Assesing Company Financial Health using an Integrated BSC-DEA Framework with a Focus on Process Digitization

Customer Perspective: a)

Objectives: Enhance customer satisfaction, increase customer loyalty, and grow market share.

Key Indicators: Customer Satisfaction Index, Customer Retention Rate, and Market Share.

Internal Process Perspective: a)

Objectives: Improve internal process efficiency and optimize operational costs.

Key Indicators: Inventory Turnover, Production Cycle Time, and Operational Costs as a percentage of revenue.

Learning and Growth Perspective: a)

Objectives: Foster innovation, develop employees, and ensure long-term sustainability.

Key Indicators: R&D Expenditure, Training Hours per Employee, and Employee Engagement Index.

Social Perspective: a)

Objectives: Enhance social responsibility, community engagement, and employee well-being.

Key Indicators: Corporate Social Responsibility (CSR) initiatives, community investment, diversity and inclusion metrics, employee satisfaction, and turnover rates.

Environmental Perspective: a)

Objectives: Minimize environmental impact, improve resource efficiency, and support sustainability.

Key Indicators: Carbon footprint, energy consumption, waste reduction, water usage, and compliance with environmental regulations.

DEA Framework for evaluation of a company financial health DEA is a non-parametric method used to evaluate the relative efficiency of decision-making units (DMUs) within an organization. When applied in conjunction with the BSC, DEA can assess how efficiently different departments, branches, or business units convert inputs (e.g., costs, resources) into desired outputs (e.g. profits, customer satisfaction) [5]: −

Inputs: In the context of financial health, inputs might include operational costs, capital expenditures, and employee-related expenses. These are resources which the company uses to generate financial outcomes.

Outputs: Outputs are the financial results achieved, such as profitability, revenue growth, and market share. DEA evaluates the efficiency of converting inputs into these financial outputs. By integrating the KPIs from the BSC into the DEA model, the company can compare the efficiency of different units in achieving financial health. DEA provides an efficiency score for each unit, highlighting which units are performing well and which require improvement.

The integrated BSC - DEA approach

The integrated BSC-DEA approach combines the qualitative and quantitative strengths of both models to provide a more comprehensive assessment of financial health.

Step 1: Identify and measure KPIs −

Define KPIs for each BSC perspective relevant to financial health.

Collect data for these KPIs across different DMUs within the company.

Step 2: Build a DEA model that uses these inputs and outputs to each DMU.

Step 3: Calculate efficiency scores −

Run the DEA model to compute efficiency scores. These scores indicate how well each DMU is utilizing its resources to achieve financial health.

A score of 1 indicates that the unit is efficient (on the efficiency frontier), while scores less than 1 indicate inefficiency.

Benchmarking

This step includes benchmarking and analysis of: −

the efficiency scores across DMUs to identify best practices and areas for improvement,

the results to understand which factors are contributing to inefficiencies and how they can be addressed.

Strategic insights and decision-making

The integrated BSC-DEA approach provides valuable insights that can guide strategic decision-making.

Continuous Improvement

One of the strengths of the integrated BSC-DEA model is its flexible nature. By continuously updating the BSC and DEA models with the latest data, companies can monitor their financial health over time and make ongoing adjustments to strategies and operations. −

Implement feedback loops to regularly assess performance, adjust targets, and refine strategies based on the latest insights from the BSC-DEA analysis.

As the business environment changes, the integrated model can be adapted to incorporate new KPIs, inputs, and outputs, ensuring that the company remains agile and responsive to market conditions.

Inputs and outputs categorization

The inputs and outputs will be categorized based on the perspectives of the expanded BSC framework:

Inputs X_ik: −

Financial Inputs: Operating costs, capital expenditures.

Customer Inputs: Marketing expenditures, customer service costs.

Internal Process Inputs: Manufacturing costs, operational expenses.

Learning and Growth Inputs: Employee training costs, R&D expenses.

Social Inputs: CSR expenditures, community investments.

Environmental Inputs: Costs related to energy consumption, waste management.

Outputs Y_rk: −

Financial Outputs: Profitability, Return on Investment (ROI), cash flow.

Customer Outputs: Customer satisfaction, market share, customer retention.

Internal Process Outputs: Process efficiency, cost savings, production output.

Learning and Growth Outputs: Innovation (e.g., new products developed), employee satisfaction.

Social Outputs: Community impact, employee well-being.

Environmental Outputs: Reduced carbon footprint, waste reduction.

Objective function

The DEA model aims to maximize the efficiency score θ_k for each DMU. The efficiency score θ_k is defined as the ratio of the weighted sum of outputs to the weighted sum of inputs: (1) θk=Σr=1RurYrkΣi=1IviXik [{\theta _k} = {{\Sigma _{r = 1}^R{u_r}{Y_{rk}}} \over {\Sigma _{i = 1}^I{v_i}{X_{ik}}}}

R is the total number of outputs across all perspectives.

I is the total number of inputs across all perspectives.

Constraints

To ensure fair evaluation and normalization of efficiency scores, the following constraints are applied:

Efficiency Constraint:

For all DMUs j = 1, 2, ., N, the efficiency score must be less than or equal to 1, indicating that no DMU can exceed 100% efficiency: (2) Σr=1RurYrkΣi=1IviXik≤1∀j=1,2,…,N [{{\Sigma _{r = 1}^R{u_r}{Y_{rk}}} \over {\Sigma _{i = 1}^I{v_i}{X_{ik}}}} \le 1\,\,\,\,\,\,{\forall _j} = 1,2, \ldots ,N

Normalization Constraint:

For the DMU under evaluation (DMU k), the sum of the weighted inputs is normalized to 1: (3) ∑i=1IviXik=1 [\mathop \sum \nolimits_{i = 1}^I {v_i}{X_{ik}} = 1

Non-Negativity Constraint:

The weights assigned to the inputs and outputs must be nonnegative: (4) vi≥0∀i=1,2,…,I [{v_i} \ge 0\,\,\,\,\,\,\,\,\,\,{\forall _i} = 1,2, \ldots ,I (5) ur≥0∀i=1,2,…,R [{u_r} \ge 0\,\,\,\,\,\,\,\,\,\,{\forall _i} = 1,2, \ldots ,R

Linear Programming Formulation

To solve the DEA model using linear programming, we transform the objective function into a minimization problem, which is equivalent to maximizing efficiency. The goal is to minimize the sum of weighted inputs while ensuring that the weighted outputs are maximized relative to all other DMUs. −

Objective Function: (6) Minimizeθk=∑i=1IviXik [{\rm{Minimize}}\,\,{\theta _k} = \mathop \sum \nolimits_{i = 1}^I {v_i}{X_{ik}}

Subject to the following constraints: a)

Output Constraints: (7) ∑r=1RurYrj≤∑i=1IviXij∀j=1,2,….N [\mathop \sum \nolimits_{r = 1}^R {u_r}{Y_{rj}} \le \mathop \sum \nolimits_{i = 1}^I {v_i}{X_{ij}}\,\,\,\,{\forall _j} = 1,2, \ldots .N

Normalization Constraint: (8) ∑i=1IviXik=1 [\mathop \sum \nolimits_{i = 1}^I {v_i}{X_{ik}} = 1

Non-Negativity Constraints: (9) vi≥0∀i [{v_i} \ge 0\,\,\,\,{\forall _i} (10) ur≥0∀r [{u_r} \ge 0\,\,\,\,\,{\forall _r}

Interpretation Of Results −

Efficient DMUs: DMUs with an efficiency score of θ_k = 1 are considered efficient, meaning they use resources optimally to achieve their outputs.

Inefficient DMUs: DMUs with efficiency scores θ_k < 1 indicate place for improvement, as they are not utilizing their resources as effectively as the benchmark DMUs.