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The challenge of modern production planning
Production planning has always been more than just a balancing act between volume and efficiency. As industries have evolved, so too have the requirements for production planning systems. Over time, the focus has expanded significantly beyond traditional metrics, introducing new complexities such as sustainability, agility, and customization. Modern businesses must navigate a landscape where these priorities, alongside cost reduction, quality improvement, and the ability to swiftly adapt to market changes, are critical to success. Added to this are the fluctuating impacts of geopolitical events, supply chain disruptions, and the rising costs of materials and energy, which further complicate the planning process.
The question is clear: What new planning methods or systems can we adopt to build resilience, respond to these challenges, and thrive in an ever-changing world?
Limitations of Material Requirements Planning in Dynamic Environments
Traditional MRP systems, while effective in stable conditions, face significant limitations in dynamic production environments. Their reliance on static data and fixed rules can lead to inefficiencies when flexibility and adaptability are required. As production goals and market demands evolve, the lack of a comprehensive cost model and the rigidity of MRP systems may hinder a company’s ability to remain competitive.
How the Production Planning Optimizer (PPO) addresses modern challenges
The Production Planning Optimizer (PPO) within the SAP S/4HANA suite is designed to tackle the complexities of modern production environments by using mathematical optimization techniques. It supports bucket-based short- to medium-term planning, enabling organizations to efficiently manage scheduling, optimize resources, and navigate various operational constraints. PPO's diverse algorithms create feasible and adaptable production schedules, particularly valuable in settings with complex material flows, multiple production stages, intricate supply chains, or highly variable demand.
PPO is most suitable when production planning must incorporate real-world constraints essential to the production process. By accurately reflecting these constraints within the system, PPO ensures that production schedules remain feasible and strategically aligned with business objectives.
Tackling complex tactical planning problems with Production Planning Optimizer
The Production Planning Optimizer (PPO) consolidates procurement, manufacturing, and distribution processes, enabling plant-specific holistic tactical planning and sourcing decisions to be made and executed based on a unified, globally consistent model. The PPO provides cost-based planning, implying that it examines all possible plans to identify the most cost-efficient solution in terms of total costs. Total costs encompass the following decision factors:
To better grasp how the Production Planning Optimizer (PPO) addresses complex planning challenges, let's dive into a specific industry.
Understanding the complexities of the consumer goods market
The consumer goods sector, particularly Fast-Moving Consumer Goods (FMCG) like food, beverages, and personal care products, is undergoing constant change due to shifting consumer expectations, digital innovation, and a growing focus on health and sustainability. Rising competition, supply chain disruptions, and increasing energy costs have further complicated the landscape by driving up manufacturing expenses and fueling inflation.
To stay competitive, companies must navigate unpredictable demand by quickly adapting to changing customer needs, managing inventory effectively, and considering factors like product expiration and evolving preferences. The digital age intensifies these challenges, as consumers can instantly compare products, making differentiation in an increasingly crowded market more critical than ever.
Managing Market Challenges: The Need for Effective Decision-Making
Building upon the preceding discussion, it becomes apparent that while flexibility in decision-making is crucial, it often encounters a significant hurdle when transitioning into actionable steps within production planning. This is because real-life industrial facilities often contend with a wide range of operational constraints and produce a variety of final products, making planning & scheduling a computationally demanding problem.
For the sake of clarity and specificity, we will narrow our investigation to the domain of beverage production. Nonetheless, it is evident that the constraints mentioned are not confined solely to beverage production but also extend to adjacent sectors.
Optimizing Production in the Beverage Industry: Managing Efficiency and Complexity
The rise of new consumer trends, such as the "sober curious" movement among millennials and Gen Z, is actively reshaping the beverage industry, boosting the popularity of non-alcoholic beer and healthy drinks. To remain competitive, companies must not only refine their marketing strategies but also strengthen brand awareness to maintain customer loyalty in an increasingly demanding marketplace. In this context, ensuring timely delivery and cost-effectiveness is crucial for satisfying customers and achieving business goals. This is particularly true in global markets like Europe, where cultural diversity has led to a landscape filled with numerous small to medium-sized enterprises (SMEs) specializing in local products or brands. For these businesses, operational efficiency is not just important—it’s essential for survival.
To meet these demands, companies must focus on effective production planning, taking into account several key factors specific to the beverage industry:
- Filling Operations: In this approach, the focus is on the filling process for beverages like bottled water, soft drinks, and juices. The main objective is to efficiently manage the filling operations to meet demand while optimizing the use of resources.
- Fermentation and Maturation: For beverages like beer, wine, and spirits, the fermentation or maturation process is an additional key factor in production. Alongside efficient filling operations, planning must also account for the timing and management of these processes to ensure smooth overall production.
Product characteristics in the beverage industry
Beverage products are generally standardized, featuring low BOM complexity and minimal service requirements. Despite this, companies often provide a wide range of variations, including different flavors, packaging formats, and promotional items. This lack of significant differentiation results in customers, particularly retailers, having more power in the market.
Factors influencing planning decisions
Managing the growing variety of products, meeting tight delivery schedules, and optimizing production processes are key to staying competitive. Successfully navigating these demands enhances resource utilization and strengthens a company's position. Planning decisions are typically organized into three levels: strategic, tactical, and operational. These processes often rely heavily on the expertise of a few key employees. However, the challenge arises when this crucial knowledge isn’t fully documented, leading to difficulties in transferring it within the organization.
In the beverage industry, the complexities of production planning can generally be divided into two main categories.
1. Demand Management
The following key elements highlight the specific areas where careful planning is essential to managing demand effectively:
2. Production Process Optimization:
- Variability in the manufacturing process with a product mix that is difficult to optimize (slow- & fast-moving products).
- Different product characteristics (flavors, container and package sizes) can lead to high setup costs if not considered during planning.
- Different types of setup (e.g. container format change vs. product change) can lead to different setup consumption (large setup vs. small setup).
- Filling lines are usually divided according to their technological aspects (e.g., filling lines for kegs are unable to fill bottles or cans) and the container materials (e.g. PET vs. glass bottles).
Main Planning constraints:
- All production steps are capacity constrained, but while reconstitution and mixing is often unproblematic, the limited capacity of filling lines is typically the major constraint of the complete supply chain
- Tank capacity, e.g. for maturation / brewing / fermentation can be a bottleneck, and fermentation or maturation times can have a significant impact on supply chain responsiveness.
- Each filling/bottling line can typically only produce a single product at any time, being adjusted to fill a certain liquid, container type and size.. Final packaging (e.g. shrink-wrapping) can also be handled by separate, parallel resources in some cases. A product changeover may involve configuration changes to the filling line and/or cleaning step.
- Some companies prefer to produce the same or similar products for entire shifts or even several consecutive shifts, especially for fast-moving products..
- Overtime, e.g. by adding additional shifts (e.g. on weekend days) is frequently used in periods of high capacity utilization.
Automating Make-to-Stock planning in Juice production: A practical project-based approach with PPO
Now that we've explored the broader landscape of production planning challenges in the beverage industry, let’s dive into a real-world example. In this section, we’ll take a closer look at how the Production Planning Optimizer (PPO) can be applied to automate make-to-stock planning in juice production. Keep in mind that what we’re about to discuss isn’t an official SAP Consulting statement, but rather insights gained from practical, project-based implementations. This approach is just one of many ways to tackle similar challenges, and there are certainly other strategies that could be effective as well.
Consider a practical scenario in which a fruit juice production company seeks to automate its make-to-stock planning process using PPO.
The production process consists of two main stages:
In dedicated tanks, raw juice concentrates are combined with liquid flavors, sugar, and water in a specific sequence. The juice is then transferred into various containers—such as cans, glass bottles, kegs, and plastic bottles—via a filling line. This line includes conveyor belts and machinery for washing, filling, sealing, labeling, and packaging.
The planning primarily focuses on the bottling and packaging process, where the juice is filled into containers of various sizes. The production line, comprising a series of conveyor belts and machines, is responsible for cleaning, filling, sealing, labeling, and packaging these containers.
Areas of concern before introducing the PPO:
- While detailed scheduling took place in SAP (PP), the rough-cut production planning took place in a non-integrated spreadsheet system
- The customer had the requirement to schedule specific products / product groups based on recurring weekly/monthly production patterns “Production campaigns”
- Complex combinations of push and pull production strategies, which are based on the utilization of rounding values and the integration of forecasts.
- A lack of systematic synchronization in determining the ideal production batch sizes, which should consider the limitations and availability of both raw materials and intermediate bulk goods.
- Complex production network with preferred storage / pre-build products (fast movers)
Generating bucket-finite plans with consideration of capacities, costs, and calendars
The customer needed a production planning calendar that could outline the scheduled production for various product families on a weekly basis. Instead of defining exact quantities upfront, the focus was on reserving certain production capacities for specific weeks—essential for their role as a subcontracting manufacturer. Beyond just a weekly pattern, there were also daily specifications for certain products that needed to be factored in. To meet this need, we introduced a planning calendar within the PPO using a feature known as 'production bounds'. Although it involved a small adjustment to the BAdI code, this addition was key. 'Production bounds' allow planners to set specific constraints—like minimum and maximum limits—on a product-by-product basis for each time bucket. This ensures that the production schedule is aligned with the batch sizes defined in the calendar.
The image below illustrates the planning calendar—each column represents the permitted product group that can be produced in each planning bucket.
Prioritizing sales orders and promotions: Leveraging penalty costs to Optimize demand fulfillment
A key aspect of production planning often involves prioritizing certain promotions, especially when launching new products into the market. This is where the concept of penalty costs for different demand classes comes into play. Essentially, it allows planners to assign different priority levels to customer orders and forecasts, ensuring that high-priority items get the attention they need. For example, sales orders might carry higher penalties for non-delivery or delays compared to forecasted demands, ensuring that customer commitments are met first.
Hint: The associated penalty costs are directly entered in PPO within the S/4HANA material master under the Advanced Planning tab. By default, PPO comes with two predefined category groups, but customers have the flexibility to create their own penalty cost groups.
In the below screenshot - you can see some example values (not related to the specific customer project)
Balancing Push and Pull strategies in the beverage production - optimizing raw juice distribution and capacity planning
One of the key planning challenges was figuring out the right quantities for each product while considering the planning calendar, available production capacities, promotional activities, and the preferred storage of fast-moving items. In the beverage industry, this often involves managing the delivery of raw juices, like orange juice—an essential ingredient in many juice variations—delivered on a set schedule, typically weekly, by tank trucks.
The planners' job is to determine how to allocate these raw juices across various semi-finished and finished products. A priority system guides this process, which can be seen as a balance between pull and push strategies. Promotions take precedence, so the pull principle is applied here—ensuring that customer orders and promotional needs are met first. However, it's not as simple as dividing each truckload of raw juice equally among all orders and promotions.
Once customer orders are fulfilled, the remaining raw juice is then "pushed" to produce fast-moving products, which are the second priority. The animation below illustrates this planning approach: products with customer orders pull in the necessary raw materials first. After all customer orders within the planning horizon are satisfied, the push principle kicks in, distributing the remaining quantities to preferred fast-moving products.
This requirement can be effectively addressed by leveraging penalty costs for storage within the PPO. By setting higher storage costs for raw juices compared to fast-moving products, you create a scenario where the optimizer finds it more cost-effective to produce and store finished goods rather than keeping raw juices in storage.
The animation below illustrates the push and pull principles as represented in the cost model (see previous screenshot). While the material flow itself is of course consistent, the direction of information flow and the resulting priorities differ. Through the cost model, it is ensured that customer orders are fulfilled first, with remaining resources then allocated to fast-moving products.
So far the optimizer had quite a bit of freedom in determining the quantities to produce. However, in the real world, once you decide to manufacture a product, there’s often a need to produce a minimum lot size to justify the setup of machinery and equipment. In our scenario, we didn’t want to enforce minimum quantities for every single product, but rather for specific production patterns.
This means that if the optimizer chooses to produce something from Pattern X, it must hit a minimum quantity threshold. The twist is that this requirement can be fulfilled by any product within that production pattern, offering some flexibility.
The image below further illustrates the concept of minimum cluster lot sizes. Some raw materials can be used to produce different intermediate products. Now, planners have the flexibility to set not only product-specific minimum lot sizes but also cluster-related lot sizes. This requirement can also easily achieved with the production bounds mentioned earlier.
Summary: The measures outlined above ultimately allowed the customer's planning problem to be resolved optimally, without any compromises.
It’s important to note that the aspects discussed here represent only a fraction of the modeling capabilities offered by the PPO.
Lessons Learned
This blog demonstrated how advanced tools in the S/4HANA suite help businesses tackle complex production planning challenges with efficiency. The project example of a mid-sized company without a dedicated optimization department shows the maturity and seamless integration of S/4HANA solutions in real-world processes. A key advantage of S/4HANA is its fully integrated planning within a single system, using a centralized material master that eliminates the need for complex external integrations or data mappings. Instead, it relies on a standard data model that can be easily extended for specific needs.
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