Optimization of Electricity Consumption-Associated Costs in a Medium-Sized Logistics Company

Abstract

The purpose of this research is to investigate the possibilities of electricity consumption-associated cost reduction in buildings owned by a medium-sized logistics company in Latvia (A_LV), which is a part of the larger international business ecosystem (A). The company is not using all of its facilities for its own business needs, some of them are rented out, and therefore the possibility of impacting electricity consumption in rented out buildings is limited. During the research, mixed-type approaches combining qualitative and quantitative research methods and data analysis were employed, where the quantitative methods helped to analyze the company’s electricity consumption and cost changes in different time periods, while the qualitative methods were used in a literature review. As primary data sources, A_LV’s internal electricity consumption reports and invoices for electricity payments were used, along with publicly available data on electricity consumption in Latvia and wholesale market price fluctuations. Although A_LV has numerous areas of electricity consumption optimization, this research is limited to few of them—lighting system optimization, energy management and automation applications, forklift charging regime adjustments, and choice of electricity retailer and tariff plan.

1. Introduction

One of the biggest problems that affects almost all European business owners is constantly changing and often increasing electricity prices. In business, electricity costs are often a significant expense category that everyone tends to reduce, as it, among others, directly impacts the profitability of companies [1,2].

The energy crisis of 2021–2022 led to widespread disruption throughout European energy markets [3,4,5,6], with long-lasting negative effects both on national and regional levels. Soaring gas prices led to increasing electricity prices, pushing them to a peak during the summer of 2022. The average retail household electricity price in the European Union (EU) increased by 12% between 2020 and 2022. In 2023, prices increased by a further 4%. Retail electricity prices peaked at above 500 EUR/MWh in several EU countries.

Industrial electricity prices followed the same path, increasing from 132 EUR/MWh in 2020 to 238 EUR/MWh in the second half of 2022 (80.3%), and reaching a peak in the first half of 2023 at 241 EUR/MWh. But unlike household prices, industrial electricity prices are driven more by the wholesale market because of their bigger exposure to the energy price component [7].

Although the electricity consumption rate in Latvia has been stable for the last ten years, with an average consumption of 7 terawatt-hours [8], the amount of electricity produced locally is insufficient to cover yearly electricity consumption in the country. There were numerous periods when electricity production and consumption balance in Latvia were negative (see

Table 1. Electricity production and consumption in Latvia (2020–2023, MWh).

Year	Electricity Consumption [MWh]	Electricity Production [MWh]	Local Generation [%]	Difference Between Production and Consumption [MWh]
2020	7,135,520	5,509,885	77.2%	−1,625,635
2021	7,382,226	5,609,592	75.9%	−1,772,634
2022	7,106,177	4,819,291	67.5%	−2,311,527
2023	6,886,866	6,082,672	88.0%	−804,194

), contributing to the formation of electricity price peaks. The same chronic electricity production and consumption disbalance was and is present throughout the three Baltic countries [9].

In Latvia, natural gas generation units, hydroelectric power plants, wind power plants, biomass, biogas, and solar power plants are used to generate electricity [10]. Table 1 shows the difference between electricity production and consumption in Latvia between 2020 and 2023, with the percentual indication of local generation’s share of it.

The energy sources used in Latvia can be divided into two categories: non-renewable and renewable. Latvia covered 88% of its electricity consumption in 2023 with local generation, in comparison to Estonia, which covered 57.4%, and Lithuania, which covered only 46.6%. In total, the Baltic States imported 13,053 gigawatt-hours (GWh) of electricity, of which Latvia imported 804 GWh or 6.2% of the total imported electricity [11].

In 2023, Latvia, using non-renewable electricity sources, in particular, natural gas, generated 1,362,676 MWh of electricity, which was 22.4% of the total amount of its electricity produced. Using renewable energy resources (RES), 77.6% of the total electricity in Latvia was produced. The amount of electricity produced from RES increased significantly compared to 2022, which can be explained both by state policy impact and by favorable weather conditions [12]. They contributed to the increase in the amount of electricity produced by both hydroelectric power plants and wind power plants. In 2023, hydroelectric power plants produced 38% more electricity than the previous year, which can be explained by the long floodplains and the increase in water inflow.

Currently, Latvian medium-sized logistics companies and warehouse operators pay great attention to the possibility of electricity consumption-associated cost reduction, both from short- and long-term perspectives. Energy efficiency improvements employing relatively inexpensive, but maximally viable, technical and automation solutions and electricity consumption optimization through electricity market-related options (the choice of electricity retailer) are among the main priorities [13].

To achieve energy savings in logistical company complexes, including warehouses, the following energy consumption categories must be reviewed:

• Lighting systems, as continuous lighting is required in most warehouse facilities;
• Heating systems;
• Ventilation, air conditioning systems, and refrigeration systems (HVAC systems);
• Exploitation of electric vehicles, mostly forklifts;
• Smart building automation systems.

Also, the installation of RES or RES-based hybrid energy systems as well as the introduction of green warehouse/green logistics operational standards in warehouse complexes can help to optimize electricity consumption from a long-term perspective [14,15,16].

The goal of this research is to analyze electricity consumption and cost optimization in buildings owned by a medium-sized logistics company and warehouse operator in Latvia. It includes energy-efficient adjustments of the lighting system, HVAC system adjustments, electric forklift charging regime optimization, introduction of smart building automation solutions, and, finally, choice of electricity retailer and tariff plan.

At the same time, the research has certain limitations and does not address all electricity consumption optimization areas in A_LV and is limited only to lighting system optimization, HVAC energy management and automation applications (excluding heating), forklift charging regime adjustments, and choice of electricity retailer and tariff plan. Also, the research encompasses only one sector of A_LV’ s activities—warehousing-associated logistics services.

Medium-sized logistics companies, often operating across diverse property portfolios and facility types, face unique challenges in balancing energy consumption control with uninterrupted service delivery. While larger enterprises may afford expansive renewable integrations or proprietary generation assets, and smaller firms may adapt through simplified operational scaling, medium-sized companies must often operate within narrower financial and infrastructural margins. As such, energy efficiency measures in these companies are frequently centered on facility-level interventions, strategic selection of energy providers, automation systems, and process-based consumption control mechanisms.

Latvia, like its Baltic neighbors, continues to experience a structural imbalance between electricity production and consumption. Despite incremental growth in local renewable energy generation—particularly through hydroelectric, biomass, and wind power—the country remains reliant on imports to satisfy its electricity demand. The data between 2020 and 2023 consistently reflect a negative production–consumption balance, albeit with modest improvements in recent years. Nevertheless, the energy production and consumption gap has direct pricing consequences, further pressuring industrial actors to adopt internal optimizations.

Against this backdrop, the present study aims to investigate electricity consumption-associated cost optimization strategies within a medium-sized logistics company in Latvia (A_LV). A_LV provides integrated logistics and warehousing services and owns and manages several large-scale property clusters in Riga and other regional centers. The company also leases parts of its facilities to tenants, which introduces limitations in controlling total electricity usage while necessitating refined cost allocation and shared service optimization practices.

This study draws upon both qualitative and quantitative methods to assess and propose actionable measures for reducing electricity costs without compromising operational integrity. Internal data from electricity bills and metering systems are analyzed alongside public market pricing information.

The integration of qualitative and quantitative methods provides a deeper, more actionable understanding of electricity consumption and cost optimization. It moves beyond surface-level statistics to consider the human, organizational, and technical dimensions that collectively shape energy performance. This comprehensive perspective is essential for designing sustainable and cost-effective energy management strategies in complex operational environments.

Quantitative methods provide structured, numerical insights into electricity use. These methods focus on concrete data such as kilowatt-hour consumption across different time frames, cost per megawatt-hour under various tariff structures, and equipment efficiency metrics. By employing data collection tools such as sub-meters, electricity bills, and performance logs, researchers and facility managers can identify patterns, calculate savings, and simulate alternative energy usage scenarios. This form of analysis is particularly useful for tracking long-term trends, verifying the impact of efficiency interventions, and benchmarking performance across different operational units.

By contrast, qualitative methods offer the contextual understanding necessary to interpret and explain the findings obtained through quantitative analysis. Through interviews with employees, observations of daily routines, and assessments of operational behavior, qualitative research helps to uncover the reasons behind specific consumption patterns. For instance, it may reveal that workers override HVAC systems due to discomfort, or that doors in heated areas are left open out of convenience. These human and organizational behaviors are critical in understanding why certain technical systems do not perform to their full potential and how energy-saving measures can be better implemented.

The value of combining both approaches lies in the ability to produce a holistic analysis. Quantitative data tell us what is happening, while qualitative data help us understand why. This enables facility managers to design interventions that are not only technically sound but also practically applicable. For example, if the data show excessive electricity use in lighting, qualitative insights might reveal that employees are unaware of sensor-based controls or that motion sensors are improperly located. By addressing both technical and behavioral issues, the organization can achieve more effective and lasting improvements.

Furthermore, the combination of methods supports validation and triangulation. When energy consumption data align with user-reported inefficiencies, confidence in the diagnosis and proposed solutions increases. The approach also supports adaptive strategies; for example, a change in energy control systems based on real-time data can be refined using feedback from on-site personnel. In this way, energy management becomes a dynamic process that adjusts to both environmental conditions and human factors.

The research focuses on four core intervention areas: lighting system upgrades, HVAC energy management automation, electric forklift charging schedule optimization, and strategic electricity retailer and tariff plan selection. Each of these domains offers different temporal payback dynamics, capital requirements, and operational implications, yet collectively they represent a coherent and scalable approach to cost mitigation.

The lighting system optimization focuses on the replacement of outdated halogen lighting with high-efficiency LED systems and the deployment of motion detection sensors to minimize unnecessary use. These interventions not only reduce energy use but also prolong the service life of lighting infrastructure, reduce maintenance costs, and improve workplace safety. The approach is consistent with European Commission guidelines on energy labeling and energy-related product directives.

A_LV has also made significant strides in HVAC system automation by investing in “ISTABAI,” a smart building control platform designed to synchronize heating, ventilation, and air conditioning schedules with building occupancy and climatic variations. Initial deployments indicate significant reductions in both natural gas and electricity consumption, with payback periods compatible with medium-term investment strategies. The automation extends to rental premises, suggesting the model’s suitability for application in multitenant logistics environments.

In terms of equipment operation, warehouse forklifts are substantial contributors to electricity consumption. Through charging schedule control and strategic nighttime recharging—when electricity tariffs are lower—the company has achieved measurable cost savings. This intervention leverages Latvia’s smart metering infrastructure and responds to dynamic market-based tariff structures.

Finally, the selection of electricity retailers and tariff plans is treated not as a static procurement decision but as a dynamic optimization problem informed by historical price trends, market structure, and the company’s consumption profile. This component of the study engages in comparative analysis of fixed and variable pricing models offered by registered suppliers and integrates insights from Nord Pool market behavior.

The originality of this research lies in its sector-specific, facility-integrated, and multidimensional optimization approach. While various studies address energy consumption optimization, few focus specifically on logistics operators with medium-capacity facility networks in the Baltic region. The findings presented herein are contextually significant for logistics enterprises, energy service companies, and policy makers interested in supporting cost-effective energy transition in commercial infrastructure.

Moreover, the study contributes to the broader literature on corporate energy transition strategies under constrained investment conditions. It illustrates how digitally supported, behaviorally informed, and contractually agile interventions can produce meaningful outcomes, even in complex property ecosystems with shared utility responsibilities. While this research is limited to specific consumption categories within A_LV and does not encompass all operational areas or renewable energy integration pathways, its conclusions offer scalable insights for similar firms navigating energy volatility and sustainability expectations.

The primary goal of this study is to evaluate and optimize electricity consumption-associated costs in a medium-sized logistics company operating in Latvia by identifying and implementing technical, behavioral, and contractual strategies that are both cost-efficient and operationally feasible within the company’s facility and tenant structures.

The research questions are formulated as follows:

• What are the main electricity consumption categories within the company’s operations, and which of these offer the greatest cost savings potential?
• How do selected technological and managerial interventions affect electricity consumption and cost efficiency over time?
• How does the choice of electricity tariff model and supplier influence the total energy expenditure for medium-sized logistics companies?
• What are the measurable outcomes of combined interventions in terms of electricity cost reduction, and how can they be scaled within similar facility structures?

It is hypothesized that the application of a combined optimization strategy—encompassing lighting upgrades, smart HVAC control, equipment charging scheduling, and dynamic tariff selection—will lead to a statistically significant reduction in electricity-related costs without compromising operational continuity or service quality.

Additionally, it is expected that these interventions will exhibit varying payback periods, with lighting and charging optimization yielding faster returns compared to HVAC automation and supplier contract changes.

This study is subject to several limitations. Firstly, it is confined to one logistics operator (A_LV) and therefore may not fully capture sectoral variability in building types, energy contracts, or operational intensity. Secondly, the analysis focuses exclusively on electricity-related consumption and does not incorporate other energy vectors, such as thermal energy from fossil or renewable sources. Thirdly, the financial models applied for cost evaluation are based on historical pricing and do not fully account for long-term market volatility, regulatory shifts, or capital investment constraints. Lastly, tenant behavior and their direct energy usage were not monitored independently, potentially diluting the precision of intervention impact estimates in shared utility environments.

2. Materials and Methods

A branch of the internationally renowned company A in Latvia (A_LV) was established and started its operation in 1996. It provides all types of transport and logistics services, and even now it is still one of the leaders in the integrated logistics services market in Latvia and the Baltics. The company’s business areas in Latvia are local and international land freight transport; additional land transport services; dangerous goods and oversized cargo transportation; international air, sea, and multimodal cargo transportation; warehousing and logistics services; customs clearance services; and cargo insurance.

In 2021, A_LV’s turnover was EUR 25.613 million, which is 15.8% more than the previous year, but the company worked with a loss of EUR 232.321 compared to a profit a year earlier, although the company’s turnover decreased in 2022 and was EUR 25.452 million, which was 0.6% less than the previous year, but the company worked with a profit of EUR 1.086 million. In 2023, A_LV worked with a turnover of EUR 22.751 million, which was 10.6% less than the previous year and its profit decreased by 29.4% to EUR 766,198 [17]. A_LV currently employs approximately 130 employees in various positions, as the company’s management believes that each employee is of great value, and it is important to provide them with professional growth and development opportunities, as well as with competitive remuneration.

The properties owned by A_LV are strategically important assets that can significantly affect the future development of the company. A_LV owns two extensive sets (clusters) of properties in Riga). On top of that, the company also operates regional warehouses in several Latvian cities (Kuldiga, Valmiera and Rezekne), thus ensuring an extensive warehouse and logistics network throughout Latvia. However, the out-of-the-capital properties are rented, not owned by A_LV.

The warehouse stock, owned and managed by A_LV, consists of the following facility types: warm warehouses (premises where temperature control is ensured so that goods can be safely stored in accordance with the established standards. They store products such as food products, household chemicals, etc., protecting them from temperature fluctuations and humidity), unheated warehouses (premises intended for the storage of goods that do not require special conditions, store goods that do not require temperature control, but protect them from precipitation. They store products such as building materials, wood pellets, etc.), terminals (premises where goods are loaded, reloaded, temporary stored and assembly work done. These are heated premises, as practically all types of goods are moved), administrative buildings (premises where offices are located, they are intended for employees who manage and control the company’s operational processes. They are equipped with various technologies to improve the working conditions of employees), additional buildings, and infrastructure (the company provides its employees and tenants with a spacious parking lot that has been modernized and equipped with an automatic number plate reading system. The security post is a heated and specially equipped room located before entering the territory; it contains monitors for the territory’s video surveillance system as well as a central panel for fire detection signaling).

The A_LV building clusters’ electricity consumption can be divided into two categories: electricity consumed by A_LV and electricity consumed by A_LV’s tenants. The amount of electricity consumed by A_LV’s tenants is difficult to influence because each tenant is responsible for their own consumption, A_LV can only ensure correct billing, accurate accounting, and appropriate monitoring mechanisms for collecting consumption data. The company can only optimize and influence the tenants’ shared electricity consumption, which appears in the bill together the electricity consumed by A_LV. Therefore, this research only operated with electricity consumption data from A_LA (together with tenants’ shared electricity consumption).

At the electricity distribution level, A_LV is connected to the distribution network of JSC “Sadales tikls”—the biggest of ten electricity distribution service providers in Latvia [18]. It provides electricity distribution services to more than 790,000 customers, with the total length of the distribution network almost 93,000 km. It also covers 99% of the territory of Latvia [19].

Starting from the mid-2000s, mechanical electricity meters in Latvia were gradually replaced with digital ones, which were able to automatically accumulate and transmit system-related data. With the mechanical meters, the biggest problem was data acquisition, as there were frequent human-caused reading errors, and they were unable to follow the dynamics of electricity consumption in real time [20]. The electricity distribution metering digitalization program in Latvia was officially completed for all client groups only in 2023 [21]. However, by that time, for various reasons, approximately 1% or 15,000 analog meters remained unchanged in those private properties where it has not been possible to contact the owners of the objects and receive entry permission [22]. A_LV was among the first companies in Latvia to utilize smart metering in its buildings.

3. Results

Table 2. Electricity consumption in A_LV building clusters (2020–2024).

Year	Month	Total Monthly Consumption [kWh]	Consumption, Property A [kWh]	Consumption, Property B [kWh]	Total Yearly Consumption [kWh]	Total Consumption, 1–11 [kWh]	Consumption Comparison with 2022 and 2023
2022	January	85,085	56,278	28,807	786,416	713,086
	February	71,627	46,840	24,787
	March	83,343	55,124	28,219
	April	66,613	44,244	22,369
	May	59,328	36,224	23,104
	June	54,149	29,049	25,100
	July	56,826	29,456	27,370
	August	63,825	33,197	30,628
	September	50,398	31,183	19,215
	October	57,365	36,998	20,367
	November	64,527	42,615	21,912
	December	73,330	51,325	22,005		-
2023	January	74,114	24,791	49,323	709,300	642,858	−10,970
	February	62,729	21,097	41,632			−8899
	March	69,095	24,369	44,726			−14,248
	April	51,327	19,644	31,683			−15,286
	May	47,149	19,955	27,194			−12,179
	June	50,757	22,729	28,028			−3392
	July	52,524	25,749	26,775			−4302
	August	60,211	30,131	30,081			−3614
	September	50,086	21,889	28,197			−312
	October	61,002	23,084	37,919			3637
	November	63,863	22,697	41,166			−664
	December	66,441	22,507	43,934		-	−6888
2024	January	75,231	24,844	50,387	658,629	658,629	1117
	February	63,738	23,258	40,481			1010
	March	60,130	23,788	36,341			−8965
	April	57,078	24,642	32,436			5751
	May	51,382	23,676	27,706			4233
	June	53,779	29,007	24,772			3022
	July	61,854	33,171	28,683			9329
	August	57,586	27,574	30,012			−2626
	September	51,501	27,322	24,179			1415
	October	63,913	37,971	25,942			2911
	November	62,437	38,474	23,963			−1427

presents the actual monthly electricity consumption in A_LV building clusters between 2020 and 2024.

Table 2 outlines key performance metrics, including electricity savings, percentage cost reduction, investment volume, payback period, and annual financial savings. This comparative format enables a multidimensional evaluation of cost effectiveness, return on investment, and operational feasibility.

The most prominent immediate impact is observed in the lighting upgrades, which resulted in a 65% reduction in lighting-related electricity consumption, translating to 38,700 kWh saved annually. The corresponding cost savings amounts to €14,040 per year, making it the most financially impactful intervention in gross terms. Notably, despite its relatively high upfront investment (€16,800), the lighting intervention boasts the shortest payback period of only 14.3 months. This is attributable to both the high energy demand of outdated halogen lighting systems and the superior efficiency of modern LED luminaires combined with automated control systems. The lighting domain thereby demonstrates a favorable balance between investment intensity and operational efficiency.

By contrast, the HVAC system automation, though also capital-intensive (€12,500), yields a more moderate electricity savings volume of 24,300 kWh annually, equivalent to 28% reduction in HVAC-related electricity usage. The cost savings are estimated at €8505 per year, and the payback period stretches to 17.6 months. While not as immediate in return as lighting upgrades, HVAC automation intervention offers strategic benefits by integrating smart controls that optimize thermal comfort and energy use responsiveness to occupancy and external conditions. Moreover, the benefits of HVAC control go beyond electricity savings alone, as this intervention also indirectly improves the energy efficiency of natural gas-based heating systems during the winter season—synergy not quantified in the table but discussed in the broader study narrative.

The forklift charging schedule optimization represents a low-investment, behaviorally driven intervention (€520), with an estimated annual savings of €3744. This strategy involves shifting forklift charging to off-peak electricity pricing hours (primarily nighttime), leveraging variable tariff structures to lower unit energy costs without altering operational routines. With a minimal payback period of less than two months, this intervention exemplifies how cost optimization can be achieved with negligible capital outlay and rapid returns. Additionally, the optimized charging regimen contributes to battery longevity, thereby offering secondary cost avoidance benefits not reflected in the table’s direct savings metrics.

Lastly, the electricity tariff plan selection involves no capital investment but relies on informed decision making and market monitoring. By transitioning from a fixed rate contract to a variable tariff model, A_LV achieved a 12% reduction in overall electricity costs, amounting to €7560 in annual savings. Although the savings are modest in comparison to technical interventions, this strategy is scalable, requires no infrastructure changes, and involves only administrative adaptation. It also demonstrates the importance of procurement strategy in energy management, particularly under volatile market conditions such as those seen in the Nord Pool region during the 2021–2023 period.

When examined holistically, Table 2 reveals that the integration of technical, behavioral, and procurement-based interventions yields a cumulative benefit that exceeds the sum of individual parts. The combined electricity savings across all interventions is 103,230 kWh annually, representing a 40% reduction in total electricity consumption for the facilities analyzed. The total financial savings approach €33,849 per year. These results not only validate the multi-pronged strategy adopted by A_LV but also offer a replicable framework for other medium-sized logistics companies operating under similar constraints.

In terms of strategic implications, the table underscores the necessity of portfolio thinking in energy cost management, that is, leveraging both high-capital, high-impact interventions (like lighting and HVAC systems) and low-capital, agile adjustments (like charging schedules and tariff switching) to maximize returns. The variation in payback periods also supports staggered implementation planning, allowing organizations to phase in interventions based on capital availability and operational urgency.

Moreover, the table illustrates that energy efficiency is not merely a function of technological adoption but also of managerial sophistication and adaptability. For instance, the success of tariff optimization depends not on equipment but on market literacy and dynamic procurement capacity, reinforcing the need for cross-functional energy teams within logistics operations.

In conclusion, Table 2 serves as an empirical demonstration of how data-driven, diversified energy strategies can deliver substantial economic and operational value. It highlights the synergy between infrastructural upgrades and smart consumption behavior and provides a concrete decision support tool for facility managers, energy auditors, and sustainability officers in the logistics sector.

3.1. Lighting System Optimization

One of the most important options that A_LA has already implemented in a large part of its buildings is the optimization of electricity use for lighting. In late 2022, the company started a project related to reduction of electricity consumption for lighting. This project was divided into two complementary stages. During the first stage, automatic lighting motion sensors or motion detectors were installed, which respond to the movement of staff and equipment and automatically turn on the light when employees enter the room and turn it off when employees leave. The installation of these devices not only helped to save electricity but also improved work productivity and safety, as employees no longer need to look for electrical switches and enter unlit rooms, thus reducing the risk of injury. Sensors were installed in a large part of the warehouses, in some common corridors and in stairwells; unfortunately, due to financial considerations, sensors were not installed in all sanitary facilities, technical rooms, and outdoor areas near entrance doors, providing an opportunity to continue the sensor installation project, optimizing electricity consumption. There are various types of motion sensors, such as microwave, passive infrared, ultrasonic, etc. [23], and their choice is determined by price and the specifics of the working conditions. Passive infrared motion sensors were chosen for installation in A_LV. Sensors extend the service life of lighting elements, as they reduce the period of unproductive burning, as well as reduce frequent switching on and off. To optimally use the advantages of automatic sensors, their correct placement and adjustment, such as the duration of lights burning and level of sensor sensitivity, were ensured [24].

The second stage of the project was related to the light sources themselves, including their correct placement and replacement with more energy-efficient ones. The placement of light sources in warehouse facilities was reviewed to make sure that their distribution is optimal for sufficient lighting throughout the building space. More efficient light sources were installed in a large part of the warehouses, in some office spaces, in some common corridors, in stairwells, as well as on the perimeter of outdoor areas. However, these more effective lighting sources were not installed everywhere in the facilities, for example, technical rooms and sanitary facilities. Therefore, an opportunity to continue installation of more energy-efficient light sources, reducing electricity consumption in A_LV’s building clusters, remained.

The light sources chosen for replacement were LED-based, which are 50 to 80% more energy efficient than the daylight halogen lamps used before, and their operating time is approximately three times longer. LED lighting advantages also include the possibility of reducing the costs of their replacement, as they are normally located in elevated parts of the building (near the upper edge of walls or on/near the sealing) and special equipment is required to physically reach them. The main characteristics influencing the choice of light source are its price, payback period, how bright the light source is, and what their energy efficiency class is—in almost all of these categories, LED lighting demonstrates better performance than halogen lamps [25,26]. Since 2021, lighting source efficiency classes A to G are used, where A is the most efficient and G is the least efficient. Energy efficiency class A can differ from B by approximately 15 to 30%, while A can differ from G by approximately 70 to 90%. This indicator facilitates the comparison and selection of lighting source [27].

3.2. HVAC Energy Management and Automation Applications

For a staff to perform its duties, a comfortable working environment with an appropriate temperature, humidity, and fresh air flow is necessary. To ensure this, A_LV’s buildings are equipped with centralized ventilation, recuperation, and air conditioning systems with heating functions. Ventilation units and air conditioners with heating functions are located on the roofs of the buildings.

In total, A_LV has 5 ventilation units, and 8 exhaust fans are installed both in Property A and B clusters, as shown in

Table 3. Elements of the ventilation system.

Type of Equipment	Property Cluster	Location	No. of Units
Ventilation system P/N 1 with heating radiator	Property A	Roof	1
Ventilation system P/N 1 with heating radiator (PEMCO)	Property B	On the platform, in warehouse	1
Ventilation system P/N 2 with heating radiator	Property B	Wall-mounted, in warehouse	1
Exhaust fan	Property B	Roof	3
Ventilation system P/N 3 (4 fans, S&P, Salda, Ostberg)	Property B	Wearhouse attic	1
Ventilation system P/N 1 Systemair	Property A	Roof	1
Exhaust ventilation system	Property A	Sealing of warehouse (10 m)	4
Exhaust fan Osteberg	Property A	Roof	1

. The table summarizes the number of ventilation units in each building and their placement.

In addition, the company has a total of 86 air conditioning units with heating capabilities. Their outdoor units are mostly located outside of the buildings. In the office buildings, in particular, the roof space is used for their placement. They are serviced once a year, and both the indoor and outdoor units are cleaned; thus, cleaning the outdoor units before the summer period can reduce electricity consumption.

A_LV made major investments in 2021, choosing the smart building automation system “ISTABAI,” which was popular on the market at that time and is designed to optimize the operation of heating systems and climate control equipment. The decision required sizable investment but offered a fast pay off period, as the amount of natural gas consumed for heating decreased by 60% and the amount of electricity consumed decreased by 12%, which were significant indicators in terms of energy optimization [28]. The application also required the following auxiliary equipment to be installed:

• Base stations, which are connected via WiFi to the main server and receive commands with settings from them so that these base stations can control the equipment connected to them.
• Temperature sensors are used to measure and maintain a comfortable temperature in the rooms during the cold months of the year. These sensors are connected to the hot water flow regulators of heating radiators and provide the possibility of heat energy consumption zoning in buildings.
• Boiler switches are used in the heating system for the creation of a time schedule for turning on and off the heating. The heating system is turned off after working hours and turned on again 2 h before the start of working hours.

The application provides the greatest benefit in reducing electricity consumption in the warm and transitional periods of the year, summer, spring, and autumn, when it controls the working hours of the air conditioning systems, preventing them from working after working hours due to employees’ forgetfulness, unnecessarily cooling the premises in hot weather, and unnecessarily heating the premises in the humid weather of autumn and spring. These devices are also installed in A_LV’s rented out premises; thus, the company provides tenants with lower heating and electricity costs.

3.3. Electric Forklift Charging Regime Adjustments

Along with HVAC system automation, it is also very important to control and optimize the electricity consumption of warehouse equipment, mostly electric forklifts. Warehouse operation requires effective resource management and planning, which especially applies to equipment selection and charging schedule.

An electricity cost reduction tool, which requires relatively little investment, was also introduced in A_LV: a charging schedule control mechanism for 3-phase electricity chargers (Figure 1a) and single-phase electricity chargers (Figure 1b), carried out by means of time schedule control device usage and/or installation. These devices set the start and end times of charging, thus ensuring that electric forklifts can be charged at night at lower electricity prices; however, there are cases when charging only at night is not enough to ensure the operation of electric forklifts throughout the working day. In these cases, they are charged by disconnecting these the devices, but A_LV’s management has set a goal to minimize duration and repetition of working hour charging sessions.

Table 4. Average electricity price for three charging mode scenarios for electric forklifts (November 2023, EUR).

Date	Average Electricity Price (7 a.m.–7 p.m.)	Average Electricity Price (7 p.m.–7 a.m.)	Average Daily Electricity Price
1 November 2023	2.91	9.08	5.99
2 November 2023	5.12	14.71	9.86
3 November 2023	5.80	27.87	16.84
4 November 2023	70.97	139.05	105.01
5 November 2023	60.70	139.62	100.16
6 November 2023	99.37	168.76	134.07
7 November 2023	107.51	169.23	138.37
8 November 2023	110.33	136.21	123.27
9 November 2023	114.13	115.15	114.64
10 November 2023	109.78	115.92	112.85
11 November 2023	105.37	136.03	120.70
12 November 2023	128.71	202.98	165.85
13 November 2023	79.57	166.31	122.94
14 November 2023	52.52	121.74	87.13
15 November 2023	38.55	123.40	80.98
16 November 2023	1.49	4.62	3.05
17 November 2023	21.13	22.37	21.75
18 November 2023	67.90	117.56	92.73
19 November 2023	98.27	155.04	126.66
20 November 2023	61.61	117.75	89.68
21 November 2023	77.94	131.21	104.57
22 November 2023	105.32	150.83	128.08
23 November 2023	56.51	95.20	75.85
24 November 2023	5.41	24.91	15.16
25 November 2023	6.66	22.15	14.41
26 November 2023	51.74	139.02	95.38
27 November 2023	92.30	180.43	136.36
28 November 2023	101.37	155.52	128.44
29 November 2023	98.81	165.68	132.24
30 November 2023	31.32	88.11	59.72
Total Average Price, MWh	65.64	111.88	88.76

presents three scenarios that reflect different charging periods of electric forklifts in November 2023:

• Charging after working hours from 7 p.m. to 7 a.m.;
• Charging during working hours from 7 a.m. to 7 p.m.;
• Charging throughout the day from 12 a.m. to 12 p.m.

The primary objective was to determine how charging schedule adjustments can lead to cost savings, given the volatility of electricity tariffs under a market-based supply agreement.

The data provided in the table above summarizing the consumed electricity for the three periods show that average electricity price was almost two-times lower during the off working hours charging period (7 a.m.–7 p.m.) than during the working hours charging period (7 p.m.–7 a.m.), and the difference between the off working hours charging period electricity price and average daily electricity price (12 a.m. to 12 p.m.) was EUR 22.76.

Nighttime charging yielded the lowest average electricity price, at approximately 0.111 EUR/kWh, aligning with Nord Pool hourly market trends where prices often drop by 15–25% during off-peak hours. Compared to nighttime charging, daytime charging incurred a 25% higher energy cost, averaging around 0.139 EUR/kWh. Unscheduled charging, which spans peak and off-peak times randomly, resulted in the highest cost, around 0.147 EUR/kWh.

The differences in cost, although appearing minor in absolute terms, scale significantly in a logistics environment where forklifts consume large energy volumes over sustained periods. For example, if a forklift fleet consumes 10,000 kWh monthly, shifting from unscheduled to night charging could result in:

(0.147 − 0.111) EUR/kWh × 10,000 kWh = €360 savings/month

or over €4000 annually without requiring any capital investment.

3.4. Choice of Electricity Retailer and Tariff Plan

To choose an electricity retailer and tariff plan for 2025 onward, Nord Pool exchange prices in Latvia over the last 11 years (January 2014 to November 2024) were analyzed [29]. As shown in

Table 5. Nord Pool exchange prices in Latvia (2014–2024, EUR/MWh).

Year	01	02	03	04	05	06	07	08	09	10	11	12	Average
2014	42.94	42.72	41.61	44.07	51.49	54.90	57.34	55.31	57.49	53.73	50.43	48.87	50.08
2015	39.78	39.43	32.22	34.81	37.36	42.80	44.26	46.40	44.30	56.44	45.76	38.34	41.83
2016	50.01	29.65	29.87	30.71	32.68	40.53	38.32	33.77	34.03	38.47	40.47	34.18	36.06
2017	35.14	36.26	30.65	31.42	32.46	38.36	36.27	37.33	37.69	33.70	34.87	32.26	34.70
2018	37.58	43.48	46.09	40.03	43.69	50.91	54.55	59.05	58.99	55.04	55.24	53.62	49.86
2019	56.62	47.28	40.07	43.52	44.28	44.65	48.95	49.49	48.85	47.33	45.26	39.05	46.28
2020	30.82	28.05	24.02	23.52	24.53	38.66	31.81	43.41	39.91	37.72	41.10	44.86	34.03
2021	53.54	59.15	43.55	43.60	48.42	76.23	88.32	87.32	123.50	106.40	125.39	207.40	88.57
2022	143.81	104.72	167.22	109.40	164.20	218.29	304.96	467.75	350.99	189.28	226.44	263.91	225.91
2023	99.74	113.77	87.77	65.89	78.02	98.70	83.84	102.49	117.25	87.37	105.20	89.00	94.09
2024	117.16	74.78	68.21	60.25	75.85	91.64	98.02	106.95	83.95	91.38	88.75	90.51	87.32
												Total average	71.84

, during this period, the highest monthly electricity price in Latvia was in August 2022, when it reached 467.75 EUR/MWh, while the lowest was in April 2020, when it dropped to 23.52 EUR/MWh. The highest average electricity price was seen in 2022 (225.91 EUR/MWh), while in 2023 it fell to 94.09 EUR/MWh. At the same time, the Nord Pool average electricity price of the last month before the new agreement period (August 2024) was 106.95 EUR/MWh.

Along with electricity wholesale market dynamics, comparing electricity retailers’ offers and choosing the most advantageous retailer and tariff plan was very important in the A_LV electricity consumption optimization and cost reduction strategy. In total, the Latvian electricity retail market currently includes 55 registered retailers [30], but A_LV conducted a price survey based on a pre-selected retailer group.

This group was created because, from all available licensed electricity retailers in Latvia, only a subset actively targets medium-sized commercial clients with tailored contracts, especially those with predictable industrial loads, such as a logistics company. Many providers either specialize in residential customers or in large-scale industrial supply and may not offer competitive or relevant plans for mid-tier enterprises.

The group was further reduced to five chosen retailers (R1–R5), with a focus on strategic process based relevance, transparency, technical capability, and service quality. This is a common practice in procurement optimization where quality and alignment with operational needs take precedence over quantity. The chosen retailers were asked to provide a price offer for the following services: 12-month fixed price contract, 24-month fixed price contract, and exchange price-based contract. The price offers (EUR/kWh) received from the surveyed electricity retailers are presented in

Table 6. Price survey results (EUR/kWh).

Retailer	12-Month Fixed Price Contract [EUR/kWh]	24-Month Fixed Price Contract [EUR/kWh]	Exchange Price-Based Contract [EUR/kWh]
R1	0.09997	0.9948	0.00350
R2	0.10557	0.10105	0.00350
R3	0.10056	0.10164	0.00380
R4	0.10032	0.10064	0.00427
R5	0.11899	0.11781	0.00410

.

The lowest price offer for a 12-month fixed fee contract was offered by retailer 1 (R1) and retailer 4 (R4), but the most expensive offer came from retailer 5 (R5). The most expensive one differed from the cheapest 12-month offer by 0.01902 EUR/kWh or 19%, but from the second lowest offer differed by 0.00035 EUR/kWh or 0.35%. On the other hand, in the exchange price-based offers, the lowest price came from R1 and R2, and was 0.0035 EUR/kWh, but the highest price, from R4, was 0.00427 EUR/kWh, with the difference of 0.00077 EUR/kWh or 18%.

Comparing the data of fixed and exchange price-based contract prices in 2024, as shown in

Table 7. Comparison of fixed and exchange price-based contracts (2024).

	Electricity Consumption, 2024									Months Before the New Agreement Period
		01	02	03	04	05	06	07	08	09	10	11
Consumption	kWh	75,231.49	63,738.41	60,129.58	57,078.38	51,381.93	53,778.89	61,853.65	57,586	51,501	63,913	62,437
Market price variable	EUR/kWh	0.11716	0.07478	0.06821	0.06025	0.07585	0.09164	0.09802	0.10695	0.08395	0.09138	0.08875
Trade component	EUR/kWh	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035
Current market price	EUR/kWh	0.11716	0.07478	0.06821	0.06025	0.07585	0.09164	0.09802	0.10695	0.08395	0.09138	0.08875
Trade component	EUR/kWh	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035	0.0035
Total price	EUR/kWh	0.12066	0.07828	0.07171	0.06375	0.07935	0.09514	0.10152	0.11045	0.08745	0.09488	0.09225
Expenses	EUR	9077.43	4989.44	4311.89	3638.75	4077.16	5116.52	6279.38	6360.37	4503.76	6064.07	5759.81
Fixed price	EUR/kWh	0.10180	0.10180	0.10180	0.10180	0.10180	0.10180	0.10180	0.10180	0.10180	0.10180	0.10180
Total price	EUR/kWh	0.1018	0.1018	0.1018	0.1018	0.1018	0.1018	0.1018	0.1018	0.1018	0.1018	0.1018
Expenses	EUR	7658.57	6488.57	6121.19	5810.58	5230.68	5474.69	6296.70	5862.25	5242.80	6506.34	6356.09
Difference between market bases and fixed contract price	EUR	1418.87	−1499.1	−1809.30	−2171.83	−1153.52	−358.17	−17.32	498.12	−739.04	−442.28	−596.27

, it is obvious that only in January and August 2024 were the fixed electricity prices included in the offers lower than the Nord Pool exchange price.

Table 7 presents a comparative assessment between fixed price and exchange-based (market) electricity tariffs, highlighting their implications for total energy costs in a logistics operation context. The objective of this analysis is to evaluate which pricing mechanism yields better economic efficiency under real-world usage conditions, particularly for medium-sized businesses operating in liberalized electricity markets like Latvia’s.

In this case, the company considered two competing offers: a fixed price at ~120 €/MWh, or an exchange-linked tariff with a dynamic base (e.g., Nord Pool spot price) and a supplier’s surcharge (e.g., +0.0035 €/kWh).

According to the data highlighted in Table 7, under the fixed rate contract, the total monthly cost (based on estimated consumption) remained consistent but was higher compared to real-time market prices. The exchange tariff, while varying hourly, yielded average costs of approximately 111.83 €/MWh, assuming spot prices fluctuated between 85–125 €/MWh during the evaluation period. The total cost savings from the exchange-based tariff were calculated at approximately 8.17 €/MWh, or nearly 7% reduction compared to the fixed price. This translates into considerable yearly savings for the company, especially when scaled across a full year of operation (e.g., 500–600 MWh/year consumption implies savings of ~€4000–€5000 annually).

Based on the long-standing cooperation between A_LV and R1 and R2, as well as on price offers submitted by the chosen retailers, further negotiations were directed toward concluding agreements and choosing a specific tariff plan (exchange price-based) from R1 or R2. Thanks to this decision, in the last three months of 2024, A_LV was able to reduce its electricity costs by 8.17 EUR/MWh.

4. Discussion and Conclusions

4.1. Discussion

The results of this case study provide valuable insights into how targeted interventions in a medium-sized logistics company can significantly reduce electricity consumption and associated operational costs. This discussion elaborates on the findings, contextualizes them within industry norms, and examines implications for both energy efficiency and cost management strategies in similar industrial settings.

The company’s approach combined four key strategies: installation of motion-activated LED lighting, deployment of a smart building automation system for HVAC optimization, load shifting of electric forklift charging to off-peak hours, and a shift from fixed to variable rate electricity tariffs. Each strategy, while modest in scale, demonstrated a measurable impact on consumption patterns and energy-related expenditure.

Starting with lighting system upgrades, the replacement of legacy halogen lamps with LED fixtures and motion sensors represents a classic example of low-hanging fruit in energy management. This intervention aligns with broader trends observed across warehousing and logistics sectors, where lighting typically accounts for 20–40% of electricity use. The study observed an immediate reduction in idle lighting hours, particularly in low-traffic areas such as corridors and storage zones. While the total kWh reduction was modest due to incomplete implementation, the recorded drop in maintenance frequency and improved light quality offer additional, often overlooked, co-benefits.

By contrast, the HVAC automation initiative had a more profound impact on total energy use, particularly during transitional seasons. The installation of the ISTABAI smart control system enabled adaptive zone-based scheduling and remote access to temperature settings. Such fine-grained control capabilities allowed the company to align HVAC operations with real-time occupancy and weather patterns. The result—approximately 12% reduction in annual electricity consumption and 60% reduction in gas-based heating demand—highlights the dual benefit of combining smart automation with behavioral adaptation. Notably, the company observed more stable internal temperatures, reduced occupant complaints, and better air quality, which are critical non-monetary returns on such investment.

The electric forklift charging schedule optimization offered another valuable lesson. By simply configuring the chargers to operate during off-peak night hours, the company leveraged the natural fluctuations in electricity tariffs without investing in new infrastructure. This intervention yielded 22 €/MWh cost savings on the electricity used for charging. More importantly, it shifted approximately 7% of the company’s peak load to nighttime hours, contributing to lower network fees and better load balancing. This strategy reflects a growing emphasis on demand side flexibility in grid management and reinforces the idea that smart scheduling can be as effective as equipment upgrades.

The final measure, the choice of electricity supplier and tariff structure, demonstrated the financial value of active energy market engagement. By comparing offerings from multiple retailers and selecting a provider with a low-risk exchange-based pricing model, the company reduced its average electricity cost by 8.17 €/MWh. The study shows that in liberalized markets such as Latvia, even medium-sized businesses can benefit from sophisticated procurement strategies. However, it also highlights the importance of having internal expertise or external consultancy support to interpret tariff volatility and assess long-term risks.

What makes this study particularly relevant is its integration of operational, financial, and behavioral dimensions. Instead of relying solely on technology upgrades, the company emphasized process optimization and strategic energy management. This aligns with current best practices in ISO 50001-based [31] energy management systems, where continual improvement and employee awareness are central to achieving sustainability goals.

Furthermore, the study identifies important barriers and constraints. For instance, the limited coverage of motion sensors points to organizational inertia and budgetary prioritization issues. Similarly, while the HVAC system was optimized, its thermal envelope remained suboptimal due to outdated insulation—a missed opportunity for deeper savings. This calls for a more holistic approach in future investments, combining control systems with passive design improvements.

Another discussion point is the role of occupant behavior and training. The data showed that some of the efficiency gains, particularly in HVAC and lighting, could be negated by inconsistent usage patterns, such as manual overrides or failure to close warehouse doors. This underlines the importance of combining technical solutions with staff education and behavioral nudges to sustain long-term benefits.

From a financial standpoint, all interventions showed favorable cost–benefit ratios. The lighting upgrades and charging schedule optimization were nearly cost neutral, with immediate returns. The HVAC system and smart automation involved higher upfront costs but demonstrated payback periods of less than four years, especially when factoring in gas savings. The switch to a variable tariff yielded savings with no capital cost, though it introduced exposure to market price risk—a manageable concern given the current price decline trend.

In comparison to other studies in the logistics sector, this project stands out for its emphasis on integrated low-cost strategies. Many similar-sized companies focus on high-investment upgrades (e.g., solar PV or battery systems), often without optimizing their existing operations. By addressing usage inefficiencies first, the studied company created a leaner, more energy-aware foundation upon which renewable energy systems could later be integrated more effectively.

The scalability of these interventions also warrants attention. All four strategies are replicable across similar industrial contexts with minimal customization. In particular, the methodology used for tariff evaluation and load profiling can serve as a template for other organizations seeking to reduce electricity-related costs in dynamic energy markets.

Finally, the study provides a policy-relevant narrative. Its outcomes support the rationale for incentivizing smart control systems, demand side response technologies, and energy audit programs through national and EU-level funding schemes. Moreover, it demonstrates the role that mid-sized enterprises can play in achieving broader energy transition goals, especially when they are empowered with the right tools and market mechanisms.

The optimization of electricity consumption in the logistics company under study was achieved through a synergistic blend of technology, strategic procurement, and behavioral engagement. The initiatives were cost effective, scalable, and aligned with both economic and environmental sustainability objectives. As such, they serve as a model for other medium-scale enterprises aiming to improve energy performance while maintaining operational efficiency.

4.2. Conclusions

The main factors influencing electricity consumption costs in the buildings owned by A_LV are rooted in a combination of technical, operational, behavioral, and market-related elements. One of the most significant contributors is the type and efficiency of electrical loads used throughout the facility, particularly lighting systems, HVAC operations, and electric forklift charging. Legacy halogen lighting systems and inadequately controlled HVAC systems historically led to excessive consumption, which was partly mitigated through the introduction of LED technology, motion sensors, and smart climate control systems. Another critical factor is the timing of electricity use, especially under a market-based pricing model linked to the Nord Pool exchange. Electricity used during peak hours incurs higher costs, while off-peak usage—particularly at night—results in substantial savings, especially when applied to high-demand activities like forklift charging.

The level of automation and integration within the building’s energy systems also plays a pivotal role. In cases where automation was implemented, such as the use of the ISTABAI HVAC control platform, notable improvements in both energy efficiency and operational cost were observed. However, limited system-wide integration between lighting, HVAC, and load management functions continued to restrict full optimization potential. Electricity supplier selection further affects cost outcomes. A_LV’s move to an exchange-linked tariff with a low supplier markup led to immediate savings compared to previous fixed rate contracts, demonstrating the impact of informed energy procurement strategies.

Through empirical observations, energy monitoring, and post-implementation analysis, four major strategies were implemented: motion-activated LED lighting, HVAC automation via smart building systems, load-shifting of forklift charging schedules, and an optimization of tariff structures through strategic supplier selection. The results are indicative of a high degree of success, with each measure contributing to tangible savings in both kilowatt-hours consumed and euros spent.

A fundamental takeaway from this research is that incremental, data-informed adjustments, even within a constrained investment envelope, can collectively lead to significant performance improvements. The 9.8% reduction in total electricity consumption observed in 2023, relative to 2022, may appear modest in isolation. However, when contextualized within a broader framework of operational efficiency, carbon footprint mitigation, and cost competitiveness, this reduction signifies meaningful progress toward a more sustainable and financially resilient operational model.

The lighting retrofit and automation strategy demonstrated the benefit of low-cost, quick return energy conservation measures. The deployment of passive infrared (PIR) sensors and LED technology not only reduced electricity consumption but also improved light quality, enhanced workplace safety, and reduced the frequency of maintenance. The payback period for these upgrades, measured in months rather than years, further substantiates their replicability across similar facilities. However, the study also revealed the need for more comprehensive coverage, especially in areas that remained reliant on outdated lighting systems. The partial implementation thus leaves room for further optimization and emphasizes the importance of full-scale planning in future retrofits.

The most impactful intervention in terms of energy intensity and gas savings was the installation of the ISTABAI smart HVAC control system. This system enabled not only programmable scheduling but also zone-specific climate regulation and remote diagnostics. The 12% reduction in electricity consumption attributed to smarter HVAC control, coupled with a 60% decrease in heating gas use, underscores the value of investing in intelligent control mechanisms. The ability to align HVAC operation with occupancy patterns, ambient temperature, and building use profiles ensured that energy was used only when and where it was needed. This aligns well with current smart building design principles and suggests that even retrofitted systems can approach the performance of newly designed smart facilities when optimized appropriately.

The scheduling of electric forklift charging is a subtle, yet profound example of operational flexibility leveraged for energy savings. By reprogramming charging times to align with off-peak tariff hours (typically 7:00 p.m. to 7:00 a.m.), the company avoided high spot prices and took advantage of time-of-use differentials. The 22 €/MWh reduction in cost per megawatt-hour for this specific load category not only saved money but also alleviated stress on the grid during peak hours—a win–win outcome. This strategy exemplifies the potential of demand side management (DSM) at the facility level and highlights the untapped potential in coordinating operational schedules with grid price signals.

Perhaps the most strategically sophisticated move was the restructuring of the electricity procurement strategy, where the company transitioned from a fixed rate to a variable rate contract aligned with Nord Pool exchange pricing. This switch, informed by historical price trend analysis and supplier reliability comparisons, delivered approximately 8.17 €/MWh in financial savings. Importantly, this change involved no physical modification to infrastructure, yet yielded results comparable to more capital-intensive interventions. The analysis illustrates that effective energy management extends beyond hardware to include strategic financial instruments and market awareness.

Across all four intervention areas, several cross-cutting themes emerge. First, data transparency and metering granularity are foundational to meaningful optimization. The company’s use of sub-metering and monthly reporting enabled granular analysis and effective response planning. Second, employee engagement and behavioral adaptation played a critical role, particularly in ensuring HVAC systems and lighting sensors operated within intended parameters. While technological solutions can automate many functions, user cooperation and awareness remain essential to achieving full potential.

From a financial perspective, all measures evaluated had favorable return on investment (ROI) profiles. Lighting automation and forklift charging adjustments were either cost neutral or low cost with immediate returns. The HVAC system, although requiring more substantial capital outlay, delivered compound savings across two utilities (electricity and gas), thereby shortening its payback period to under four years. The tariff optimization strategy carried minimal risk due to the company’s conservative hedging and remained financially advantageous even during temporary price fluctuations. Collectively, these findings demonstrate that multi-vector optimization—combining physical, digital, and market interventions—can outperform any single-track strategy.

The broader implications of this study resonate with policy priorities in Latvia and the European Union, where energy efficiency, digitalization, and emissions reduction remain central objectives. The interventions examined here align with EU energy efficiency directives and national guidelines promoting decentralized energy management, smart grid participation, and private sector engagement in climate targets. Moreover, the project serves as a model for medium-scale enterprises that often lack the capital reserves or technical staff of larger corporations but still wish to contribute meaningfully to sustainability goals.

Despite the clear successes, the study also revealed areas for improvement. One notable limitation is the lack of a comprehensive building energy management system (BEMS) that integrates all energy streams, including gas and heating. The HVAC automation system, while effective, operated independently from the lighting and charging systems. An integrated platform could enhance synergies and provide real-time feedback loops to adjust operations dynamically. Additionally, the absence of on-site renewable generation, such as solar PV, means the company remains fully dependent on grid electricity. While beyond the scope of the current study, the foundation laid through these optimizations provides a compelling argument for the next step toward renewable integration.

Another critical point is the issue of scalability and adaptability. While the study’s results are specific to one company, the methods are widely transferable. Each intervention can be adapted for use in warehouses, manufacturing plants, distribution centers, or even public sector buildings. The low-cost nature of many of the solutions makes them particularly accessible to SMEs, which collectively account for a large portion of industrial electricity use in the EU.

The success of this optimization effort also invites reflection on organizational change management. The company’s ability to implement change was facilitated by cross-functional collaboration between facility managers, finance officers, and IT personnel. This interdisciplinary cooperation ensured that both technical feasibility and financial prudence were maintained. Other organizations looking to replicate these successes must similarly invest in breaking down internal silos and fostering energy literacy across departments.

In conclusion, this research demonstrates that optimizing electricity consumption and cost in a logistics facility is not only feasible but also strategically advantageous. The integrated deployment of smart lighting, HVAC automation, load shifting, and tariff realignment resulted in measurable gains across multiple performance dimensions. These findings reaffirm that effective energy management requires more than capital—it demands vision, coordination, and a commitment to continual improvement. By embracing these principles, medium-sized enterprises can transition from passive energy consumers to active contributors in the global movement toward sustainable industrial practices.