The literature identifies a wide range of root causes for BWE, stemming from a combination of behavioral, operational, and structural factors, main BW drivers demand errors , items price, and lead times fluctuations ( Khan & Ahmad, 2016; Michna & Nielsen, 2013; Bhattacharya & Bandyopadhyay, 2011; Kelepouris et al., 2008; Zhao & Wang, 2008; Paik & Bagchi, 2007; Moyaux et al., 2007; Alony & Munoz, 2007; Croson & Donohue, 2006; Lee et al., 2004; Akkermans & Vos, 2003; Khosroshahi et al., 2016; Sterman, 2010). Nine key causes have been identified for the construction supply chain: price fluctuation, demand forecast updating, lack of transparency, lead-time, number of echelons, machine breakdown, shortage gaming and rationing, workloads, and capacity limits ( Bhattacharya & Bandyopadhyay, 2011; Khan & Ahmad, 2016; Michna & Nielsen, 2013).

The negative implications of BWE are substantial and have been widely documented in various industries. These include excessive inventory, product shortages, setup inefficiencies, idle and overtime labor, scheduling difficulties, strained supplier relationships, and reduced customer satisfaction ( de Almeida et al., 2015; Disney & Towill, 2003; Lee et al., 1997). The costs associated with these inefficiencies can be significant, making the bullwhip effect a prominent concern in global operations management, particularly given the increasing complexity of modern supply chains ( Ferdows, 2018). Recent studies have sought to analyze demand patterns and assess the role of information sharing in identifying the root causes of this phenomenon ( Yao et al., 2021; Montoya & Gonzalez, 2019). The consequences of this effect are profound, leading to a range of inefficiencies, including excessive inventory, resource misallocation, strained supplier-customer relationships, and increased operational costs, which can reach up to 25% in some industries ( Ouyang, 2007). Extensive research has examined BWE and its causes, identifying key drivers such as demand forecast updating, order batching, price fluctuations, and rationing ( Lee et al., 1997). Studies have demonstrated that fostering collaboration and integration among supply chain partners is crucial for effective mitigation ( Braz et al., 2018; Alony & Munoz, 2007). Timely corrective actions can significantly reduce the magnitude of this effect by more than 50% ( Wisner et al., 2021). However, the inherent complexity of modern supply chains, which often involve hundreds of interconnected organizations, makes achieving such a level of collaboration a persistent challenge. Although the bullwhip effect has been extensively studied in the manufacturing and industrial sectors, there is a critical gap in understanding its unique implications within the construction supply chain. This industry is particularly vulnerable to demand distortions owing to its high complexity, project-based procurement practices, uncertain lead times, and variable demand. These characteristics have direct consequences for cost overruns, project delays, and overall resource inefficiency.

Various measurement and mitigation strategies have been proposed. The magnitude of the bullwhip effect is typically quantified using variance ratios such as the ratio of order variance to sales variance ( Duan et al., 2015; Khosroshahi et al., 2016). Other methods include comparing production and sales variations ( Shan et al., 2014; Bray & Mendelson, 2015; Cachon et al., 2007) and analyzing the differences between order and demand variances ( Bray & Mendelson, 2012; Chen & Lee, 2012). These measurement techniques have allowed for the empirical analysis of BWE and its different types, including shipment, manufacturing, and order bullwhips ( Jin et al., 2017). Reducing bullwhip effects through mitigation strategies often focuses on increasing visibility and improving communication and collaboration with enhanced trust through supply chain management partners. ( Dai et al., 2017; de Almeida et al., 2015). Additional factors for mitigation include pull systems, reduced lead times, improved inventory control, and smoother ordering patterns ( Braz et al., 2018). The vulnerability of global supply chains to bullwhip effects has been further highlighted by the recent crises. For example, the swine flu outbreak ( Manners-Bell, 2023) and the COVID-19 pandemic ( Baldwin & Tomiura, 2020; Patrinley et al., 2020; Xu et al., 2023) demonstrated how demand shifts and disruptions can intensify BWE, emphasizing the need for robust and resilient supply chain designs.

Price fluctuations are one of the main causes of the Bullwhip Effect. It generally occurs because of coupons, promotions, and discounts on products, which leads to more orders than needed for customers ( Disney & Lambrecht, 2008). After that, the services or products selling is stopped owing to saturation, and when it returns to the regular price, the orders will be less. Therefore, the orders do not show the real demand, and the fluctuation in consumption is much lower than the variation in bought quantities. Accordingly, the Bullwhip Effect occurs owing to this variation ( Chayjana, Rabbani, Razmi, & Sangari, 2021). In the construction industry, price fluctuations occur owing to different local and international forces that impact the costs of materials, labor, and tools. Azhar et al. (2008) revealed that the top three factors that cause overrun costs in construction projects are related to pricing: the price fluctuation of materials, unstable costs of materials, and high equipment costs. Price fluctuation is a common problem in the construction industry, and it results in the Bullwhip Effect, delays, and cost overruns. In addition, Omoregie and Radford (2006) indicated that price fluctuation was the most severe reason for the large increase in the project’s total cost for contractors, agents, and consultants. Pricing in the construction industry may be unit price, lump sum, or negotiated. The selected type was based on the contract and the estimated duration of the project. According to Bunni (1991) in FIDIC sub-clause 13.8, there are adjustments for cost changes during the project if the parties have established a table in the “Appendix to Tender,” which is a part of the contract in the tender documents. Applying this sub-clause includes adjustments for all paid with the falls and rises in the prices of materials, labor, and other things related to the work. The table is to be filled with the contract papers containing the materials that would follow this sub-section, and the calculation of the reduction or increasing amount is based on formulas in the FIDIC.

Therefore, the contract can save many troubles if all essential materials are listed in the appendix to tender, but price fluctuations can damage the owners, subcontractors, and contractors by causing the Bullwhip Effect, which affects the suppliers. However, in many cases, price fluctuations are not mentioned in the contract, and most of the materials are bought at the start of the project, which may cause a high increase in the demand for these materials, which can also be a cause of the Bullwhip Effect.

Demand forecasting is linked to the pricing process, and can be used in pricing decisions. Regularly, managers react to stabilize orders from downstream levels, not directly from the market. Suppose the downstream actors use only one echelon inventory to forecast and optimize the inventories. In that case, the decision-makers in the upstream supply chain react to stabilize the orders, which causes the Bullwhip Effect in the entire supply chain Figure 5 below ( Habibur, Nawazish, Monirujjaman, & Rafiquzzaman, 2014).

According to O’Brien et al. (2008), the uncertainty in the construction supply chain is less than the uncertainty in the manufacturing supply chain because the latter has seasonality in consumption, innovation, and competition, which requires advanced strategies to forecast demand. However, according to Davis (1993), the construction supply chain and its customer demand are fairly stable because of planning and quantity surveying, which provides suppliers with sufficient time to estimate the demand precisely. The construction supply chain is complex because it contains many suppliers, contractors, and subcontractors, as shown in Figure 7. In addition, forecasting is essential for preparing plans, deciding tasks, and allocating different resources (materials, laborers, and equipment). The planners for any project usually prepare a master plan to predict milestones, site layout, and resource scheduling. Subsequently, more specified plans are prepared to manage and control the construction process and resources. The most common way for procurement is to buy large quantities and install them in the worksite or in a warehouse, which may cause an issue in matching the demand during project execution that leads to the formation of the Bullwhip Effect ( O’Brien, Formoso, Ruben, & London, 2008).

This means that when data from downstream in the supply chain are available and clear to those upstream, it helps in coordination and sharing between different levels. Such cooperation can effectively reduce the Bullwhip Effect. By contrast, when information is not transparent or shared, conditions for the Bullwhip Effect are more likely to occur. The data that should be shared with different parties are historical demand records and orders from downstream levels. Geary et al. (2006) revealed that “work progress, inventory, and flow rate” item data, and making real-time decisions must be available and accessible to all SCM employees. Thus, delays in transmitting information are reduced along with the guesses ( Dahlin & Säfström, 2021). Sharing information can be defined as creating a stronger connection between a company and its suppliers. This sharing contributes to improving quality, reducing customer complaints, and increasing customer satisfaction ( Shaikh, Shahbaz, Din, & Odhano, 2020). Behera et al. (2015) considered the lack of information flow between different levels of supply chains as a major problem in the construction industry. Consultants, contractors, subcontractors, suppliers, and clients exchange incorrect documents and data. The features of information flow in the construction supply chain are slow, recreated between trades, and lack of sharing and using IT tools. In contrast, the manufacturing supply chain is highly shared, integrated, and fast ( O’Brien, Formoso, Ruben, & London, 2008). Additionally, Hellani (2021) stated that for the supply chain, the main challenge are the (transparency, trust, and traceability issues), resulting in negative customer feedback. See Figure 8.

Lack of transparency is a major problem in large projects because of their complicated supply chains. This leads to poor decisions, poor coordination and communication, high levels of waste, an unsafe working environment, labor dissatisfaction, and variability ( Brady, Tzortzopoulos, Rooke, Formoso, & Tezel, 2018). This lack of information flow may cause the Bullwhip Effect in the construction supply chain, which may be mitigated by wide transparency systems that eliminate guessing or waste and reduce the uncertainty in the system ( O’Brien, Formoso, Ruben, & London, 2008).

The lead time is described as the duration between the order placement and new service or product delivery. This indicates that when the product’s lead time is short, the customer can get it soon, which reduces the Bullwhip Effect. Many researchers have studied and developed an understanding of lead-time in the Bullwhip Effect ( Habibur, Nawazish, Monirujjaman, & Rafiquzzaman, 2014). Alony and Munoz (2007) states that lead time variance does not start the Bullwhip Effect, although it exacerbates it. The lead time is the submission of the material order, customer, and production lead times. In summary, this is the total time from order to delivery of the product ( Rheude, 2021). Figure 9 shows the effect of the lead time.

In all types of industries, item and equipment delivery lead times are defined as the time calculated between the (ordering and complete delivery of the materials). The lead-time must be estimated precisely to obtain an accurate procurement plan. The procurement of materials with longer lead times requires more attention owing to higher risks. The lack of lead-time determination can result in the late or earlier delivery of materials to the construction site ( Kar & Jha, 2021). The longest lead time in a construction project is the critical path, which is the chain of activities from start to finish with no free float ( Zhai, Zhong, Li, & Huang, 2016). Construction projects require a large variety of materials and tools with different lead-times. The researchers prepared different tables for the average lead-times for each item, as illustrated in Table 5. In addition, project planners and managers estimate the lead time values at the beginning of each project to prepare the procurement plan ( Zhai, Zhong, Li, & Huang, 2016). However, the calculated lead times may change owing to weather, holidays, strikes, transportation methods, market conditions, etc. The variance in the lead time increases the uncertainty, which can be solved by ordering the long lead time items before or increasing the risks of delaying the project. These two results cause uncertainty and fluctuations in demand, which increase the Bullwhip Effect in construction projects ( Kar & Jha, 2021).

Also the Figure 10 below shows the average lead-time for different materials used in the construction industry.

The echelons should have a minimum number to achieve suitable supply chain objectives. Higher levels of echelons mean more time for information processing and require more time to deliver services or products to customers. This number varies from one supply chain to another. There are up to six levels, whereas in other supply chains, there are up to three levels. Moreover, large supply chains have a higher probability of misunderstanding and collaboration issues, which can lead to mistakes in determining actual demand ( Rahman, Rahman, & Talapatra, 2020). Construction projects have long supply chains with a high number of echelons, which causes many issues in information and material flows from upstream to construction sites. In addition, a higher number of echelons leads to a stronger Bullwhip Effect on the upstream levels.

Different members of the supply chain are based on each other. An issue or stop at any level of the chain can affect all members and cause chain distortions. Therefore, a machine breakdown in the upstream supply chain results in a product being hampered downstream. The demand in the subsequent stages cannot be fulfilled by the supplier when the machine is broken. Then, the retailer cannot provide demand to customers, which arises from the Bullwhip Effect ( Habibur, Nawazish, Monirujjaman, & Rafiquzzaman, 2014). The manufacturing supply chain is mostly used for highly automated machines, robots, production routes, and standardization. However, the variability of production in the construction industry varies due to the productivity and availability of labor, lack of standardization, weather conditions, complex material flow, and space availability ( O’Brien, Formoso, Ruben, & London, 2008). Moreover, equipment planning includes selecting the equipment, working shifts, size and number of machines, technical staff, and planning for their storage and repair. The site also plays an essential role in the selection of the soil type, site location, and condition. Equipment in the construction industry generally requires high initial investments from contractors and special technical teams for maintenance. Any breakdown in large machines can delay the project by months until the contractor finds the replacement or fixes it. In addition to the loss of time, materials, and costs due to these issues ( Adik & Bobade, 2018). Figure 11 shows the causes of the equipment breakdown.

Most of the issues that lead to machine breakdown are usually simple to analyze, and the reason for the failure is clearly fixed. However, in some cases, failures are intermittent, which causes a sudden stop in construction or production, leading to large losses and delays. These irregular problems are categorized into three main groups. First, the failures are due to thermal issues, which occur when there is a considerable change in the temperature, such as extreme hot and cold weather, and when the machine experiences a large change in temperature. Second, construction equipment and machine mechanical failures are initiated by different drivers at construction sites. Finally, erratic failures are the hardest to predict and require a long time to be identified. This is caused by overloads in hydraulic and electric systems. These issues cause machine breakdown in construction projects, leading to the Bullwhip Effect in the supply chain (H.O. Penn, 2021). For instance, a mixer breakdown because of a technical issue at a construction site may stop the construction process entirely until the machine is repaired or replaced. Then, the demand will increase rapidly, which causes demand fluctuation that leads to the Bullwhip Effect.

A shortage in supply makes buyers want to secure their needs by following the “shortage gaming,” which is a strategy from customers to deal with the shortages. On the other hand, rationing is the supplier’s reaction towards a gaming shortage. In shortages, customers increase their orders to cover their future consumption. However, suppliers satisfy buyers through rationing. This causes variability in orders and supply chains ( Bhattacharya & Bandyopadhyay, 2011).

The construction industry often faces material shortages because there are many materials in construction projects. According to MarCom (2021), 71% of contractors face a shortage in at least one material, mostly lumber, steel, and lighting supplies. These shortcomings make contractors want to secure their materials by ordering larger quantities to avoid any delay in the project. The suppliers of these materials will increase their production to meet the new demand, which leads to variation in the demand-supply relationship and causes the Bullwhip Effect ( Kumar, Srinivasan, & Tanwar, 2013). Moreover, shortages affect the lead times, which also causes the Bullwhip Effect ( MarCom, 2021).

Working hours can cause the Bullwhip Effect because a higher working rate means more mistakes and difficulties in achieving quality, which results in more rework in the project. Thus, there is an increasing workload for reworking and fixing problems. This causes delays in the process and fluctuations in the supply chain because the Bullwhip Effect is sometimes estimated by backlogs instead of completed works ( Alony & Munoz, 2007). The construction industry is facing a shortage of skilled laborers; about 92% of the contractors have reported that they have some difficulties in employing skilled laborers ( U.S. Chamber of Commerce, 2021). Therefore, workers in this field face high pressure to work overtime. However, this may increase their income, but it is not good for job quality and the health of laborers. Working for long hours causes a lack of focus and energy, leading to errors in production or construction. Thus, the quality of work will decrease along with production and profits ( Kessler, 2020). Moreover, Long (2021) stated that mistakes in construction projects result in more reworking, machine breakdown, and delays. The aforementioned reasons, which are common in the construction industry, can cause fluctuations in production and affect productivity. This variability in production causes variability in demand, which disturbs the supply chain and initiates the Bullwhip Effect. Capacity limit. Factories and manufacturers generally provide services and products to the downstream members. When the factory works in full capacity in a certain period of production and cannot satisfy demand, there will be a lack of services or products downstream. Then, the manufacturers take more time to meet the demand of products, cause erratic orders in the downstream members, and create the Bullwhip Effect ( Habibur, Nawazish, Monirujjaman, & Rafiquzzaman, 2014). For each factory or project, there is a capacity limit that cannot be exceeded because of available tools, equipment, workforce, and materials. Whenever the construction team reaches the maximum capacity without meeting the planned schedule, it will require increased productivity and material production. The Bullwhip Effect occurs owing to productivity and demand fluctuations.

In this section, the Bullwhip effects (BWE) or Bullwhip Effect Measure (BEM) is calculated for some case studies in construction projects, which depends on building a mathematical model that takes into account the variance of both sales and orders (which depends on forecasting the sales) and the initial values of BW.

The bullwhip effect considered as the main problem in the SCM, where excessive orders fluctuations increase time & costs loses and as basic the demand fluctuation can be measured and analyzed on Variability bases, as stated by Lee et al. (1997) where defined changes of the “variance in demand σ ² upstream” for non changing flow units But at same time Chen et al. (2000) recommend that BWE measured by new formula where” the ratio of σ ²/μ upstream of the supply chain, where μ is the expected the value of the intensity of flows, as shown in below formula: Bullwhip = σ 0 2 σ D 2 (1)

These variability issues and trade-offs were studied by Dejonckheere et al. (2003), where the items and equipment demands are managed as an uncertain factor in the project contingency plans; thus, enriching the SCM information is vital to decrease the BWE, so the actual variation replaced by the measure of the relative dispersion to the stream line with ( Chen et al., 2000) Equation 1 above, statistical refinements by ( Chen et al., 2000) where new calculations used Equation 2, where the researchers finally express the formula of the variance equation of orders that can be easily analyzed in spread sheets as follows: Var ( x ) = ( n − 1 ) ( − x ¯ ∕ n ) 2 + ( x n − x ¯ ∕ n ) 2 n − 1 (2)

Another refinement was performed for the bullwhip effect measurement (BEM) to the t period, BEM _t , by using the conditional expression presented in Equation (3) below and used in numerical case studies ( Parra-Pena et al., 2012; Chen et al., 2000). BEM = { BEM t − 1 + var ( Order ) vav ( Sales ) if orders − sales ≠ 0 (3)

In the above equation, BEM was calculated as a “cumulative value for each period”. In summary, Table 6 shows a comprehensive summary of the causes, impacts, and mitigation policies regarding BWE in the construction industry.

The Bullwhip Effect (BWE) in the construction industry is not precipitated by a single factor but emerges from a complex, interconnected system of causes that is deeply embedded in the sector’s structure and practices. These causes can be categorized into three primary dimensions: informational, operational, and behavioral. Informational root causes, such as demand signal processing and lack of transparency, create a foundation of uncertainty and poor visibility, distorting the flow of accurate demand data in the supply chain. Operational drivers, including order batching, lead-time variability, machine breakdowns, and capacity limits, introduce physical delays and inefficiencies that mechanically amplify order variance. Finally, behavioral factors, such as price speculation, short-age gaming, and risk aversion methodologies cause supply chain decision-makers to increase their orders on purpose at early stages of sensing risks, and now this behavior and actions become routine acts and make the problem worse. These factors are interconnected. When information and communication are unclear, people react in ways that make operations even less stable. As the problem is built into the system, solving it requires a broad approach. requires a holistic strategy that addresses data transparency, process coordination, and contractual incentives simultaneously, rather than tackling individual symptoms in isolation.

A comparative analysis of the existing literature reveals a significant disparity in Bullwhip Effect (BWE) research. Although the phenomenon is extensively documented and modelled within the manufacturing and logistics sectors, scholarly inquiry into its construction-specific manifestations remains underdeveloped. Prevailing studies often extrapolate general supply chain principles without adequately accounting for the intrinsic characteristics of construction projects, such as their temporary multi-organizational structure, fragmented stakeholder landscape, and tender-based procurement models. Consequently, there is a critical absence of empirical studies that quantitatively measure the Bullwhip Effect’s magnitude within the construction context using real-world or secondary data. Furthermore, while a constellation of potential causes has been identified anecdotally, the current body of knowledge lacks holistic, systems-based analysis. The relative impacts of these causes and their complex interrelationships and feedback loops within the construction supply chain are not yet understood. The prevailing approach often involves the application of manufacturing-derived theories without sufficient adaptation to the transient, contractually complex, and project-centric nature of construction operations. This research aims to address these critical gaps by focusing on the Bullwhip Effect, which causes investigation for the construction industry and synthesis, and to analyze and validate the causes of the BWE identified in the construction fragmented literature, which results in identifying and exploring novel, construction-specific causal factors that remain shortly unaddressed in existing frameworks. The interdependencies and dynamic feedback loops between these causal factors are analyzed to understand their systemic nature. and developed a quantitative model to measure the Bullwhip Effect and construct a conceptual framework for understanding its propagation within construction supply chains. By achieving these objectives, this research provides a foundational and holistic understanding of the dynamics of the Bullwhip Effect in construction. This will ultimately enable the development of targeted evidence-based strategies to dampen its amplification, thereby fostering a more stable, efficient, and cost-effective construction supply chain. This study empirically focuses on the construction sector in Kuwait, providing a specific context for modelling and analysis.

The conceptual framework synthesizes the reviewed literature and illustrates how the main drivers of the Bullwhip Effect interact within a construction supply chain. It also highlights the role of managerial practices and technology adoption in mitigating the amplification of demand, Proposed Conceptual Framework shown in Figure 12 below:

This framework demonstrates how demand-side variability, combined with supply side constraints and management inefficiencies, leads to a Bullwhip Effect. Mitigation strategies include interventions that can reduce amplification and improve supply chain performance.

Case-study analysis

This case study analysis is based on cement sales and order quantities, measured in tons, across the case study period from the data of Mostashar United Company-Kuwait. The BEM is a key metric in this analysis, calculated using the expressions presented in Equations above (1), (2), and (3) ( Lee et al. (1997), Chen et al. (2000), Dejonckheere et al. (2003), and ( Parra-Pena et al., 2012)). All calculations and data presented in Table 6 and Figure 13 were performed using Excel spreadsheets. The discrepancy between the sales and orders of cement quantities indicates the existence of the Bullwhip Effect. Figure 13 graphically illustrates the demand volatility between the Sales and Orders over the time period explain the difference between sales and orders in cement lots, bullwhip effects exist also where Figure 14 shows the cumulative increase in the Bullwhip Effect Measure (BEM) over time for the studied case. The Bullwhip Effect Measure (BEM) Calculation Report. The analysis of the Bullwhip Effect (BWE) is based on the comparison of the variability in the order stream versus the variability in the demand stream. The following formulas are referenced in the literature and used in this case study:

Equation (1): Variability Basis where the BWE was initially measured by the ratio of variance to the expected flow upstream, as proposed by Chen et al. (2000). BWEα σ 2 μ Upstream of supply chain (4)

Equation (2): Variance Equation of Orders where this formula represents a statistical refinement for analyzing the variance of orders in a spreadsheet environment. In its most common form (ratio of variances): BWE = σ order 2 σ Demand 2 (5)

And finally Equation (3): Cumulative BEM for Period t ( BEM _t ) where this conditional expression is used for the detailed, period-by-period (cumulative) calculation, where the measure is based on the Coefficient of Variation CV ² to account for the relative dispersion. BEM _t is calculated as a cumulative value for each period t. BWE t = C V 0 , t 2 C V D , t 2 = σ 0 , t 2 σ D , t 2 ∕ μ 0 , t 2 μ D , t 2 (6)

D _t: Demand (Sales Ton) at period t

• O _t: Order (Orders Ton) at period t

• μ _t: Cumulative Average (Mean) up to period t

• σ ² _t: Cumulative Sample Variance up to period t

Since there is a clear difference between the order quantities and the sales quantities of cement, the Bullwhip Effect appears to have increased over the case study time period, as reported by Onuoha (2018) for Electronic Components and by many other researchers. Therefore, better forecasting and more accurate calculations of both sales and orders may reduce such differences and the bullwhip effect. The final cumulative BEMt for the entire 31-day period (t = 30) is 0.9933. Since the cumulative BEMt is less than 1.0 (0.9933 < 1.0), the data suggests the overall supply chain is experiencing a smoothing effect (or reverse bullwhip effect), rather than the traditional Bullwhip Effect. This means that order variability is slightly less than demand variability when measured relative to their respective average flows. However, the detailed period-by-period analysis shows high Bullwhip Effect periods (e.g., BEMt =1.5910 at t = 15) interspersed with periods of smoothing, which together result in a near-neutral cumulative effect. The fact that the final BEM is very close to 1.00 indicates that the relative variability between orders and sales is highly balanced over the entire case study period.

A deep review of the literature, combined with perceptions from our case study analysis, identified numerous strategies to decrease and mitigate the Bullwhip deep Effect in industries and supply chain, organized into four primary areas of focus or Core Strategic Pillars. This strategic structures was similarly proposed by Onuoha (2018) in his doctoral dissertation, and the strategies were confirmed effective in minimizing and mitigating the bullwhip effect in the electronic component supply chain. These strategies—Collaboration and Enhanced Teamwork, Optimizing Information Exchange and Communication, Building Supply Chain Resilience Against Component Shortages, and Strategic Planning for Resource Utilization—have demonstrated their reliability in decreasing the Bullwhip Effect (BWE) and serve as a valuable reference guide for the construction industry. Strategy 1: Fostering Collaboration and Enhanced Teamwork Among SCM Partners. This strategy emphasizes the need for robust supply chain collaboration. The enhancement of teamwork between Supply Chain Management (SCM) partners is crucial, particularly where the supply chain leaders’ trust is an important issue. Trust can be built through negotiated agreements and information-sharing policies, which enable effective collaboration and provide insights into long-term forecasts. Pataraarechachai and Imsuwan (2017) also highlighted collaboration as a critical strategy for mitigating the drivers of the bullwhip effect. This requires adopting the Integrated End-to-End Network Approach, which relies on manufacturing cycle process flow charts from each participant’s company to support the end-to-end approach as a valid collaboration strategy. Raza and Kilbourn (2017) noted that a strong end-to-end network partner collaboration strategy can lead to improved operational performance, with Schoemaker and Tetlock (2017) emphasizing the importance of a sustained collaboration strategy to remove operational waste, such as the bullwhip effect, and gain a competitive advantage. Another approach is Operational Process Alignment (Real-time Data Integration), which uses infrastructure systems to acquire and evaluate channel partners’ point-of-sale data, feeding real-time aggregated sales and order data into integrated network systems. Cannella et al. (2018) noted that leaders should leverage infrastructure systems to achieve real-time information sharing concerning demand requirements, component availability, and factory capacity, ultimately reducing lead-time concerns, achieving on-time delivery, and mitigating the bullwhip effect. In correlation to the Conceptual Framework, Bullwhip effect theorists note that organizational resources are the source of sustained business performance as well as operational disruptions ( Pfeffer & Salancik, 2015). Generally, a strong collaboration strategy can improve internal and external resources, reducing the bullwhip effect and positively influencing optimum end-to-end operational performance also Stategy 2: Optimizing Information Exchange and Communication Among SCM Partners. A robust communication strategy is essential to reduce the bullwhip effect, which is often driven by a gap in demand and capacity information sharing. Tieman and Darun (2017) found that efficient supply chain management requires a steady communication flow between network partners, and Cannella et al. (2018) addressed the information-sharing gap by proactively managing the input and output demand information. Leaders often employ digital communication systems to improve the speed of demand input and output information transfer, leveraging several sub-stategys. One is the Beer Game Simulation Strategy, which involves using the beer game as part of the supply chain culture to provide employees with visibility into the adverse effects of the bullwhip in the supply chain, necessitating demand forecast planning. This simulation strategy helps supply chain human resources gain firsthand experience on how the bullwhip effect works, acquiring skills to proactively monitor and manage its behavior. The beer game is a powerful communication tool for managing the complex bullwhip effect, minimizing total supply chain costs, and reducing adverse consequences. Bandaly et al. (2016) supported this, noting that supply chain leaders use simulation strategies for early identification and mitigation of bullwhip effect triggers. This also aligns with the findings of Shukla and Naim (2017) regarding the use of an educational approach to foster regular cross-competence training and form personal relationships, which is vital for network partner satisfaction, making them more motivated to participate in efforts to reduce bullwhip effect triggers. Another key sub-strategy is the Inventory Level Monitoring System. Monitoring inventory levels provides an early warning signal against “shortage gaming,” which is used by some channel partners to secure buffer material in anticipation of a demand surge. Debnath et al., (2017) advocated for supply chain leaders to use inventory level monitoring as a communication strategy to rationalize demand signals transmitted to and from network partners, while Ma et al. (2017) noted that an effective leader uses a communication strategy that includes appropriate management of inventory levels at each network process step to ensure support for unanticipated demand surges and to reduce the bullwhip effect. Finally, Forecast Management System (FMS) Reliability is crucial. The strategic supply flexibility plan documents how each participant positions flexible inventories at separate geographically diverse locations, driven by customer consumption rate. Ahmad and Zabri (2018) assured that FMS dramatically improves ordering systems, and Choudhury (2020) found that the forecast management system is a powerful communication tool for sharing forecast information to reduce demand variability implications while sustaining partner relationships, business growth, and competitive advantage. Onuoha (2018) found that supply chain leaders use input- and output-vetting techniques to reduce demand variability. An integrated forecast management system facilitates faster communication with network partners, reduces the gap in customer demand information flow, and dampens the bullwhip effect ( Schoemaker and Tetlock, 2017). Govind et al. (2017) noted that supply chain leaders use demand forecasting management metrics and integrated communication systems to monitor and manage the bullwhip effect behavior within the network. In correlation to the Conceptual Framework, bullwhip effect theorists suggest that supply chain leaders should anticipate some degree of operational disruptions, but use effective strategies to mitigate the effects of demand variability and component shortages ( Perrow, 2011; Tieman and Darun, 2017). Cannella et al. (2018) explained that network partners can reduce the information-sharing gap driven by unanticipated demand surges by proactively managing input and output demand data, vetting the data, and communicating the information to partners in a timely manner. Kache and Seuring (2017) suggested that leaders employ synchronized communication systems, such as databases, to improve information transfer speed and reduce the bullwhip effect where Stategy 3 established by: Building Supply Chain Resilience Against Component Shortages. A critical challenge is developing strategies for component shortage reduction to minimize the bullwhip effect. Bounou et al. (2017) acknowledged that inefficient operations have an adverse economic consequence, namely a reduction in revenue generation stemming from component shortages and production waste like idle employee time. Moraitakis et al. (2017) noted that lead time, shortage gaming, and order batching are driving factors that amplify the bullwhip effect within the supply chain network. Substategys of the Component Shortage Reduction Strategy include: lead-time and cycle-time optimization, process integration, technology and flexible manufacturing, factory capacity and equipment management, prioritization, operational cost reduction, and logistics management. Stategy 4: Strategic Planning for Optimized Resource Utilization. Effective resource management is a powerful strategic initiative for supply chain leaders. Research by Ingy Essam (2017) noted that implementing a resource management strategy is an effective means for supply chain leaders to improve operational performance, system capabilities, and information communications, all of which result in bullwhip effect reductions. Marhamati et al. (2017) suggested that supply chain leaders could use IT systems to source electronic components from low-cost countries and distribute them across several regions to mitigate component shortages and reduce the bullwhip effect. Sub-strategy’s of resource management strategies include leveraging IT systems and human resource engagement ( Onuoha, 2018).

The strategies to reduce Bullwhip Effects are compared with qualitative data from semi-structured interviews (15 participants, Table 2) and survey responses (50 respondents, Table 3). The Mean Q1 Impact Score is 3.12, indicating a moderate to high perceived impact of the Bullwhip Effect among respondents, with a standard deviation of approximately 1.48 and a mode of 5 (10 respondents indicating the highest impact). The most frequently cited causes in Q2—Demand Forecast Updating (11), Rationing & Shortage Gaming (8), and Machine Breakdown (8)—and main mitigation suggestions in Q3—Collaboration (11), Information Sharing (8), and Resource Planning (8)—closely align with the four Core Strategic Pillars: Collaboration (Strategy 1), Information Sharing (Strategy 2), Resource Planning (Strategy 4), and Shortage Resilience (Strategy 3). The survey and interview results strongly support these strategic pillars. The consistent mention of ‘resilience,’ ‘shortage mitigation,’ and ‘transparency systems’ across both data sources highlights the practical relevance of the framework’s four core strategies for mitigating the Bullwhip Effect.

The bullwhip effect poses a significant challenge for construction supply chains as it amplifies variability, disrupts planning, and leads to delays and cost overruns. Several interrelated strategies can be implemented to address this issue. The first step is to identify and monitor the bullwhip effect by conducting systematic assessments of inventories, both on-site and off-site, including suppliers, to detect early signs of demand distortion and material bottlenecks ( Wisner et al., 2021; Henneberry, 2021). Alongside monitoring, the development of balanced and adaptive procurement plansgrounded in both historical and forecasted demand data ensures that material requirements are anticipated accurately and updated regularly ( Singh, 2018). Enhanced information sharing is critical for improving transparency and collaboration at all supply chain levels. Providing stakeholders with access to demand forecasts, inventory status, and future orders enables better coordination and reduces miscommunication, thereby lowering uncertainty ( Disney & Towill, 2003; Croson & Donohue, 2006; Disney & Lambrecht, 2008). In parallel, lead time management is essential, and long lead times are a significant cause of increased demand fluctuations. By aligning procurement planning, logistics operations, and supplier collaboration and by implementing quicker production and delivery processes, the bullwhip effect can be reduced by up to 80%. ( Michna & Nielsen, 2013; Henneberry, 2021). Another effective strategy involves adjusting the order policies. Rather than placing large, infrequent orders, managers should adopt smaller, more frequent purchasing cycles to avoid excessive inventory build-ups and demand fluctuations caused by price volatility ( Jaipuria & Mahapatra, 2014). Complementing this, supply chain simplification, by reducing the number of suppliers and echelons, enhances communication, accelerates information flow, and minimizes opportunities for demand distortion ( Rahman et al., 2020). Operational reliability also plays a critical role in mitigating bullwhip effects. Providing regular training for equipment operators ensures the proper use of machinery and reduces errors that can lead to delays or breakdowns. At the same time, implementing a preventive maintenance plan prepared by qualified engineers and technicians minimizes equipment failures and safeguards the continuity of material supply (H.O. Penn, 2021). Construction supply chains need to brace themselves for wide-ranging disruptions. Embedding crisis readiness into contingency plans, especially by factoring in the bullwhip effect, strengthens risk management. Advanced simulation and modelling tools such as agent-based modelling empower organizations to explore scenarios and craft robust strategies for navigating crises, including pandemics. ( Borshchev & Filippov, 2004). In summary, mitigating the bullwhip effect in construction requires a multifaceted approach that combines proactive monitoring, improved communication, optimized procurement practices, and reliable operational management. Through collaboration, transparency, and strategic resource management, construction firms can significantly reduce demand variability and strengthen the resilience of their supply chain. In summary, the Bullwhip Effect (BWE) in the construction industry does not arise from a single factor, but rather from a complex interaction of causes that have a critical impact on the bullwhip and are embedded within the construction sector’s structure and practices. These causes are grouped into three main dimensions: informational causes include (updating demand forecasts and lack of transparency, which create uncertainty and poor visibility; they also mess the flow of accurate demand information throughout the supply chain. Operational factors, including order batching, variability in lead times, equipment breakdowns and failures, and capacity constraints, contribute to delays and inefficiencies, thereby amplifying order variability and Behavioral factors such as price fluctuations, scarcity-driven gaming, and risk aversion, causing decision-makers to place larger orders as a reaction to perceived uncertainties, which in turn leads to the establishment of amplified demand. The case study conducted in this research demonstrates that BWE can be quantitatively measured and has significant effects on material ordering costs and sales in construction projects. Findings reveal that the bullwhip effect intensifies over time if mitigating strategies are not implemented, the bullwhip effect directly escalating production costs. Several strategies have been identified that can effectively reduce the bullwhip effect in the construction industry, such as collaboration, improved communication, minimization of component shortages, and effective resource management. Finally, a set of strategies is provided to prevent the occurrence of the Bullwhip Effect and reduce its value.