integrated optimization turns individual decisions into a robust overall plan.“
How a resilient plan emerges from many dependencies
For this, the relevant levers and limits are brought together in a model: production quantities, inventory targets, capacities, lot sizes, setup logic, warehouse limits, material availability, and delivery requirements. This holistic view does not create a theoretical ideal plan, but rather a plan that takes actual operational constraints into account.
When a product is prioritized, it not only shows the volume effect but also the consequences for service, inventory, and capacity. If a bottleneck system fails, it becomes clear which products are delayed, where pre-production makes sense, and at what point additional inventory helps more than further interventions in the process.
A more realistic approach to conflicting goals
This is particularly important for chemistry because planning can rarely be reduced to a single optimum. A viable plan not only makes it clear which option is computationally attractive but also which priorities are actually feasible and understandable within the company.
This makes it clearer why certain decisions were preferred, which restrictions were decisive, and which side effects were consciously accepted. This is precisely what makes integrated planning not only operationally more resilient but also more adaptable for management and cross-departmental coordination.
„What's crucial is that integrated planning isn't just analytically convincing, but also practical in everyday life.“
From Abstract Logic to Practical Application
OPTANO helps companies bring this integrated view into their planning. For inventory and production planning, this means: safety stocks, production quantities, and service levels are not treated as separate levels, but planned in context. This allows for more robust evaluation of planning adjustments, clearer classification of bottlenecks, and earlier identification of conflicting goals.
This is particularly relevant in situations where local decisions would worsen the overall plan. Maximizing the utilization of a single plant or universally increasing safety stock might seem sensible at a sub-area level. However, within the overall system, this often leads to new bottlenecks, additional changeovers, more off-grade products, or unnecessary capital tie-up. Integrated optimization logic helps to make such shifts visible early on.
What benefit arises from this in everyday life
The practical benefit lies less in spectacular individual results and more in improved planning quality. Decisions become more consistent because relevant dependencies are considered jointly. Ad-hoc interventions decrease because the consequences of plan changes can be assessed earlier. At the same time, it becomes more transparent where existing resources truly provide protection and where they merely mask a lack of coordination.
For companies in the chemical industry, this is also a financial question. Those who plan service, inventory, and production together create better conditions for stable supply capability and a more conscious use of working capital. This very connection makes the difference between mere plan continuation and resilient control.
Where Excel ends, the real decision logic begins
The replacement of Excel in chemistry doesn't start with more computing power, but with a different perspective on planning. As soon as service, inventory, and production have to be decided simultaneously, it's no longer enough to keep individual spreadsheets clean. Then, a logic is needed that makes conflicting goals visible and leads to a viable plan under real-world constraints.
The decision described at the outset, to prioritize an important product or protect an ongoing campaign, is therefore more than an isolated case. It demonstrates on a small scale what is important on a large scale: not isolated planning values, but a coordinated decision about delivery capability, effort, and capital commitment.
If you want to explore how integrated inventory and production planning can be implemented in your company, it's worthwhile to have a professional discussion with OPTANO. Together, you can specifically identify which conflicting goals are causing the most effort in your planning today and how they can be managed more systematically.
Key Takeaways
- High inventory levels in chemistry are often not just a safety reserve, but an indication of inadequate planning.
- Excel can model individual decisions, but reaches its limits when service, production, inventory, and restrictions must be evaluated simultaneously.
- Relevant boundary conditions include batch sizes, changeovers, off-grade, inventory limits, bottleneck machines, and material availability.
- The key advancement lies in integrated planning that jointly optimizes safety stocks, production quantities, and service targets.
- This makes conflicting objectives more transparent, interventions more robust, and working capital issues better linked to operational reality.