Master Production Schedule in the Consumer Product Goods Industry: Benefits of APS Applications

Abstract

The presented article deals with the application of tools to calculate the Master Production Schedule (MPS) in the production of beverages (consumer packed goods) and its impact on the inventory profile in finite or infinite capacity. The calculation of MPS can be performed at the level of an ERP solution or with dedicated standalone planning software. The first part of the manuscript defines MPS calculations, its position in the planning hierarchy and defines reason of importance having MPS calculations in the consumer product industry. The second part of the article analyses sample data of beverage production, calculates MPS for each item and compares the results of finite/infinite capacity utilization. In the last part of the manuscript, authors discuss if the challenges described in introduction part of the article can be addressed with specific planning solution to calculate MPS.

1. Introduction

Companies in the Consumer-Packaged Goods (CPG) industry, especially those in food and beverage, face various challenges and unprecedented market changes. This fact raises the importance of properly implemented digital transformation strategies [1]. From corporate level up to small SMB companies with revenue in tens of millions. Those companies confront with tight competition, small profit margins and the trend of rising end-customer requirements. The complexity of the food and beverage industries, the overall market conditions that call for a diverse and ever-increasing product portfolio by end customers, and the need for synchronization between several batches and continuous stages make the efficient process of production planning and scheduling a very complex and challenging task. One of the biggest priorities of the CPG company is fulfilling consumer orders on time and with the agreed quality. This requires managing their production activities effectively. Consumer product industry companies, especially those which are in Food and beverage industry faces also other challenges:

• Demand variability—based on seasons, market areas and similar
• Shelf-life—different products vary on shelf-life which should be taken to consideration while creating mid-term planning and forecast.
• Regulation and compliance—different market may vary on different regulations which influence product configuration and increase pressure of accurate planning.
• Quality controls—different required quality from different customers increase product mix.
• Supply chain distribution—caused by seasons, supply chains or different unplanned situation [2,3,4].

On the other hand, the positioning of digital transformation strategies is very urgent. The main trend is to become more autonomous while remaining human-centric, with operations being top of mind for decision-makers. The truth is that many of the challenges food and beverage producers face today might be linked to outdated workflows and poor technology integration also. This fact is leading to serious productivity and collaboration challenges. It requires fully implementing and integrating technologies that help transform key industrial operations. Production companies in the CPG industry must create more end-to-end processes for better transparency, improve cross-department collaboration, and invest in digitalization strategies. These efforts can have a significant positive impact on production productivity, waste reduction, and on-time delivery in the food and beverage industry [1,3]. According to some authors, we can define forecasting as an important business process that seeks to estimate the future sale and product consumption which leads to make them in a fixed quantity. A very basic definition of the MPS (Master Production Schedule) is that it is a production planning tool used to outline which products need to be manufactured (time and quantity). It can also be understood as the connection between sales and manufacturing departments, balancing supply, and demand [4,5]. A very basic definition of the MPS (Master Production Schedule) is that it is a production planning tool used to outline which products need to be manufactured (time and quantity). It can also be understood as the connection between sales and manufacturing departments, balancing supply, and demand [6].

The combination of two paradigms at the system level (pharmacies and wholesalers) and the level of discrete events (manufacturers, production operations) is interesting. The authors managed to significantly reduce costs related to lost opportunities and increase the level of service within the Pharmaceutical Supply Chain.

In their studies, the authors [7,8,9] deal with the use of a digital twin in the optimization of intelligent production systems, which enables continuous evaluation and monitoring of production and logistics parameters in real-time. The authors emphasize the importance of analyzing real data, with which it is necessary to work when finding complex optimal solutions. This is a perspective view of dynamic production planning and management.

The study by the authors [10] is a contribution in terms of a critical analysis of the characteristics and limitations of existing production planning algorithms from the point of view of intelligent manufacturing. Another contribution is to propose the best strategy for selecting scheduling algorithms in connection with the real status.

Several authors [11,12,13,14,15] consider modelling and simulation as one of possibilities to optimize the production planning of an automated production system. In [11] study, authors focused on the process of multi-stage production planning of automated manufacturing, through the processing and testing of a simulation model for an automated production line in the TX Plant Simulation software version 2404 environment. A similar study was by the authors [12], who dealt with route planning in a multi-agent system to optimize the supply of a modular production line with the primary objective of minimizing travel time and total distance travelled. The proposed approach was verified using virtual reality for a better understanding of the scheduler’s problems. This approach has the potential to further advance the planning process in various areas. Localization solutions are addressed by the author [16], who in his study implements the design of a practical computerized localization expert system. It is based on a new idea of using the resolution of a computer map as an image to calculate the position.

This article briefly describes production planning as a topic, the tools used for the calculation of the Master Production Schedule (MPS), and the application of a specific software solution, Siemens Opcenter Planning version 2404, in the Consumer Products Goods industry [17,18]. The authors questioned whether the specific challenges of food and beverage production can be partially addressed by more precise planning to finite/infinite capacity. Therefore, they decided to analyze the benefits of MPS within the selected industry using sample data [3].

In general, almost every manufacturing company with discrete manufacturing processes can benefit from the application of advanced planning and scheduling solutions. The impact of the APS solution on the monitored metrics in the supply chain is closely related to the complexity of the manufacturing process, the number of products and their variations, and the need for flexibility. In practice, we encounter spreadsheet software that replace advanced scheduling tools. The given solution is inflexible with a low level of visibility. A large part of the knowledge base that forms the scheduling logic is in the planner’s head. This complicates the scheduling process in the event of its failure. The implementation of APS advanced scheduling solutions is generally applicable, while in the implementation methodology, the individual methodologies are distinguished mainly by the scheduling model (which is mainly influenced by the production strategy). The variables that influence the selection of the right scheduling model for a manufacturing company include:

• production strategy,
• production scope,
• prioritization of production orders (by delivery date, by customer prioritization, etc.),
• production calendar,
• production constraints (primary and secondary),
• production resources,
• optimization strategy.

These variables are determined in the pre-implementation phases and their combination is different for each manufacturing company.

2. Materials and Methods

The well-known concept of MRP II (Manufacturing Resource planning) encompasses a set of processes and methods for effective planning of all available resources of a production company. A fundamental idea underlying the MRP II concept is that the development of product and production must be planned in part long before there is actual customer (internal or external) demand. Some authors propose a planning hierarchy, namely three-level planning according to temporal range, a typical feature of the MRP II concept:

• Long-term planning: Master planning
• Medium-term planning: Detailed planning and scheduling
• Short-term planning & control: Execution and control of operations:
  • Sales and distribution
  • Research and development.
  • Production
  • Procurement

Long-term planning (Master planning is another term used for long-term planning) takes place several weeks or months to a year before realization depending on market and industry. The main aim is to forecast the total demand for specific products and processes that will be placed into production from the outside or on the logistics network by internal or external consumers. The production company can then derive quantities and acquire the resources that are necessary to meet the expectations of the demand. These might be persons, production infrastructure, or deliveries from third parties. Medium-term planning timeframe concerns the range of months to weeks. Its purpose is to forecast demand in more details with a higher level of precision along the time axis. Demand for resources must correspond to the resources probably available at certain times. Consequently, sourcing agreements that were reached during long-term planning might have to be precision tuned or modified. Short-term planning and control concern the actual servicing of orders. It represents the short-term temporal horizon of the days or weeks during which physical logistics take place, see Figure 1 [16,19,20].

The Master planning schedule (MPS) case described further in this paper is based on data from the consumer products industry, specifically beverage production. Beverage production is usually make-to-stock. As mentioned in the introduction, MPS can be calculated by an ERP solution. The biggest benefits of these kinds of solutions are:

• Integration—Integration of various business processes into a single system, so MPS calculations are aligned with other business processes.
• Basic planning—EPR solutions provide basic MPS calculations. This fits to production companies with straightforward processes.
• Data Synchronization

Despite the benefits of MPS calculated by ERP, several software vendors, like Siemens, offer dedicated software solutions for advanced planning and scheduling. These solutions provide several benefits, such as:

• Advanced Optimization—dedicated planning software uses sophisticated algorithms for production optimization.
• Flexibility and focus on details—APS solution provides more flexibility while planning and can handle more complex production environments with multiple constraints.
• Scenario Analysis—dedicated planning software allows to create what if scenarios [3,7,21].

2.1. Master Production Schedule (MPS)

As it was described briefly before, the Master Production Scheduling (MPS) provides a main plan usually called a Master plan. The master plan defines what production company needs to produce, how much, and when. The main aims of MPS are to reduce storage costs and to increase stability in planning. This is useful in the following cases:

• To make sure that the production company has good raw material availability, and safety stock are entered. This inevitably is causing high stock levels. Thus, high storage costs are incurred, especially for valuable materials and products.
• The master plan (MP) of these materials influences the entire production process: The planning of the dependent parts (co-products) depends further on the planning results of the finished products and main assemblies, even if the finished goods represent a small share of all the materials which must be planned. This is described in Figure 2 [22].

The process to calculate a master production schedule should have specific parts:

• Product list—products which specific company produce.
• Variation sub-lists for each product
• Timeframe—Year, Month, and week.
• Quantities—production quantities per specific time [22].

A typical environment in which specific planning software like Advanced Planning and Scheduling (APS) is taking an important role may be located as it can take data from existing systems or the User Interface, see Figure 3 [19,20].

2.2. Advanced Planning and Scheduling Tools

The authors decided to use the Siemens Opcenter APS solution for MPS calculation because of its general popularity in the EMEA region as standalone planning and scheduling software, and the history of the Siemens Opcenter Planning software tool (formerly Preactor) across various industries. APS solutions are a production and capacity planning solution that helps production companies make strategic decisions about production, resources, processes, and workforce. The main features are:

(a)

Bill of materials planning

(b)

Make—to—order planning

(c)

Make—to—stock planning—Generate accurate and achievable master production schedules (MPS). This includes considering rough cut capacity, pack-forward figures, target days of stock cover, manufacturing preferences, minimum and maximum reorder quantities, reorder multiples, and product shelf life. Production capacity can be specified as a quantity, duration, or weight.

(d)

Visualization of production schedules—Once an initial master production schedule (MPS) is created, data can be displayed as stock profile graphs and capacity usage graphs within the system [19].

2.3. Planning in Consumer Product Goods Industry—Beverage Production

The beverage production of cold brew coffee, for which the data used in the manuscript are derived, consists of one production site with several different departments. This type of production is usually managed by a team responsible for mid- to long-term planning, along with several schedulers who handle short-term detail planning [19].

2.4. MPS Calculation Make–to–Stock—Beverage Production

Make-to-stock items are planned to use forward logic, which is typical for the consumer-packaged goods (CPG) industry, particularly in food and beverage. For these items, dedicated planning software like Siemens Opcenter Planning calculates an MPS value on the opening date of each planning period, from the beginning to the end of the planning horizon within a specific timeframe.

The trigger for an MPS value to be generated is illustrated with the following simple script [19,23]:

If (Opening stock − demand) < Min stock, Then

(1)

Volume should be made:

MPS = Target Stock level − Opening stock + Demand

(2)

Else

No MPS volume is required.

MPS = 0

(3)

Calculation of MPS used to determine the planned production quantity for specific time frame on demand, which is forecasted, taking to consideration safety stock and existing inventory levels [6].

MPS = (Forecasted demand + Safety Stock) − (Beginning Inventory + On hand Inventory + Work in progress)

(4)

Two primary factors can be defined that influence the MPS value: the minimum stock level and the target stock level. The basic calculation is that if the closing stock for a given item falls below the minimum stock level (assuming no more is produced), the aim will be to close that bucket with a stock level at the target level. Both the target stock and minimum stock levels are calculated from the Minimum Days of Cover and Target Days of Cover fields. These can be either decimal values or whole numbers.

The main rule for both fields is that neither can have a value less than zero. However, a value of zero is completely valid for either the minimum or target days of cover. When conditions require an MPS value to maintain an item’s stock level, planning software (like Siemens Opcenter Planning) will generate an MPS value while respecting the minimum order and reorder multiples for the given item. If this trigger occurs on a non-make day (as defined in the calendar), the planning software will test whether an MPS value of zero will cause the item to enter a negative stock position. A planning system can be configured to either generate an MPS value that returns the item’s stock level to its target stock or to allow the shortage [19].

2.5. Planning Process and Source Data in Beverage Production

The planning horizon is set to 20 days, but dedicated planning software, such as Siemens Opcenter Planning, allows users to adapt it to their specific needs. The adjusted time horizon provides an immediate level of detail for the mid-term and is more generic for the long-term. By providing source data such as forecast data (which contains customers name, order dates, item description, quantity and similar) or sales orders (which contains source, idem code, item description and similar), along with other information like stock levels, non-aggregated demand, resource availability, and resource usage, users can calculate the MPS for every item shown in the print screen of the planning software (see Figure 3), which currently has empty parameters.

Planning software calculates daily opening and closing stock values, considering the initial stock and demand set for each item. When the closing stock falls below the minimum stock value, the software highlights the item for production and adjusts the stock to the target quantity [19].

2.6. Stock Profile Example

This figure shows the stock profile, which should remain between the target stock and minimum stock levels. The planning software offers user interactivity, allowing users to adjust MPS and forecast bars with immediate recalculation of the stock profile. This interactivity is also shown in Figure 4, Figure 5 on day 25, when the forecast and MPS are above the optimum [23].

2.7. Results—Beverage Production

After applying the basic algorithm to the entire hierarchy, it is necessary to calculate the demand for every sub-product up to the raw material and perform the MPS calculation for all the items. Siemens Opcenter Planning (APS Software) automatically calculates it at every level with function calculates all. The MPS values calculated indicate the supply quantity of raw materials needed to meet production requirements for the final products within the planning horizon. The results of the automatic calculation of MPS are shown on the print screen from Opcenter Planning software, see Figure 6. In this model, we use planning with infinite capacity as the baseline for further adjustments [23].

Capacity utilization is shown in Figure 7. While infinite planning indicates that capacity exceeds actual storage capacity (blue line—storage capacity). By applying constraint storage capacity we see that capacity utilization of storage is below actual capacity, see Figure 8. The system is restricting item production to the maximum capacity of each resource (storage capacity). To maintain the stock level within the required limits system will move the production schedule for make-to-stock items further to the future and make-to-order items earlier in the past. This adjustment is essential to guarantee timely completion of make-to-order items according to their specified due dates [23,24].

By implementing advanced scheduling tools and creating scheduling logic, the initial system requirements were covered, which can be worked with as they are well visualized. This helps to:

• the possibility of quickly making changes to the schedule depending on the availability of material. The Siemens Opcenter APS system allows manual intervention in the schedule and its subsequent recalculation over time. Also thanks to the preview of material loss, the user can adjust the schedule so that the resource is used optimally.
• achieving accurate scheduling complexity due to the large number of product variations. The implemented solution for advanced planning takes into account each product variation as a separate product. System optimization is also achieved by combining orders with the same product.
• the ability to manage complex requirements for line setup (machine setup), complex requirements for the production process. The applied Siemens Opcenter APS system allows us to create a matrix of transition from one product to other types and at the same time, e.g., If inter-operation activity is necessary, it allows products to be combined into groups of the same products before and after operations in order to minimize downtime during production changes.

3. Discussion

In the real world of industrial companies, planning and scheduling can be considered low-hanging fruit in their path of digital transformation, with a significant impact on return on investment. Contrasting the above-mentioned use case of MPS calculation in beverage production with the typical challenges of the CPG industry brings more clarity to the real-world challenges that CPG companies face. The comparison of capacity utilization between finite and infinite planning proves the importance of having a dedicated APS solution that focuses on constraints and addresses one of the most important challenges: fulfilling consumer orders within a reasonable time frame and agreed quantity. Finite capacity planning increases the accuracy of sales forecasting. Finite capacity utilization focuses on constraints. Primary and secondary constraints define the production logic of the company. Considering these constraints ensures that planning is aligned with reality. An accurate production plan optimizes procurement costs by streamlining the acquisition of raw materials. This approach also enhances resource utilization by considering available resources and establishing capacity requirements within a specific timeframe. In our case, calculating the Master Production Schedule (MPS) under constrained (finite) capacity helps maintain stock levels within targeted and minimum limits while optimizing resource utilization. This also helps the food and beverage company prepare for demand variations and promptly react to any supply chain distribution issues. The production planning results for orders with different characteristics are obtained through the development and implementation of a specific mid-term planning algorithm based on MPS calculations. Considering the specifics of the consumer product industry, especially food and beverage, where each item has specific storage and delivery limits, shelf-life, or must comply with different regulations for specific markets, accurate MPS calculation is crucial to maintaining a competitive position. The MPS calculation is a specific function of the APS solution, which operates standalone next to the ERP solution or as part of the planning module of the ERP solution. This specific function enables CPG (food and beverage) production companies to create what-if scenarios with optimization capabilities based on domain-specific criteria, allowing them to automatically evaluate various options.

As shown by the sample data, without the functionality of MPS calculation included in the APS solution, companies may reach limits on production optimization and the creation of what-if scenarios. When procurement optimization is performed and several alternatives are developed, a solution without an advanced planning system cannot identify or consider manufacturing implications. These implications can affect the suitability or priority of the procurement alternatives. The sample data provide a full picture of how the integration of MPS calculation within an advanced planning solution can extend those capabilities across domains in what-if scenarios and optimization.

4. Conclusions

Effective management of production processes in the current market economy is not possible without the use of modern planning methods supported by the right software solutions. Even, nowadays, we are seeing many food-beverage companies managing their plans in table editors. To ensure an effective planning process in this context, it is necessary to develop an adequate reference model of the planning processes on mid-term basis. This serves the basis on which various mathematical models, recommendations for decision-making, and various advanced planning methods can be used and combined. The main difference between advanced planning methods is their approach, i.e., the planning process is considered as dynamic process with constraints. This makes it possible to use the so-called sliding horizon, i.e., at each planning moment, a plan decision is prepared for a certain upcoming time interval, which is incorporated into the current one.

The second advantage of mentioned methodology of MPS calculation by advanced planning and scheduling tool is the mandatory evaluation of available right information. This input serves to support the decisions to be reached. As a rule, criteria for the quality of decisions are established, and methods of achieving high values of these criteria are chosen. The third essential feature of the mentioned approach is the consideration of various limitations. Above all, if there is a capacity limitation, it is necessary to address it immediately in the planning process, this can eliminate further problems with supply chain distribution or shelf-life of products (in food-beverage industry). The accuracy of MPS calculation is based on the input data. This data needs to be sourced from ERP systems. In addition, to fulfil the tasks, it is necessary to reload the data into the planning module of APS system, to supplement the data relevant for decision-making and to include any missing data from the ERP system. This connection works in both cases, when MPS calculation is performed by ERP add-on of APS or as APS standalone. The difference is only the technicalities of the connection. The obtained results should be returned to the ERP system for their application. The application of such a complex data processing mechanism has led, in practice, to relatively limited capabilities of APS systems. Currently, these systems appear to be suitable for use at the strategic planning level of the enterprise rather than the level of preparation of the master production plan. Short-term planning using these systems is usually appropriate for large-scale production with a relatively long (weekly or longer) planning horizon.

While working on this manuscript, the authors have identified several possibilities for further studies and development such as:

• How data accuracy influences the overall planning process?
• Whether the cloud applications for advanced planning increase overall computing capabilities of the planning process?
• Whether the implementation of MES solutions, as well as manufacturing intelligence tools, influence overall planning accuracy.

The study was designed to declare the usability of the advanced scheduling tool in conjunction with the active activity of a person who is a decision-making factor in the chain. This connection represents a higher level of digitalization, which can lead to the creation of conditions for Industry 5.0. The specific advantages of the solution presented in the article are supported by:

• The ability to evaluate process performance based on initial constraints, which allows you to create an accurate advanced planning model for limited capacities.
• Coordinate demand and production orders. The data model of the advanced planning scheduling model Siemens Opcenter APS allows you to divide orders according to various attributes, priorities, delivery dates. Such an option also allows you to create a schedule for a given time period and use What if analysis to determine the impact on a given order.
• Correctly evaluate the allocation of material between orders for proper material flow management. Advanced planning tools allow us to monitor the flow of material and, using the Material Explorer function, also the consumption of material over time—how the production order flows.
• Automate the scheduling process (ability to combine automatic scheduling logic with manual intervention). The scheduling algorithm logic allows you to automatically create a schedule from the beginning of production to the final one on the line. Siemens Opcenter APS also allows manual intervention in the schedule. Such intervention is automatically recalculated.

The specifics of the consumer products industry, especially food and beverage, which typically falls under the SMB category, also present unique challenges to be mapped and further researched. This industry is characterized by high product rotation with lower prices and many challenges within the supply chain. All these factors make APS implementation very challenging. The authors identify a research gap in combining APS solutions with other systems, such as LIMS, within this industry.