With tourist arrivals almost doubling over the past five years, growing from 8.8 million in 2013 to 15.8 million in 2018, Indonesia’s star-rated hotel supply has experienced a dramatic expansion over the same period. Driven by the growth in inbound visitor arrivals and strengthening macro-economic fundaments, an expanding middle class, and rising consumer spending, the star-rated hotel sector is set to see a healthy 12% CAGR in room supply over the next five years to 2023.
Despite this dramatic growth, there is concern that service standards and productivity levels lag behind the growth in hotel infrastructure, leading to less efficient operations and sub-optimal industry performance. As a result, a growing number of Indonesian hotel investors have recently started to use a sophisticated linear programming technique called DEA, or data envelopment analysis, for benchmarking and performance improvement target purposes.
The use of DEA uncovers significant peer-based improvements and cost saving opportunities not identified by traditional financial and operating ratio analysis used in the lodging industry. Why? Because it calculates efficiency based on observed best practice – not on “average” or “comparable” data, and simultaneously accounts for the interactions and substitutions between multiple inputs (e.g., FTEs, housekeeping payroll & related and laundry, linen and supplies) and multiple outputs (e.g., rooms cleaned, inspection reports, absenteeism and guest satisfaction). Single-factor productivity measures, which are the staple of current operating ratio analysis used in the lodging industry, are unable to do this!
Financial ratios, probably the most widely used measure of hotel performance, provide information on the overall financial performance of a hotel, but provide little in the way of tangible information about the amount by which performance might be improved or the area or areas where effort should be focused in order to improve performance. To make improvements, realistic and achievable improvement targets have to be identified with sufficient information for managers to be able to work towards them. This is where DEA comes in. For further details on DEA please feel free to download our article on DEA.
The following case study is drawn from a major consultancy undertaken in mid 2019 for an Indonesian client whose name and specific details remain confidential under our agreement. However, our client was kind enough to allow us to release a small sample of our results from our larger study on the basis that no identifying material be released. This small sample demonstrates some of the effective working insight that we can provide to improve the profitability of any hotel portfolio. It gives practical applications of DEA and highlights some of the benefits of its use in the lodging sector. We are grateful for our client’s permission to use its information in this post.
We are therefore pleased to provide a brief report on the efficiency rankings and targeted peer-based improvements and expense savings for a portfolio of 26 hotels located in mainly city locations across Indonesia. The portfolio is part of a larger portfolio which includes hotels with an ADR higher than US$100.
The portfolio is geographically diverse with two hotels in each of Jakarta, Bali, Yogyakarta and Bandung and the remainder in cities such as Batam, Bintan, Surakarta (Solo), Makassar and Semarang. Other locations are listed in the Department Efficiency Rankings table an the end of the post. The hotels carry both domestic and foreign brands. The hotels range in size from 98 rooms to 291 rooms, with an average of 174 rooms. Further details on the characteristics of the portfolio are given in the table below.
Descriptive Statistics for the 26 Hotel Portfolio
The client wanted to employ DEA to enhance the company’s existing performance management system and help sustain a continuous process of improving organizational performance as it ramps up a five – year development program to open about 20 new hotels. It was proposed that DEA be employed to critique budgets and examine end-of-year performance.
The client wanted to use the outputs of our work to increase the overall profitability of the portfolio by optimally allocating asset management resources and establish peer-based benchmarks for the entire portfolio. In addition, the client sought to:
- Identify star performers to locate “best practice”
- Identify under-achievers to locate “poor practice”
- Identify peer hotel departments for inefficient hotel departments to emulate
- Set realistic, peer-based improvement targets, and
- Uncover the largest potential efficiency gains
With more traditional approaches to performance measurement, such as ratio and regression approaches, it is possible to provide a graphical representation of the results. It is also possible with DEA to produce a graphical representation of the results, but only in situations with one input and two outputs, or two inputs and one output. Beyond this, the graphical representation would be in n-dimensional space.
The graphical representation below helps explain the principles of DEA and is used to illustrate:
- The construction of the efficiency frontier
- How “best practice” hotels are identified
- The performance gap between “best practice” hotels and under-performers
- How benchmarks can be set for under-performers, and
- The setting of performance targets for under-performers
The efficiency frontier is the frontier (envelope) representing “best performance” and is made up of the hotels in the portfolio which are most efficient in transforming their inputs into outputs. The hotels that determine the frontier are those classified as being 100% efficient. With one input and two outputs, the efficiency frontier is constructed in three steps as follows:
Step 1: With one input (Rooms FTE per Room in our case) and two outputs (Room Revenue and Rooms Occupied), we plot the values of outputs obtained with the defined input as illustrated below.
Location of the 26 Hotels in Relation to the Efficiency Frontier with One Input (Rooms FTE per Room) & Two Outputs (Room Revenue & Occupied Rooms)
Step 2: We then identify the hotels giving maximum outputs with the given amount of inputs. These hotels form the efficiency frontier. In the diagram above we find that the National Hotel 4 and Jakarta Hotel 1 form the efficiency frontier and are deemed 100% efficient. Hotels located within the frontier or envelope (i.e. in the region of under-performance) are found to be inefficient.
Step 3: We identify targets for the inefficient hotels by drawing a straight line from the origin, through the inefficient hotel to the frontier. In the example above the line is drawn through Yogyakarta Hotel 2 to the frontier, which is the target for the hotel.
Virtual Yogyakarta Hotel 2 on the efficiency frontier, is representative of an efficient hotel, albeit an imaginary one, the characteristics of which demonstrate the improvement in output needed to move the Yogyakarta Hotel 2 from its current position, onto the efficient frontier at the point with the star.
The imaginary hotel at this point is referred to as a composite or virtual hotel. An efficiency measure can be calculated from this graph by calculating the ratio of the distances between the origin and current location of the Yogyakarta Hotel 2 and the origin and target location on the efficiency frontier. The potential for improvement can be quantified and under-performers assigned appropriate benchmark partners.
The illustration above also highlights the existence of “peers” for the inefficient hotels. The National Hotel 4 and the Jakarta Hotel 1 are clearly potential peer hotels for the Yogyakarta Hotel 2. Further peer hotels were identified as a result of the simultaneous evaluations of all inputs and outputs. Recall that DEA simultaneously considers the multiple resources used to generate multiple outputs. Although the composite hotel (which sets the targets for the improvement of an inefficient hotel) is a combination of its peer hotels, the peers do not necessarily contribute equally to the construction of the composite hotel.
DEA results in each hotel or hotel department being allocated an efficiency score between zero (0%) and 1 (100%). A hotel department with a score of 100% is relatively efficient, a hotel department with a score of less than 100% is relatively inefficient. Scores are relative not absolute, as they are relative to the other hotels in the portfolio.
Nine of the 26 Room departments had an overall efficiency of 100% based on the simultaneous evaluation of two outputs (rooms occupied and room revenue) and three inputs (rooms payroll & related expenses, rooms other expenses and rooms per hotel. The remainder had overall efficiencies ranging from a high of 97% to a low of 76%.
Department Efficiency Rankings for the 26 Hotel Portfolio
Note: Maximum Efficiency Score = 100. Please refer to the footnote for definitions of the efficiency terms.
Any hotel department with a score of less than 100% is relatively inefficient, e.g. a hotel department with a score of 80% is only 80% as efficient as the best performing hotel department in the portfolio. An inefficient department should be able to produce its current level of outputs with fewer inputs or generate a higher level of outputs given the same inputs when compared with the actual achieved performance of other departments in the analysis.
As illustrated in the rankings above, only two hotels had five efficient departments, four hotels had four efficient departments and seven hotels were inefficient in all departments.
Our detailed analysis identified targeted expense savings of $7.4 million, or about $287,000 per inefficient hotel on average with a range from $18,000 to $710,000. The largest targeted expense savings were found in the Energy Department ($2.8 million), followed by Property Operations & Maintenance ($730,000) and Rooms ($570,000). The distribution of targeted expense saving is illustrated below.
$7.4 Million Targeted Expense Savings – 26 Hotel Portfolio
The highest priority hotels for immediate remedial action for all expenses include Bandung Hotel 2 ($710,000), Jakarta Hotel 2 ($620,000), National Hotel 4 ($520,000), National Hotel 7 ($510,000) and National Hotel 1 ($450,000). The five hotels account for almost $2.8 million or about 38% of the targeted expense savings for the portfolio. The top 10 account for $4.7 million or 63% of the total. The distribution of targeted expense savings for the 15 highest priority hotels is illustrated below.
$6.1 Million Targeted Cost Savings for 15 Hotels – 81% of Total
An important feature of DEA is the ability to identify efficient hotels, the reference set, which can be examined in order to identify appropriate targets for inefficient hotels to work towards.
Charged with interpreting and interpreting the results of our DEA analysis, we critically reviewed the operating practices of both the efficient and inefficient hotels, to determine the best course of action in order to bring about improved performance.
Surprisingly, productivity analysis of lodging facilities is still performed using measures that fail to capture the complexity of today’s operations. Traditional partial-factor productivity measures do not account for relationships among input resources. Labor productivity, in particular, is often used as a surrogate for overall operational performance, without regard to other relevant variables.
DEA is a technique that allows for measurement of the relative efficiency of hotels and departments. Its main strength lies in its ability to capture the interplay between multiple inputs and outputs, a process that cannot be satisfactorily probed through traditional ratio analysis which forms the basis of the lodging industry’s benchmarking efforts.
Purely financial measures of performance are no longer sufficient to ensure long-term competitiveness. Our analysis takes the data for existing performance measurement systems and uses a mathematical technique to combine all the performance ratios into a single efficiency score. More importantly, it tells you where your hotels can improve – based on the performance of their peers. This means that the targets for improvement are objective, realistic and achievable.
Please contact us if you would like to discuss how we benchmark and improve the efficiency of your Indonesian hotel portfolio.
Efficiency is defined as the weighted sum of the outputs produced by a process, divided by the weighted sum of the inputs used by the process.
Overall efficiency or sometimes called aggregate efficiency refers to the extent to which a hotel achieves overall productivity attainable in the most efficient manner. A hotel is said to be technically efficient if it maximizes output per unit of input used. Technical efficiency is the efficiency of the production or conversion process and is calculated independently of prices and costs. The impact of scale size is taken into account as hotels are compared only with hotels of similar scale sizes.
A hotel is “scale efficient” when its size of operation is optimal. If its size of operation is either reduced or increased its efficiency will drop. A scale efficient hotel is operating at optimal returns to scale.