What are the root causes of material delivery schedule inaccuracy in supply chains?

2.1 Delivery schedule and schedule inaccuracy

A delivery schedule is defined as “the required or agreed time or rate of delivery of goods and services purchased for a future period” (Blackstone, 2013). Delivery schedules contain planned orders and call-off information on various planning horizons. The order information can be expressed in different planning buckets, normally varying among days, weeks and months.

Schedule inaccuracy in a material ordering process refers to the difference between a planned and actual demand for items (Lee and Adam, 1986). The absolute percentage error, or mean absolute percentage error (MAPE), is a common way of measuring forecast inaccuracy (Makridakis et al., 1998), thus a relevant performance measure of schedule inaccuracy.

2.2 Theoretical perspectives on factors causing delivery schedule inaccuracies

This study addresses schedule inaccuracies in material delivery using two theoretical perspectives to understand the various underlying elements contributing: decoupling management and complexity theory. Both decoupling management and complexity theory literature provide essential theoretical lenses through which we can understand, anticipate and perhaps even mitigate the delivery schedule inaccuracies in material ordering processes.

2.3 Causes of delivery schedule inaccuracy

The terms schedule instability and nervousness have been used to define and understand variations in delivery schedules, leading to schedule inaccuracies. Steele (1975) uncovered the phenomenon of schedule instability and related effects on performance. Over several decades, researchers have suggested scheduling configurational variables or parameters that reduce or dampen schedule instability (e.g. Atadeniz and Sridharan, 2019; Herrera et al., 2016; Ho, 1989; Lalami et al., 2017; Lee and Adam, 1986; Li and Disney, 2017; Zhao et al., 1995; Zhao and Lee, 1996).

Primary scheduling parameter variables include planning horizon (e.g. Lee and Adam, 1986), time fence management/frozen period (e.g. Zhao et al., 1995) and re-planning periodicity or frequency (e.g. Ho, 1989). These parameters can moderate generated instability in delivery schedules (Shurrab and Jonsson, 2023).

Schedule instability is also contingent on contextual planning environment variables (Harrison, 1997; Inman and Gonsalvez, 1997; Jonsson and Mattsson, 2003). Previous studies have considered several contextual variables potentially impacting delivery schedule inaccuracy: external (demand- and supply-related) and internal (product- and manufacturing-related) variables.

External demand-related variables include customer input (e.g. Pujawan, 2008), demand pattern (e.g. Zhao et al., 1995), sales forecasting errors (e.g. Lee and Adam, 1986) and stock-out cost (e.g. Ho and Carter, 1996). Demand patterns – featuring frequent low actual demands and increased product life cycle uncertainty – and forecasting errors – leading to late increases in demand – can cause schedule instability. In contrast, late changes in end-item specifications can moderate such generated instability Shurrab and Jonsson (2023).

External supply-related variables include supply relationships and supplier profiles (e.g. Krajewski et al., 2005). A lack of readiness for supply chain disruptions and transportation dynamics characterizing suppliers or relationships with suppliers can cause delivery schedule instability. Alignment among engineer-to-order’s (ETO’s) and suppliers' operations times, pick-up frequency, lot sizing rules, unit packaging (unit load) or any other delivery flexibility forms can moderate schedule instability Shurrab and Jonsson (2023).

Internal product-related variables (e.g. product structure and item commonality) and manufacturing-related variables (e.g. Ho, 1989) can also generate delivery schedule instability (e.g. Lee and Adam, 1986) through causal and moderating effects (Shurrab and Jonsson, 2023). Manufacturing-related causal variables could include, for example, production disruptions and inventory stock discrepancies. Related moderators include capacity scalability, transportation optimization, schedule miscommunication, safety stock policies and policies penalizing suppliers for delivery quantity and timing deviations.

3.1 Study design

The data analysis is based on qualitative and quantitative data collected at a European personal car manufacturer (here, called the OEM). A single case was considered appropriate due to the massive amount of data that needed collection. We wanted to understand the case company’s specific operating variables and conditions, identify relevant explanatory/predictive variables, collect variable data and analyse and interpret the findings in detail. Therefore, a qualitative explorative study preceded a quantitative data analysis. The qualitative study identified possible causes and specified variables with potential causal effects on delivery schedule accuracy. We then collected quantified data corresponding to some of these variables and built a database of variables to further analyse the predictive difference of these variables on delivery schedule inaccuracies. As such, we apply a mixed method design to use the findings of the first study to inform the second study and expand the insights generated in a developmental manner (e.g. Davis et al., 2011). The following eight data collection and analysis phases are conducted: (1) mapping the material planning process, (2) collecting/extracting two years of delivery schedule data from the OEM’s enterprise resource planning system and forming delivery schedule groups, (3) measuring delivery schedule accuracy on different horizons, (4) exploring features and proposing how OEM-specific features explain schedule inaccuracies, (5) defining quantitative feature measures and collecting quantitative data of identified features, (6) exploring relationships between features and inaccuracy (plotting and correlations) and interpreting the size of predictive differences, (7) analysing feature reduction, logistic regression and random forest and (8) interpreting data model findings.

The qualitative and quantitative studies were jointly conducted by a team of four researchers and two practitioners. Two researchers focused on data analytics, and the other two framed and led the data collection and analysis. One of the practitioners represented the case company, focusing on material planning and delivery schedules. The other practitioner is a specialist in delivery scheduling and information sharing in the automotive industry (i.e. the case company’s industry). Both practitioners actively participated from the beginning to the end of the study, including problem formulation, research design and research analysis.

3.2 Delivery schedule process and delivery schedule data collection (Phases 1–2)

The OEM has 10 assembly factories globally. Most purchased components are delivered in sequence. The material requirements planning (MRP) and delivery schedules are generated on daily planning buckets with a 60-week horizon and are updated daily. Call-off volumes required for the upcoming two days are frozen, and a safety lead time of two days is used for inbound material from suppliers. The last day of (customer) order confirmation (LDOC) of a car is 3–4 weeks before the delivery date. Four weeks of the order book is frozen every Thursday. In other words, on Wednesday, the production schedule contains a three-week frozen period; on Thursday, the fourth week on the horizon becomes frozen. Accordingly, the end product demand is perfectly known during 3–4 weeks. The assembly sequence is fixed for the next two weeks, meaning that the company knows exactly what car variants to assemble each day during this period. Consequently, we presume the gross requirement of purchased items to be close to fixed during 2–4 weeks.

Schedule accuracy (the dependent variable) was measured based on historical delivery schedule data. Therefore, the first quantitative data collection phase was to collect historical delivery schedule data from the OEM and build a two-year delivery schedule database (2017–2019). In the empirical analysis, we aggregated the data into weekly planning buckets. The database of delivery schedules contained 16.5 million schedule records (rows). The records were grouped, and each group included records with identical combinations of (1) item number, (2) ship-to-gate address (unique delivery address) and (3) demand (delivery) week. Consequently, each group included all planned/required order volumes of the unique items shared by the OEM with the individual suppliers over two years, specifying the unique delivery addresses and weeks. The grouping resulted in 0.63 million unique schedule groups. These groups were called “delivery schedule groups”.

3.3 Exploring possible root cause effects at the OEM (Phase 4)

Based on the literature review on delivery schedule inaccuracy causes – in combination with workshops and dialogues within the OEM – we identified features with expected explanatory effects on schedule inaccuracy. The OEM representative (i.e. the co-author mentioned earlier) led this exploratory phase. The identified causes were discussed in a larger research project involving two more automotive OEMs. Therefore, although the variables were generated from one specific OEM, they were validated as generally relevant possible causal variables in other automotive settings.

In total, six interviews were conducted with the senior logistics planning manager responsible for production planning and material ordering. Archival data, in the form of detailed descriptions and maps of production and material ordering process steps, subprocesses and systems, were also collected and analysed. Figure 1 summarizes the identified categories of features and individual feature variables at the OEM.

The LDOC is an important decoupling point. Inside the LDOC, the order book is fixed. Its data consist of customer orders (used as actual demand data within this period) and a mix of customer orders and sales forecasts (used outside this point). Therefore, we can distinguish between variables affecting the gross requirement outside (1 in Figure 1) and after (2 in Figure 1) the LDOC. We also distinguish between variables as follows: variables affecting the net requirement before and after the LDOC (3 in Figure 1), variables not acting as possible root causes of schedule variations but that can potentially amplify (moderate) them (4 in Figure 1) and variables affecting the variations by interrupting the levelled schedule (not in Figure 1).

The assembly sequence is fixed for two weeks, meaning that, combined with a 100% accurate market demand (fixed-order book), the gross requirement within the two-week horizon should be close to 100% accurate. A prerequisite for such accuracy is that the production capacity is unchanged within this two-week horizon, the scrap levels do not exceed set parameters and the daily production volumes do not deviate from planned volumes.

If the production output deviates from the plan, the OEM’s strategy is not to add extra capacity that day to ensure that the actual daily production output does not deviate from the planned. Instead, the required compensatory capacity is scheduled for a later date. As the assembly sequence is frozen for two weeks, if the production volume plan is not met on the first day, the remaining unmet volume of cars planned for assembly will be assembled at the beginning of the next day. Production deviations entail changes in daily gross requirements at the item level if another mix of car variants is built on the second day compared to previously planned. However, capacity solutions such as overtime may be added later that week, leading to changes in gross requirements since more cars will be assembled per day than previously planned.

Other variables affecting the gross requirement on short horizons are phase-in or phase-out decisions, as the exact date may change on short notice. The deviation between the planned and fixed order sequencing on the assembly line (during the 3–4-week frozen period) also changes the short-term gross requirement, correction of scrap levels and inventory balance records.

Outside the LDOC, market forecast accuracy (i.e. if order intake deviates from the forecast) is expected to explain a significant proportion of the gross requirement changes. Market forecast accuracy is also affected by overall production programme decisions at the car model level, such as shifting volumes between models or markets. Sometimes, the production programme requires modifications towards optimized levels in line with sales initiatives to meet quarterly sales targets in the sales organization. As for changes in the net requirement (inside and outside the LDOC), variables leading to changes in material ordering parameters (e.g. safety stock, order quantities, unit loads and pick-up frequencies) come into play.

Variables with presumed moderation influence on delivery schedule variations include suppliers' local bank holidays, vacations and opening hours, unit loads, full truckload optimizations, transport lead time and pickup frequency. The transport lead time is from pickup at the supplier until the goods are received at the OEM. The inaccuracy is expected to be higher the more extended the transport lead time since material ordering becomes more dependent on forecasts. Larger unit loads and more infrequent pickups may increase the lumpiness of schedules, thus increasing inaccuracies. However, they may, at the same time, stabilize the schedules on the short horizon, as minor daily variations may be evened out when using larger unit loads and weekly instead of daily deliveries. Opening hours at suppliers, cross-docks and plant goods receptions may impact daily schedules but are not expected to affect weekly schedules. Local plant shutdowns associated with bank holidays and vacations are other potential variables that may lengthen delivery lead times and cause multiplication effects.

Take rate is a variable not mentioned in Figure 1, but it has a presumed effect. It refers to the proportion of assembled vehicles that contain an item. The highest take rate is achieved for items included in all vehicle models and all model variants. The lowest take rate is for variant items included in one or a few models. A low take rate is expected to affect gross requirement uncertainty due to increased market forecast inaccuracy.

3.4 Dependent and independent variable definitions

3.5 Independent variable reduction and data analysis

4.1 Identified differences related to individual features of delivery schedule inaccuracies

Table 5 presents the results of the 20 logistic regression analyses. The table lists the significant features of the various models. Appendix 2 presents the five most important features of random forest models as a complement.

Our regression findings (Table 5) show that the forecasted volume, item’s order life cycle, take rate, unit load, production deviation, pick-up frequency and transport lead times are significant variables in most models. The estimated values of the corresponding coefficients (the influence) vary in significance and direction between models.

Order life has a significant positive influence on the longer horizon for all models. As proposed, order life significantly causes gross requirement accuracy on longer horizons since demand forecast accuracy is expected to be lower early in the order life cycle when there are lower sales. On the 2-week (5, 10%, 30% and 50% models) and 4-week horizons (5%, 10% and 30% models), the order life variable significantly affects in the opposite direction. Accordingly, the later an item is in the life cycle, the higher the probability of inaccuracy on shorter horizons. When exploring the data, we found that items close to the phase-out usually have inaccurate plans due to, for instance, changed safety stocks and late phase-out rescheduling, which has also been reported in the literature (Teunter et al., 2011; Wänström and Jonsson, 2006). Consequently, the findings verify that order life cycle data can explain schedule inaccuracies. However, the way it impacts varies over short and long horizons.

Our order life cycle measure indicates how late an item is in a life cycle. Although the current measure does not cover phase-out planning and its influence on inaccuracy, we expect the inaccuracy to be more significant during phase-out than during periods of continuous demand. Perhaps the size on a 2-week horizon indicates this significance, but we need a more accurate phase-out variable to measure the phase-out phenomenon. Alternative life cycle measures may cover additional life cycle patterns, for example, separating phase-in, steady-state and phase-out periods (Nepal et al., 2012).

Take rate and forecasted item volume are significant in almost all models. These are measures of item commonality and product complexity and are expected to directly impact the demand forecast (i.e. the gross requirement). Items with low take rates, and corresponding low volumes, pose a significant forecasting challenge and in turn intensify schedule inaccuracies by causing lumpiness and intermittency in the requirement. A decreased item commonality could further destabilize the schedule, as outlined by Meixell (2005). Therefore, the influence of such commonality on schedule inaccuracies was anticipated across both short and long horizons. The two features are correlated, but they also differ. The forecasted item volume is affected by planning parameters (e.g. production sequencing and batch sizing) and the number of units of the respective item per assembled car. In addition, some of the transformed forecasted volume variables show partially limited levels of linearity, which may explain the lack of significance in some models (e.g. for the 100% models on 8- and 10-week horizon – See Appendix 1).

Unit load is significant in all models, with a reverse influence for the 5% and 10% models compared to the 30%, 50% and 100% models. The findings indicate that larger unit loads (i.e. lower values of the transferred unit load variable) create stability in the face of larger variations. However, these same larger unit loads can also lead to more significant inaccuracies when faced with smaller variations, suggesting the paradoxical nature of large unit loads. On the one hand, they confer responsiveness when there are larger fluctuations. On the other hand, when schedule variations are considerably smaller, larger unit loads contribute to lumpiness in demand (as suggested by Teunter et al. (2011) and Wänström and Jonsson (2006)), which amplifies schedule variations. This dichotomy underscores the delicate balance involved in managing unit loads to optimize schedule accuracy.

When there is a deviation between planned and actual production volumes, item demand fulfilment in the imminent period is expected to be affected (Atadeniz and Sridharan, 2019). The effect is expected to exist on all horizons but particularly on short horizons when the assembly sequence is fixed. The findings confirm that production deviation has a short-term influence (Pujawan and Smart, 2012) during the period of a two-week fixed assembly sequence. Most production capacity losses at the studied OEM are solved by overtime on the weekends during the same weeks. Therefore, a daily production deviation affects the daily schedule accuracy but may not be visible in the weekly bucket studies. Consequently, the short-term influence may be stronger when considering daily buckets. The impact of production deviation may also be further conditioned by features not studied here; for example, the safety stock policies, capacity scalability (e.g. maximum overtime and space), suppliers' limited flexibility (Shurrab and Jonsson, 2023) and take rate with larger expected influence for items with low take rate.

While pick-up frequency might intuitively seem to enhance accuracy due to the potential for a rapid response to changing material requirements (Shurrab and Jonsson, 2023), our findings reveal a nuanced picture. Higher frequencies show a statistically significant predictive difference in all regression models, with more considerable inaccuracies associated with higher frequencies. The distribution of frequencies at the OEM ranges from daily to weekly. Because smaller production deviations are normally mitigated by weekend overtime shifts within the same week, day-to-day variations do not impact schedule accuracy as long as the weekly requirement is consolidated into weekly pickups. The aggregation of requirements on a weekly basis consequently provides robustness, leading to schedule accuracy. However, the story is different regarding daily pickups: daily variations directly impact the accuracy of these schedules.

The findings indicate that a longer transport lead time is associated with higher inaccuracies. The feature was significant in all 50% and 100% models but not in all 5%, 10% and 30% models. We expected a stronger and more significant predictive difference in transport lead time. This was not identified, probably because almost all transportation lead times are within a week and almost none is longer than two weeks.

Car models emerged as significant variables in some regression models, irrespective of the horizon or the degree of inaccuracy. Notably, how these car model variables influence schedule variations differs depending on the magnitude of the MAPE thresholds. Both the significance and, in certain instances, the directionality (whether positive or negative) of their impact on schedule variations differ. Hence, we can infer that car model variables influence schedule accuracies differently, contingent on the severity of schedule inaccuracies. Despite these findings, the interpretation of how and why different car models affect schedule accuracies is multifaceted and extends beyond the scope of this study. This complex interplay is mainly attributable to several items being utilized across multiple car models, suggesting various potential differences and characteristics that could impact variations. Consequently, an in-depth analysis of features specific to different car models is essential for a more nuanced understanding of their influence on schedule accuracy.

The influence of the item’s order life cycle on delivery schedule instability becomes increasingly notable for longer, compared to shorter, planning horizons. Similarly, the production deviation influence is more notable on shorter horizons. This distinction contrasts with the take rate, forecasted volume and pickup frequency, demonstrating a relatively stable impact across all planning horizons. An inverse relationship emerges when examining the order life cycle on shorter horizons, potentially indicating a phase-out influence. Consequently, our analysis substantiates the claim that features contributing to delivery schedule instability vary in their influence, depending on the planning horizon relative to the LDOC decoupling point and the frozen production plan.

The random forest models (Appendix 2, Figure A9) identify unit load, transport lead time, order life, take rate and production deviation as the five most influential features. This roughly corroborate the general findings of the logistic regressions regarding what features affect schedule accuracy. Unit load stands out as the most influential feature in random forest models. However, it should be noted that the impact of features is not as clearly identified in random forests as in logistic regression models. Feature importance in random forest models reflects what variables are useful for classifying/predicting scheduling inaccuracies, but not how these affect its likelihood (as in logistic regression). An exploration of the unit load suggests that there are subregions in the variable range that do not follow a straightforward linear fit. This is an interesting case for how other models (i.e. non-linear) can pick up useful feature patterns to explain delivery-inaccuracy classifications.

4.2 Conceptualizing the influences on delivery schedule inaccuracy

Here, features of delivery schedule inaccuracies are discussed with the ambition of conceptualizing the causality of delivery schedule inaccuracies (Figure 2). Firstly, the findings show that features can be associated with delivery schedule inaccuracies in different ways, depending on whether the variable affects (1) before or (2) after a decoupling point separating forecasts and customer orders (here, defined as the LDOC), if it affects the gross or (3) net material requirement and if it is a direct or (4) moderating cause. We also propose a category of causes (5) related to the disruption of a levelled plan from the qualitative data analysis, but this category is not empirically analysed quantitatively here.

The first two categories cause delivery schedule inaccuracies by directly affecting gross material requirements. The LDOC operationalizes the CODP (Wikner, 2014). Before the LDOC, the market demand is known, as it consists of customer orders, not forecasts. Production capacity losses resulting in lower production output than planned and extra/exceptional customer orders accepted before the LDOC are features identified in the qualitative analysis. The features directly affect the gross requirement before the LDOC. After the LDOC, the gross requirement is more uncertain, especially for variant items with low take rates, as it is increasingly dependent on the market forecast further out in time from the LDOC. Our quantitative analysis identified a large and significant influence of the take rate feature on schedule inaccuracy before and after the LDOC. It also verifies the expected impact of the production deviation before the LDOC.

Looking at the variables describing delivery schedule inaccuracies through the lens of complexity theory reveals a network of interconnected variables. At the core of this network, we find product complexity, a determinant that various researchers have singled out for its significant influence on delivery schedules (e.g. Kabak and Ornek, 2009; Pujawan et al., 2014; Shurrab and Jonsson, 2023; Sivadasan et al., 2013).

The take rate and forecasted volume features serve as tangible measures of product complexity. As proposed by Serdarasan (2013) and Shurrab and Jonsson (2023), product variety can lead to increased internal variety, causing fluctuations in demand and, subsequently, delivery schedules. Incorporating the perspective of decoupling management (Banerjee et al., 2012; Wikner, 2014), it is clear that product complexity management is critical. As CODPs and CADPs shift, the impact of product complexity on delivery schedules can differ. Particularly, in a non-make-to-stock environment, where the CADP plays a significant role, the ability to manage and absorb product complexity can become a decisive factor in maintaining accurate delivery schedules.

Looking at the complexity absorption capability of a firm, Huatuco et al. (2021) reinforce the idea that an organization’s internal environment needs to be well-equipped to handle high product complexity. If there is a lack of capacity, flexibility or adequate information systems, there is a greater likelihood of schedule inaccuracies.

Echoing this, Holweg et al. (2018) suggest that the design and variations of processes within a firm can contribute to delivery schedule inaccuracies. The firm’s internal process variations and design, such as insufficient buffering of variations and bottlenecks, can further amplify the impact of product complexity on delivery schedule inaccuracies. Summarizing these insights leads us to the following proposition:

Figure 2 illustrates the third category, wherein changes in the net requirement emerge from process variations tied to alter planning parameters. The concept that modifications in planning parameters, including safety stocks and lot sizes, can instigate net requirement fluctuations is reinforced by studies from Atadeniz and Sridharan (2019) and Li and Disney (2017). They proposed that these parameters could serve as moderators, buffering the effects of variances on delivery schedules. Although we examined these impacts in our qualitative study, the quantitative analysis did not directly address how they altered the net requirement. Nonetheless, we indirectly deduced their influence from the order life cycle variable results, such as decreased safety stock volumes during phase-out periods (Wänström and Jonsson, 2006).

Our quantitative analysis pinpoints the order life cycle as a significant determinant of delivery schedule inaccuracies. It exposed that these inaccuracies could either be amplified or mitigated at different order life cycle stages due to interacting variables. For instance, items in the later stages of the life cycle might influence the net requirement before the LDOC due to these process variations, thereby enlarging schedule inaccuracies. As the product transitions towards its phase-out period, items tend to become slow-moving, leading to a lumpiness in their demand (Andersson and Jonsson, 2018).

A decoupling management perspective (Banerjee et al., 2012; Wikner, 2014) can help elucidate further complexities. In the phase-out stage, unpredictable and declining demand could shift the CODP or extend the hybrid zone closer to the end customer, implying a transition from forecast-driven to actual demand-driven production and delivery. This move minimizes surplus stock risks. Simultaneously, during the phase-out period, companies might opt to customize the remaining units for maintaining appeal or managing dwindling resources, moving the CADP and hybrid zone towards the end customer. This switch escalates customization while decreasing standardization. These CODP and CADP shifts could sway planning parameters, processes and scheduling decisions. The intensified dependence on actual orders (due to CODP shifts) and customer-specific adaptations (due to CADP shifts) could enlarge schedule uncertainties, potentially exacerbating inaccuracies, primarily if not adequately managed.

In contrast, items at the early life cycle stage pose distinct challenges. Holweg (2005) suggested that larger variation and complexity in early life cycle stages – owing to new product introductions and ramp-ups – spur more extensive gross requirement changes, hence inflating delivery schedule inaccuracies across the time horizon. From the complexity perspective, Shurrab and Jonsson (2023) argued that interactions between product and process complexities and demand and supply chain complexities at various life cycle stages can amplify delivery schedule instability. This perspective reaffirms complexity theory’s stance on the dynamic and non-linear attributes of the variables involved. Thus, synthesizing our findings, we propose the following:

The order life cycle and production deviation variables show the decoupling role, in which variables' influence before and after decoupling points separate forecast-driven from non-forecast-driven requirements (Wikner, 2014) and frozen from non-frozen production plans. Several other variables can directly (as possible causes) or indirectly (as moderators) affect schedule inaccuracy, regardless of the time horizon (Shurrab and Jonsson, 2023). However, features impacting production process performance may affect the gross requirements and their fulfilment before the LDOC (i.e. before and in the hybrid decision zone of LDOC), as referred to by Wikner (2014). In contrast, process variables generating changes in planning parameters can change net requirements, affecting schedule accuracy before or even after the LDOC, depending on the variable in play. Such changes could be adaptations to customer orders but are typically internally generated and, therefore, unrelated to a CADP (Wikner, 2014). When they occur depends on internal time fences and planning policies. Previous studies have indirectly supported the contingency effect of the CODP. For instance, although delayed differentiation to final assembly operations (i.e. pushing back the CODP) enhances competitiveness (Blecker and Abdelkafi, 2006), Shurrab and Jonsson (2023) and Pujawan et al. (2014) found it also causes late changes to end-item specifications. Atadeniz and Sridharan (2019) found a negative effect of late demand increases and short frozen periods – a challenge also emphasized by Holweg (2005) – highlighting the criticality of decoupling point and time fence choices for delivery schedule accuracies. Consequently, we introduce the LDOC to represent a critical decoupling point and emphasize the importance of time fences for parameter changes in delivery schedule accuracy. As such, we propose the following:

The fourth and fifth categories in Figure 2 do not directly affect gross and net requirements but contribute to inaccuracies in other ways. The fourth type moderates the influence of inaccuracies multiplicatively. We identified some variables in this category as process design features, such as large unit loads, infrequent pick-up frequencies, local vacations, holidays and opening hours. They usually amplify generated (caused) variations, aligning with previous studies' results (e.g. Inman and Gonsalvez, 1997; Krajewski et al., 2005). Strikingly, we identified a significant predictive difference between these variables and that they may stabilize schedules by reducing variations on the short horizon while amplifying on the long horizon, or vice versa. Both the complexity and process theory perspectives motivate a combined direct and indirect effect of features on schedule inaccuracies. Still, the combined amplifying and stabilizing effects of the same variable have not been emphasized in the literature. This indicates a combined complexity absorbing and generating effect for the same variables. Consequently, we propose the following paradox:

The fifth category causes inaccuracies by disrupting the levelling of a plan. This effect is specific to the manufacturing strategy environment of our empirical study (i.e. levelled production in repetitive high-volume manufacturing). We did not directly study this fifth effect in the quantitative study. Still, examples of process design features were identified in the qualitative study: unit loads, pick-up frequencies and planned seasonality build-ups. The disrupting influence of these features is primarily due to synchronization and compatibility issues with the supply chain. These issues are typically attributed to process design features, such as increasing product complexities (Bozarth et al., 2009; Fernández Campos et al., 2019), constraints on capacity scalability and strict delivery terms (Shurrab and Jonsson, 2023) and process context features, for example, limited suppliers' flexibilities (Ponomarov and Holcomb, 2009). Accordingly, we propose the following: