Shared Power Bank Station Cost Control Exploration with Index Evaluation Method

The sharing economy of power banks has grown rapidly; it was a period of rapid expansion driven by the market, and now it is a mature industry that can only survive if it can improve its own efficiency. In this case, how to control costs for the company will directly affect its profits and whether it can remain competitive in the long term. Unlike traditional manufacturing, the cost structure of shared power bank stations is high initial investment in hardware purchase and then has high operating costs for regular maintenance, venue rental, etc., and is subject to change according to use, showing new operational characteristics different from traditional operating models ^[4]. Therefore, the previous way of reducing costs based on experience will not work, and a more scientific and quantitative way to manage finances is needed.

The Index Evaluation Method can solve such problems theoretically and practically. Basically, this method will construct a multi-level, hierarchical, and all-round system of quantitative indicators for the various cost centres to evaluate, monitor and improve resource distribution. The core idea is to turn the subjective reasons for the increase in cost into measurable indicators and then make an objective comparison with the average value in the industry or the company's historical performance. Assigning weight to the index is based on how much each one will be affected in the total operating cost; decision-makers will be able to find out which areas have low efficiency, decide on rectification measures and allocate funds reasonably.

In actual use of the Shared Power Bank operation, the above will be used to determine the indices. First, a few main expenses, such as depreciation of the equipment, rental of the place, payment for electricity from the grid and costs of repair work, will be broken down. Then, according to industry rules and the company's own financial goals, the basic target will be set. Thirdly, the actual operating data will be continuously compared with the above indicators to calculate the operating deviations. Finally, according to the degree and nature of the deviation, a relevant cost control measure will be taken. Therefore, the above system is not only an emergency measure but also proactive and data-driven. The purpose of this way is to provide an objective and verifiable basis for the station's strategic decisions in light of the cost savings achieved; thus, service quality and equipment readiness will not be sacrificed unintentionally, and the station will be able to maintain its operations, which is in line with the refined operation trend of the current shared power bank industry that requires the establishment of a multi-dimensional financial evaluation system for micro-unit assets to break through the bottleneck of extensive management ^[1].

According to the entire life cycle operation process of shared power bank stations, the main cost parts can be divided into three logical large groups based on the actual operation of enterprises and the results of on-site investigations.

The first group is the cost of hardware and daily maintenance. It consists of the start-up cost of a charging cabinet and a power bank module, as well as the regular repair and replacement costs of various components. They are fixed investments and the costs of regular operations. Materials, logistics, and direct labour expenses for technical staff at the site will be incurred, as these are fixed costs during a certain period of operation.

The second group is the Site cost and user diversion cost. The primary expenses are venue rental fees paid to cooperatives, along with marketing costs for attracting and retaining users such as promotional subsidies and advertising fees. The venue rental fee is generally a fixed income-sharing percentage or a fixed amount over a certain period, and the marketing expense varies according to the size of the campaign. Therefore, the venue rental has fixed costs in the contract period and high variability in marketing expenses, which is closely related to the market expansion plan.

The third category is energy consumption and equipment lifecycle costs. The cost of power to operate the power station and the depreciation or amortisation of hardware assets during their useful life are included in this study. Electricity cost varies according to how often a user rents and charges, and it is a variable cost. Depreciation is calculated based on the service life and salvage value of the asset on a straight-line basis, and thus, it is a fixed cost.

The fixed-cost items are those that do not change with changes in the number of rentals, such as depreciation and relatively stable site fees; variable-cost items, such as electricity and new marketing expenses, vary according to changes in the amount of business activity. To avoid ambiguity in the data of the above index evaluation system and empirical analysis, the scope of statistics for each part is clearly defined. Hardware costs are based on the time when the warehouse is shipped, and site costs are based on the time when a contract is established; energy costs are calculated according to the time when electricity is measured, and lifecycle costs are based on the time when capital is invested. Based on this precise boundary, the data for the cost control assessment will be collected objectively and repeatedly.

Hardware and daily maintenance are major variable costs in the operation of shared power bank stations; to control them accurately, we need to develop a set of quantifiable indicators for operational cost. The first is the amount of repairs required by each station within a certain period, divided by the total repair cost. The Data is collected in the form of an enterprise asset management system; generally, a reasonable reference threshold should be below a certain amount of money per Station per month. The second index is the average Fault response time, which is the average time difference from the fault report to the arrival of the technician. Based on the Timestamps of the Dispatch system, the threshold should be less than a few hours to ensure service continuity. The third index is the annual Hardware damage replacement rate; that is, the number of damaged hardware components replaced each year divided by the total number of deployed components. Based on the Inventory replacement logs, generally, the threshold is set to be lower than a certain proportion to avoid loss of funds. The fourth is the daily on-site inspection labour cost per station; that is, the sum of all labour wages for the day is divided by the number of inspected stations. Based on the workforce management records, the threshold is set with reference to the regional minimum wage standard and the efficiency of route optimisation. The fifth index is the per-unit Maintenance consumable cost of a single power bank; that is, the total number of power banks that have been maintained is divided by the total amount of Consumables used. Based on the Warehouse requisition form, a threshold is set based on the discount for bulk purchases. The above indicators are closely related to the overall cost control; for example, a long average Fault response time will increase the unit's hardware Failure maintenance cost due to secondary damage, and an excessive annual hardware damage replacement rate will increase both the Consumable and labour costs. The above indicators can reflect the real operation of hardware management and daily maintenance from different angles, providing a clear and quantifiable basis for subsequent overall cost evaluation and targeted optimisation measures.

The first-line expenditures to be funded are site occupancy and user diversion costs in the multi-dimensional cost control index evaluation system of shared power bank stations. Five quantifiable indicators will be built to better measure the input-output efficiency of these investments.

The site rent per unit effective user flow is calculated by dividing the monthly site rent by the monthly active borrowing users of the station. It is determined by dividing the financial lease records of the property management system by the backend user transaction data, and the industry reasonable reference range is 0.5 to 1.5 CNY per user. It directly shows how well spatial resources are being used economically.

The user acquisition cost per new registered user at the station is calculated by dividing the total user diversion expenditures by the number of new registered users in the period. The data are from the marketing expenditure ledgers and the user registration database. The reference range is usually 3 to 8 CNY. It is used to assess the direct financial efficiency of diversion investments.

The conversion rate of passing traffic to paid borrowing users is used to measure the momentum of operation, and it is derived from paid borrowing actions divided by the estimated number of people at the station. The data are based on sensor-based footfall counts and order logs, with a reference range of 2% to 5%. To see if the site occupancy cost will increase or have no revenue.

The annual site contract renewal cost fluctuation rate is used to assess whether the rent is stable; that is to say, what is the year-on-year change in rent when the contract is renewed. Based on the comparison of lease agreements, a reasonable range is 0% to 5%. Too much fluctuation indicates that the occupancy cost is rising uncontrollably.

The cost of offline promotional materials for user diversion is the amount of tangible marketing funds spent, which is divided by the total procurement cost of physical goods and the distributed quantity. Based on procurement invoices, the reference range is 0.5 to 2 CNY per unit. It is required that the physical diversion expenses be proportional to the localised degree of participation.

The first part of the system weight allocation is to give a high weight to the cost of site rent per unit of effective user flow and the conversion rate of passing traffic to paid borrowing users. They are the reasons for the transformation of occupancy into revenue. The second is the change in the cost of station maintenance and renewal each year; the third is the additional operating cost for offline materials. So, in the second part of the attribution, the evaluation system will focus more on the overall input-output efficiency rather than individual cost reduction.

The long-term operation of shared power bank stations has to face the problem of high energy consumption and large equipment life cycle costs. Five specific indicators are needed to build a quantifiable evaluation index system. First, the average power consumption of a single station in a year is the baseline energy efficiency, which is the total power consumption of the station divided by the number of years of operation. Second, the unit kWh cost for each charging cycle of the power bank is the energy cost efficiency, and it is the total electricity expense divided by the accumulated effective charging volume. Third, the average service life of a single shared power bank is the total operating months divided by the number of retired power banks. Fourth, the equipment residual value recovery rate at the end of its life is the value of the asset recycled at that time, calculated as actual residual revenue divided by initial procurement cost. Fifth, the idle loss rate of underutilised stations is a measure of wasted capacity, and it is the number of unrented station hours divided by the total available station hours. Due to different measurement units, the data are standardised by the extreme value method, and all indicators are mapped to a dimensionless interval from zero to one. Optimised benchmark values are based on the norm of the industry and the best level of the past, to provide a clear goal for cost reduction. These indicators are strongly related to the total cost of the full-life operation. Higher power consumption and idle loss rates will gradually reduce profits, and the shorter life of the power bank will also increase the amount of capital expenditure. On the contrary, a high residual value recovery rate can cover the disposal cost at the end of life. They are of guiding significance for the operators. Based on the deviations from the benchmark values, the operator can actively carry out equipment renewal planning and allocate funds to replace the damaged power banks in a timely manner. In addition, high unit kWh costs and idle loss rates are directly related to stations that need energy conservation transformations; therefore, a reasonable adjustment of charging algorithms or relocation of underutilised stations will be made to control costs in the equipment life cycle.

Selecting representative samples and standardizing the collected data are necessary conditions for guaranteeing the validity and accuracy of the empirical evaluation. 30 typical shared power bank stations in a second-tier city were chosen as research samples for this paper. These stations are mainly in the areas of shopping malls, dining halls, office buildings and scenic spots. This sample selection is quite reasonable and representative in that it includes all sizes of operations, various places and different numbers of people. Shopping malls and scenic spots generally have high and concentrated foot traffic, but catering shops and office buildings have different times when they are used and their flow is not very large. With all kinds of environments covered, the sample can reflect the variety of operating conditions and cost structures of the urban shared power bank network well.

Then, twelve months of actual operation data of these 30 sample stations were collected. This dataset covers all the cost indicators mentioned above to maintain the continuousness and systemic nature of time. However, due to the different dimensions and units of the collected data, direct comparison or aggregation will affect the result. For this reason, all the collected data have been processed by standardised dimensionless processing. A uniform, dimensionless scale will be created through mathematical calculations so that all variables with different units can be fairly compared.

In addition, to avoid biased results, outliers due to temporary emergencies, such as station closure for a short period due to store renovation or power failure, have been excluded from the dataset. To make sure that the data are only in normal operation and not caused by abnormal reasons, some abnormal data points need to be removed. After rigorous screening of the samples, dimensionless processing, and outlier removal, a standardised, unified dataset for analysis was finally obtained. The revised data will ensure that the index weight calculation and empirical evaluation in the later part are more reliable and accurate, thus providing an empirical basis for optimising cost control strategies.

The core of deriving cost control improvement paths is to turn the evaluation data into operational management rules. According to the Analytic Hierarchy Process and Entropy Weighting Method, the weight of each cost control index is calculated objectively and comprehensively, so that the subjective judgment of experts can be properly combined with the objective data. Based on the standardized dataset, all selected sample stations have been comprehensively assessed for cost control performance and ranked. According to the final scores, these stations are divided into three grades: good, medium and poor. Based on the indicator scores of these levels, the main problems in cost control at the stations are determined accurately. Low-tier stations usually have low levels of depreciation control and maintenance frequency, while average-tier stations have poor energy utilisation and low space leasing efficiency.

Therefore, the targeted and operable cost control optimisation paths for different station scenarios have been concluded. The first path for low-tier stations is to strengthen hardware lifecycle management and predictive maintenance scheduling to reduce the excessive depreciation and repair costs. For average-tier stations, the required path is dynamic rental fee negotiation based on the utilisation rate and the application of energy-saving operation modes. A simulation of the above strategies was carried out with a weighted evaluation model. The simulation results show that the operation cost of the shared power bank station can be reduced by 12%-18% by adopting the proposed optimisation path. This quantifiable reduction will prove that the derived path is reliable and show that the index evaluation method can be used to manage the cost of shared power bank stations to provide a standardised and evidence-based decision-making system for industry operators.

Based on the index assessment method, a thorough analysis of the cost control mechanisms of Shared Power Bank Stations will be carried out, and some theoretical and practical knowledge will be obtained. Fundamentally, the index evaluation method can convert abstract cost management goals into measurable and actionable indicators to provide an orderly plan for reducing costs in all areas of the life cycle of a station ^[3]. The main idea is to build various types of indices in terms of equipment purchase, running costs, energy use and depreciation; thus, managers can determine the factors that drive expenses accurately. After careful index weighting and scoring, the operation mode has changed from reactive spending to proactively controlling costs. The actual usefulness of this method is quite large; it has organised the scattered financial data to form a generalised decision-making system so that the resource allocation can meet the operation's efficiency goals. Through constant monitoring of the above-mentioned evaluation indicators, an enterprise can change the maintenance schedule, adjust the density of stations and obtain reasonable procurement conditions based on actual results rather than estimates ^[6]. Finally, the Index Evaluation Method can be used to manage shared power bank stations in a stable, scientific manner for long-term profitability in a competitive environment. It is a link between the large-scale financial strategy and the small-scale operation; thus, it can be shown that systematic index evaluation is not only an analytical tool but also a key mechanism to achieve long-term cost reduction and operation stability. The above framework may be applied to wider sharing economy models in the future, further demonstrating the importance of standardised cost assessment in the context of technology-led service delivery.

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