In the previous article, we analyzed the role of Integrated Business Planning as a tool for aligning strategy, demand, and production capacity in cardboard packaging companies. An essential approach to managing market complexity and ensuring decision-making consistency across the entire supply chain
The effectiveness of any planning model depends on its ability to translate into concrete results on the shop floor. In this sense, the production department represents the most effective starting point for launching an improvement journey. This is where inefficiencies, often invisible at an aggregated level, are concentrated, yet they directly impact productivity, customer service, and margins.
Production is also the area where it is easiest to isolate variables, measure impacts, and achieve tangible economic returns in relatively short timeframes. For this reason, acting on this department not only generates immediate results but also creates the conditions to address more structured topics such as organizational optimization, the revision of indirect processes, and process digitalization.
In light of these considerations, we present below five recurring critical issues observed in cardboard converters and box manufacturers during operational analyses, along with targeted interventions capable of delivering measurable benefits in the short to medium term.
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Machine downtime: the problem is not the data, but the structure
One of the first findings that emerges during analysis concerns machine downtime management. In most cases, data exist, but they are not structured in a way that supports effective decision-making. Causes classification is often redundant or unclear, with overlaps between categories and an inconsistent distinction between planned and unplanned downtime. In addition, relevant operational phases, such as start-ups, are frequently excluded, treated separately, or not analyzed in sufficient detail. This setup limits the ability to identify the real causes of machine availability losses and prevents the construction of reliable analyses of the main sources of downtime. The result is a reactive management approach, where problems are addressed case by case without an overall view of priorities.
Indicators are valuable for shaping the future through continuous improvement using the Pareto principle: investing time and money, limited resources, starting from the most impactful inefficiencies.
The most effective intervention in these cases is the restructuring of the downtime classification model, introducing a clear hierarchy between planned and unplanned downtime and defining causal groups consistent with production dynamics. When combined with direct involvement of shop-floor staff and a systematic analysis of the main wastes, this work transforms data from a descriptive element into a decision-making tool. Without this intervention, it is pointless to talk about digitalization, BI, and AI: if data (causal) are not reliable, improvement is impossible. The benefit is immediately visible in the ability to focus efforts on the highest-impact causes, progressively reducing downtime and increasing equipment availability.
Even causes related to speed losses and scraps are often underestimated but fundamental. Overall Equipment Effectiveness (OEE) depends not only on availability, but also on performance and quality. Not reaching nominal speed or producing scrap without understanding the causes means losing valuable data that could be used to identify and reduce inefficiencies.
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Setup and changeovers: efficiency is decided in minutes
How much is one minute of startup worth per year?
(Machine hourly cost / 60) × total number of startups.
In packaging, machine startups are one of the main sources of inefficiency. Die-cutters, folder-gluers, flexo printing lines, and casemakers are characterized by frequent format changes requiring complex setup activities heavily dependent on operator experience. In many plants, these activities are performed according to long-established practices that are rarely analyzed systematically. The consequence is high variability in startup times and difficulty distinguishing value-added activities from pure waste. The absence of standards makes it difficult to transfer know-how and replicate best performance.
The application of SMED methodology allows direct intervention on this issue. Through detailed analysis of setup sequences, separation of internal and external activities, and simplification of operations, it is possible to significantly reduce changeover times. In many cases, this approach leads to reductions of 30–50%, with immediate effects on production flexibility, the ability to handle smaller batch sizes, and the capacity to serve more customers in a single day without compromising overall efficiency.
For companies operating on two or three shifts, this can also mean saving hundreds of thousands of euros per year. The goal should be to make machine setup increasingly resemble a Formula 1 pit stop.
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TPM: from reactive maintenance to operational reliability
Another recurring issue concerns maintenance management. Although Total Productive Maintenance is a well-known model, its application in real operational environments is often partial or limited to isolated initiatives. In practice, a reactive approach still prevails, where maintenance is mainly carried out after breakdowns, directly impacting production continuity. The lack of structured autonomous maintenance, combined with the absence of reliable historical data and consistent preventive plans, leads to an increase in unexpected downtime and a reduction in equipment lifespan. This results in significant indirect costs that are difficult to quantify but clearly visible in process instability.
The progressive introduction of TPM principles represents a key step toward improving equipment reliability. The introduction of autonomous maintenance is one of the most effective quick wins within TPM to improve equipment reliability. Directly involving machine operators in simple periodic checks and maintenance activities, on a weekly or monthly basis, makes it possible to detect anomalies before they turn into failures. Those who operate the machine daily are often the first to notice abnormal noises, vibrations, or behavioral changes in the equipment. By integrating these activities with standardized checklists and a ticketing system for reporting anomalies to maintenance, greater collaboration is fostered between production and maintenance departments, which are often in conflict. This approach increases operator responsibility, helps preserve wear-prone components, and makes the production system more stable and predictable.
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The Production Manager is not an operator
From an organizational standpoint, one of the most common issues concerns the role of the Production Manager or Operations Manager. In many companies, this role is heavily involved in daily operations and spends most of the time managing emergencies, coordinating interventions, and solving contingent problems. This approach limits the ability to focus on higher-value activities such as data analysis, identification of improvement areas, and structured resource management. In the absence of reliable indicators and a coherent reporting system, even strategic decisions tend to be based on perception rather than evidence.
Repositioning the Production Manager role first requires clear, shared, and continuously updated data, capable of providing an immediate view of line performance. In this context, introducing BI systems with real-time production KPIs is a fundamental tool, both for analyzing the previous day’s performance and for longer-term assessments that identify recurring inefficiencies and structural issues. At the same time, it is necessary to reduce direct operational workload through intermediate coordination layers and a clearer distribution of responsibilities. Only in this way can the Production Manager regain a truly managerial role focused on analysis, continuous improvement, and overall performance enhancement.
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The production organization structure that improves performance
The lack of a structured organizational model is another factor that negatively affects efficiency. In many companies, the setup is limited to a Production Manager and a group of machine operators, without intermediate roles capable of managing operational coordination. This leads to a continuous flow of escalations toward the manager, who is directly involved in heterogeneous issues, resulting in decision overload. At the same time, operators are often engaged in ancillary tasks that reduce their effective production time.
The solution lies in defining a clear hierarchical structure within production, with well-defined roles and responsibilities. The machine supervisor assumes an operational role focused on improving line productivity, coordinating the team and managing aspects such as quality, production speed, startups, and autonomous maintenance. The shift supervisor, instead, drives operational continuous improvement through daily management, lean workshops, and audit activities. The Production Manager maintains a tactical and strategic view, making decisions based on company objectives, discussions with shift supervisors, and production data analysis.
A quick win in this area is the introduction of the ASP (Production Service Assistant). This role manages equipment and support materials such as printing plates, inks, and dies. This setup improves operational flow, reduces downtime, and frees operators to focus on increasing machine availability.
Building value by acting on production
The issues described represent recurring patterns in cardboard packaging companies and, at the same time, concrete opportunities for improvement. Production-focused interventions have the advantage of being circumscribed, measurable, and quickly implementable, with direct effects on key indicators such as equipment availability, lead times, and productivity. It is precisely this combination of impact and speed that makes production the ideal starting point for any transformation journey. The results achieved at this stage not only generate tangible economic returns but also contribute to building a data-driven culture and continuous improvement mindset, essential for tackling more complex projects.
Once operational efficiency is consolidated, the company can reinvest savings from eliminated inefficiencies into further, more strategic improvement initiatives, such as advanced planning and commercial development. However, without a solid and reliable production foundation, any evolution risks to remain incomplete.
For this reason, addressing today’s main production pain points is not just an improvement opportunity, but a necessary condition to launch a successful continuous improvement journey in the cardboard packaging sector.
