A Decision Analysis Framework for the Identification and Performance Preservation of Strategic Products in the Supply Chain

Abstract

Background: This study develops a decision-making framework for the identification and performance preservation of strategic products using a non-parametric analysis of items within the product portfolio. Methods: Data Envelopment Analysis (DEA) and the sensitivity analysis of Inverted Data Envelopment Analysis (IDEA) are adapted to explore a new application area in growth product management. A field study from the retail sector of a developing economy is conducted to evaluate the method’s practicality. Results: This study suggests that the power of suppliers, product shelf life, and the ratio of sales to inventory are important supply chain considerations in identifying strategic products accommodated in Slow-Moving Consumer Goods (SMCG) supply chains. Conclusions: The field study shows that sensitivity analysis, in the new application area, provides insights for the identification and performance preservation of strategic items in a product portfolio. Data-driven solutions tailored to the operational needs of the case company and its different product categories conclude this article..

1. Introduction

Rapid changes and market fluctuations characterize the consumer goods industry [1]. Retailers should regularly refine their product portfolio, follow new trends, and leverage new technologies to align with these dynamics and stay competitive [2]. Product portfolio management is challenging in the retail industry, considering the assortment’s width and depth to provide better supply chain performance in terms of product diversification [3]. The question is what course of managerial actions should be taken when dealing with a large assortment of low-turnover products that take up space, use resources, and limit the overall profitability [4].

Introducing new products, removing the poor-performing products, and managing the existing products are required to boost the product portfolio regularly [5]. The existing product portfolio management practices are mostly focused on new product development and the deletion of underperforming products for strategic portfolio adjustment to keep up with the competition [6]. Product revitalization is another example of dealing with poor-performing products in a reactive way [7]. Proactive performance management is often overlooked in traditional product portfolio management. In particular, preserving the performance of strategic products is important for staying competitive [8], maintaining loyal customers [9], and strategic partnerships [10].

Traditional product portfolio management is dominated by myopic financial perspectives, where overlooking the strategic and supply chain considerations may result in a decline in portfolio performance in the long term. The main contribution of this study is to develop a systematic approach to support the identification and performance preservation of strategic products, which has not been studied before. A non-parametric comparison of the products in the portfolio is considered for the identification of strategic items, while the objective is to maintain their performance through proactive improvements in outputs given a certain amount of inputs (i.e., resources). This approach stands in contrast to product deletion and revitalization decisions, where the decision-maker is merely interested in reallocating resources by reacting to poor product performance [11].

Data Envelopment Analysis (DEA) [12], considering variable returns to scale, and sensitivity analysis with Inverse Data Envelopment Analysis (IDEA) are adapted to evaluate product portfolios and explore improvement possibilities. DEA has been widely used for efficiency analysis [13,14]. Sensitivity analysis of DEAs for the identification and performance preservation of strategic items in a portfolio is novel and, to the best of the authors’ knowledge, has not been studied. Consumer goods retailers deal with a bulky assortment of products. Data from the largest retail store in a developing economy are considered to assess the method’s applicability and provide insights for application in other contexts. A field study of Slow-Moving Consumer Goods (SMCG) from the retail sector will explore the role of performance preservation in product portfolio management and provide a generalizable analysis.

The remainder of this article is organized into four sections: Section 2 conducts a literature review to explore the decision factors in product performance evaluation and improvement. Section 3 introduces the research method and preliminaries. Section 4 presents a field study, showcases the practicability of the proposed decision framework, and provides practical implications for the findings. Finally, Section 5 draws general conclusions and suggests directions for future research.

2. Literature Review

2.1. Theoretical Background

Product life cycle and organizational reputation have long been considered to be the major drivers for reactive product portfolio management decisions [15]. The evolution of product portfolio management suggests that changes in technology, competition, and customer needs result in old products becoming obsolete [16]. Product deletion decisions are a prime example of managerial reactions to poor sales performance, a small market, and/or poor product profitability. Eliminating unnecessary goods helps improve capital circulation and inventory efficiency, and this can serve as a reactive tool for addressing portfolios’ underperformance [17]. Product deletion can also be applied as an operational strategic decision to improve supply chain competitiveness [7]. Marketing and finance managers primarily decide at the organizational level when to discontinue obsolete products [18].

The literature on product deletion has received refreshed recognition in the past few years. The most recent examples include the game-theoretic analysis for analyzing supply chain interactions in product deletion decisions [19], a portfolio optimization framework based on product deletion for decarbonization [20], and the empirical study conducted by [21] to analyze the role of customer reviews in product deletion decisions.

There are studies on product assessment that investigate the performance—for example, in terms of conformity [22], creativity [23], and sustainability [24]—of products in various contexts. The next section reviews the decision considerations that may impact the identification and performance preservation of strategic products, a proactive product portfolio management decision that is underdeveloped in the literature.

2.2. Decision Considerations

Product portfolio management decisions should account for supply chain considerations, competitive forces, and financial and market-related factors. This section explores the literature to identify the most relevant decision factors for portfolio evaluation and product performance improvement in the consumer goods industry.

2.2.1. Financial and Organizational Considerations

Financial, organizational, and market factors are the major drivers of product portfolio management decisions. Ref. [25] suggested that the financial aspects should be considered in a reactive product deletion decision; the authors found that revenue, cost analysis, and the company’s ability to compete for the market share are the core factors. In practice, products are evaluated based on a simplistic categorization considering profit margin and sales volume [7]. A company’s product portfolio should serve its core competence and establish a strategic fit [26]. The following factors represent the financial and organizational considerations in evaluating product performance:

(1) Profit margin refers to the difference between the selling price of a product and the operational costs—that is, the profit made on each unit sold. Profit margins have a direct impact on financial performance, with higher profit margins showing better performance [16].

(2) The resource intensity of the product, especially capital and human resources for production and distribution, limits operational efficiency. Optimal resource allocation and management improve productivity, reduce costs, and enhance service quality and product availability [27].

(3) Organizational strategic fit accounts for the compatibility between business strategy, operational capabilities, and market-related opportunities and threats. Products and services are considered unfit if they do not serve the strategic fit and reduce competitiveness in the market [28].

(4) Consumer feedback is perceived as the main driving force for the business’s success, determining the prospect of attracting new customers or losing regular customers. Paying attention to customer needs by modifying products and services often results in increased demand and improved brand reputation [21].

(5) Demand variance: A high variance in customer expectations signals the opportunity for extending the product portfolio; not acting promptly may cause a loss of market share and weaken the brand’s reputation [28].

2.2.2. Operational Considerations

Given the nature of slow-moving products, they may lose value over time. In this situation, the supply chain and logistics considerations become prominent. A large product variety adds to logistical complexities and potentially increases costs. This section explores time, quality, and flexibility as major operational considerations. The timely delivery of products is crucial in business-to-business (B2B) supply chains. Operational flexibility supports the supply chain performance in reacting efficiently to market changes. The related factors are as follows:

(6) The need for reallocating resources: Business opportunities for exploring a new market or bridging a gap in an existing market require production capacity and logistics resources. In this situation, resources should be reallocated for more efficient use [28].

(7) Logistics costs account for the largest operational costs in the consumer goods industry [29]. Special requirements like refrigeration and packaging increase the logistics cost and impact the profit margin and operational efficiency.

(8) The shelf life of the product refers to the change in its value over time. Prime examples are changes in quality if the product remains in the facilities for too long, as well as changes in fashion, since staying long in the store makes it more likely for the product to be outdated [11].

(9) Low logistical flexibility makes it harder to respond to demand changes and unplanned operational needs. This can increase costs and delays in the long run and negatively impact the overall performance of the company [28].

(10) Sourcing effectiveness refers to the performance of the suppliers and suppliers-of-suppliers in terms of responsiveness and quality [30].

(11) The average sales–inventory ratio determines how efficient the product is when considering warehouse spaces and the capital it uses. This measure can be used to tactically enhance operational effectiveness—for example, improving a product that occupies warehouse space for too long and drains the financial resources when they can be used more efficiently [31].

2.2.3. Strategic Considerations

The actions of competitors determine competition within the market. Competitive forces should be strategically considered for medium-term adjustments following market dynamics [32]. New entrants can be expected when a gap is perceived in the market, leading to a long-term decline in market share. In addition, a company may lose its leverage over its suppliers and business partners. The following factors are identified as strategic considerations in product revitalization decisions:

(12) Discount percentage refers to the marketing strategy to attract new customers and increase sales volume. It can also be used as a strategic measure for rivalry in certain market situations. This solution can, however, put a restraint on the profitability of the company and weaken the company’s leverage in the market [33].

(13) Market rivalry impacts income and market share for the product. In a saturated market, the product can be maintained in the portfolio only if it can be differentiated from the competition [28]. Advertising, promotions, and quality enhancements are some solutions influencing the competition.

(14) The repayment period is the timeframe within which the company must fulfill its payment obligations to the supplier. As a contract term, the repayment period specifies the nature of the business partnership, industry norms, and the negotiation leverage of the company [11]. Longer periods allow the opportunity to collect customer payments before settling the account with the supplier.

(15) The threat of substitute products is when alternatives offer the same or even better features or a more attractive price. This can potentially lead to a decline in sales and market share if the underlying conditions are not proactively analyzed [28].

(16) Environmental risks refer to the negative impacts of producing and/or consuming a certain product [20]. With the growing consumer awareness about environmental issues, aspects like waste generation, recycling, and carbon footprint have become inseparable from managerial decisions.

3. Research Method

A company may lose its market share if it merely focuses on the performance of individual products and ignores the strategic outlook [34]. The peer evaluation of competing products in the company’s portfolio helps provide a realistic ranking of the items relative to each other and considering several performance measures. This is in contrast to the traditional methods, where products are assessed individually—for example, based on the sales volume and profit margin [28].

DEA can be used for the relative performance evaluation of different units within a sector by comparing their performance [35]. DEA evaluates the products’ performance compared to peers, considering diverse inputs and outputs. As a supplementary analysis tool to DEA, sensitivity analysis helps explore potential changes through input and output adjustments [36]—that is, exploring possible improvements while preventing unnecessary input increases or output reductions [37]. Potential changes in a product’s performance impact the overall efficiency of the portfolio; this strategic outlook should be taken into consideration in product performance preservation. The outcomes of such an analysis can be a basis for product portfolio assessment and performance preservation possibilities.

The research method consists of two computational phases.

• Identifying the strategic products;
• Determining the improvements desired for performance preservation.

On this basis, marketing and product management professionals can provide tailored solutions that materialize the desired improvements while considering the company’s resources (inputs).

3.1. Identifying the Strategic Products

Given the objective of maximizing the output-to-input ratio, DEA assigns a score of 1 to the efficient units. A drop in the performance of these items can be consequential for the portfolio and overall performance. Therefore, these items are deemed strategic and candidates for performance preservation analysis. This evaluation is relative; for example, if product A generates more output than B while utilizing the same input, then A is more efficient. The products that are not on the efficient frontier require revitalization and are not the subject of analysis in the present study.

Given n products, m inputs, and s outputs indicating the products’ performance, the following model is used to identify the strategic products, where x and y indicate inputs and outputs, respectively, while u and v are the weights assigned to the outputs and inputs, respectively:

$$Max\theta =v_1{x}_{1k}+v_2{x}_{2k}+\dots +v_m{x}_{mk}$$

(1)

$$u_1{y}_{1k}+u_2{y}_{2k}+\dots +u_s{y}_{sk}=1$$

(2)

$$v_1{x}_{1k}+v_2{x}_{2k}+\dots +v_m{x}_{mk}-u_1{y}_{1k}+u_2{y}_{2k}+\dots +u_s{y}_{sk}\le 0$$

(3)

$$u_1,u_2,\dots ,u_s\ge 0$$

(4)

$$v_1,v_2,\dots ,v_m\ge 0$$

(5)

To achieve maximum efficiency in Equation (1), the weighted sum of outputs should be equal to 1 (Equation (2)) while ensuring that there cannot be more outputs than the used resources allow for (Equation (3)). According to Equations (4) and (5), the weights are non-negative.

3.2. Determining the Desired Improvements for Performance Preservation

We conducted sensitivity (investment) analysis separately for each strategic product to determine the desired output changes. Analyzing the improvement rates of products under consideration provides decision support for product performance preservation and strategic improvement. The objective was to determine the desired improvements (i.e., increase in outputs or decrease in inputs).

For a formal definition of the problem in Phase II, let us assume that $x_0$ and $y_0$ represent the actual input and output values for the product under consideration. Given $n$ products in the portfolio, the objective is to identify the desired output ${\beta }_0$ for a certain level of input ${\alpha }_0$, assuming that efficiency remains constant at ${\phi }_0$. The desired output is expressed by ${\beta }_0=y_0+\mathsf{\Delta }{y}_0$. It is also hypothesized that the input value may change from $x_0$ to ${\alpha }_0=x_0+\Delta x_0$, where $\Delta x_0>0$, to achieve the desired improvements. The mathematical model for sensitivity analysis is presented below:

$$Max({\beta }_1,{\beta }_2\dots {\beta }_s)$$

(6)

$$\sum _{j=1}^n{x}_j{\lambda }_j\le {\alpha }_0$$

(7)

$$\sum _{j=1}^n{y}_j{\lambda }_j\ge \beta {\phi }_0$$

(8)

$${\beta }_s\ge y_0$$

(9)

$${\lambda }_j\ge 0,j=1,\dots ,n$$

(10)

The objective in Equation (6) is to maximize the output vector. Equation (7) specifies that the total quantity of every input must not be larger than the maximum value. According to Equation (8), the output quantities cannot be less than the fixed efficiency associated with the actual output value. Equation (9) estimates the desired output quantities, which must surpass the actual output value. Equation (10) states that the weight values should be non-negative.

4. Results, Analysis, and Discussions

4.1. Data Collection and Preparation

Kourosh chain stores in Iran were considered for the field study. Since its establishment, the Kourosh Group has experienced rapid and sustained growth, with over 6000 stores across the country. The company is active in e-commerce and retail, with 10 companies; hence, product portfolio management plays a key role in managing their operations. The portfolio spans over 25 product categories, including edible oils, protein products, canned goods, dairy items, dried fruits and beans, rice, nuts and snacks, tea and coffee, desserts and jellies, sauces, saffron, etc.

The data collection procedure began with screening the products. It was assumed that products with a negative profit margin are alternatives for deletion and, hence, should not be considered for improvements, because of the immediate risk that they impose. Products with poor sales due to quality issues or insufficient demand were exempted for the same reason. The items with a shelf life under 60 days were excluded because they fall out of the slow-moving category: the beverages, cosmetics and hygiene, dried fruits, and home and kitchen categories were considered for performance preservation analysis. Finally, items under each category were analyzed separately to ensure a meaningful peer evaluation and frontier analysis.

Data collection continued with screening decision factors. Given the factors presented in Section 2, interviews with experts and qualitative assessments found that 11 factors could be excluded considering marginal relevance and/or data. Profit margin, discount percentage, the repayment period, the average sales-to-inventory ratio, and product shelf life were considered for further analysis.

The last step consisted of collecting and analyzing raw data to establish input and output variables for DEA analysis [38]. This study treats positive factors as outputs where higher values are preferred. Negative factors are regarded as inputs where lower values indicate better performance. The factors can also be classified into dependent and independent variables, with the independent variables being the subject of direct decision-making. The classification of factors is summarized in

Table 1. Initial classification of the factors.

Classification 1
Dependent Variables (Indirectly Decided)	Independent Variables (Directly Decided)
Product Shelf Life (PSL)	Profit Margin Percentage (PMP)
Ratio of Sales to Inventory (RSI)	Discount Percentage (DP)
	Repayment Period (RP)
Classification 2
Input Variables ($-$)	Output Variables ($+$)
Product Shelf Life (PSL)	Ratio of Sales to Inventory (RSI)
Discount Percentage (DP)	Profit Margin Percentage (PMP)
	Repayment Period (RP)

.

Classification 2 is the basis for efficiency analysis, where the output variables are adjusted to achieve the desired improvement outcomes. Inverted values of discount percentage and the average sales-to-inventory ratio are considered to ensure compatibility with the respective variable category. The rest of the adjustments and calculations are listed in

Table 2. Scaling and calculation of inputs and outputs.

Factor	Source
Profit Margin Percentage (PMP)	${\displaystyle \frac{\mathrm{Revenue} \mathrm{margin}}{\left(\mathrm{Number} \mathrm{of} \mathrm{sales} \times \mathrm{Consumer} \mathrm{price}\right)}}$
Discount Percentage (DP)	$\left({\displaystyle \frac{\mathrm{Discount} \mathrm{amount}}{\mathrm{Number} \mathrm{of} \mathrm{sales} \times \mathrm{Consumer} \mathrm{price}}}\right) \times 100$
Repayment Period (RP)	From the contract with the supplier
Product Shelf Life (PSL)	From the product information
Ratio of Sales to Inventory (RSI)	${\displaystyle \frac{\mathrm{Number} \mathrm{of} \mathrm{sales} \mathrm{in} 6 \mathrm{months}}{\mathrm{Average} \mathrm{daily} \mathrm{inventory}}}$

.

4.2. Identifying Strategic Products

Given five factors and five strategic products from the beverages category, the linear programming problem in Equation (11) was developed, with product 393-4 being the item under consideration.

$$\begin{gathered} Max\theta =0.30u_1+380.17u_2+51u_3\hspace{1em}\hspace{1em}\hspace{1em} \\ \mathrm{Subject} \mathrm{to}: \\ 945.88v_1+80.47v_2=1\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}0.30u_1+380.17u_2+51u_3-945.88v_1-80.47v_2\le 0 \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} 0.23u_1+379.13u_2+49u_3-\mathrm{7.18.55}{v}_1-66.72v_2\le 0 \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}0.25u_1+376.62u_2+58u_3-728.48v_1-62.64v_2\le 0 \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}0.18u_1+372.12u_2+46u_3-676.09v_1-60.97v_2\le 0 \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} 0.37u_1+1056.04u_2+47u_3-559.41v_1-100.16v_2\le 0 \\ {u}_1,u_2,u_3,v_1,v_2\ge 0 \end{gathered}$$

(11)

DEA_Solver was used to solve the optimization problems. Products with an efficiency of 1 were considered for product performance preservation analysis.

Table 3. Efficiency analysis of the beverage product category.

	Product ID	393-4	393-6	393-7	393-8	724-12
Output	Profit Margin Percentage (PMP)	0.30	0.23	0.26	0.19	0.37
	Discount percentage (DP)	0.26	0.26	0.27	0.27	0.09
	Repayment period (days)	51	49	58	46	47
Input	Average ratio of sales to inventory	0.11	0.14	0.14	0.15	0.18
	Durability (days)	80	67	63	61	100
Results	Efficiency	0.7501	0.8532	1	0.8973	1
	Efficiency percentage	75	85	100	90	100

presents the results of solving Model (6) for the beverage category, based on which 393-7 and 724-12 were considered for further analysis in Phase II. A total of 407 linear programming models should be developed and solved to identify the strategic products in the rest of the categories.

4.3. Analyzing the Required Improvements

This phase determined the target values for product performance preservation—that is, enhancing discount percentages, profit margins, and payback periods to a desired level while maintaining consistent efficiency. Fixing the inputs at the current level may not yield positive outcomes; therefore, a raise of up to 5 percent was made possible. This increase was identified through the sensitivity analysis, which shows the minimum necessary input increase for achieving the desired output.

Table 4. Desired inputs for the strategic items from the beverage category.

ID	Efficiency	Efficiency Percentage	Minimum Entry	Desired Entry
393-7	1	100	765	(Minimum average ratio of sales to inventory)
724-12	1	100	587	66

shows the efficiency analysis outcomes for the products under consideration. Taking 724-12, the IDEA model is shown in Model (12).

$$\begin{gathered} Max = \left({\beta }_{11,724-12}\times {\beta }_{21,724-12}\times {\beta }_{31,724-12}\right) \\ \mathrm{Subject} \mathrm{to}:\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} \\ 0.30t_1+0.23t_2+0.25t_3+0.18t_4+0.90t_5\ge 1\times {\beta }_{11,724-12} \\ 382.17t_1+379.13t_2+376.62t_3+375.12t_4+1056.04t_5\ge 1\times {\beta }_{31,724-12} \\ 51t_1+49t_2+58t_3+46t_4+47t_5\ge 1\times {\beta }_{21,724-12} \\ 945.88t_1+718.55t_2+728.48t_3+676.09t_4+559.41t_5\le 590 \\ 80.47t_1+66.72t_2+62.64t_3+60.97t_4+100.16t_5\le 120 \\ {\beta }_{11,724-12} \ge 0.90 \\ {\beta }_{21,724-12} \ge 47 \\ {\beta }_{31,724-12} \ge 1056.04 \end{gathered}$$

(12)

4.4. Discussions and Practical Implications

Table 5. Performance preservation analysis for strategic products in the beverage category.

Product ID		393-7	724-12
Output	Profit margin Percentage	0.26	0.37
	Discount Percentage	0.27	0.09
	Repayment Period (Days)	58	47
Input	The Average Ratio of Sales to Inventory	728	559
	Product Shelf Life (Days)	62.64	100.17
Desired Improvements	Efficiency	1	1
	Efficiency Percentage	100	100
	Profit Margin Percentage (PMP)	0.27	0.39
	Discount Percentage (DP)	0.23	0.09
	Repayment Period (RP)	58	50
	Minimum Product Shelf Life (PSL)	66	105
	Minimum Ratio of Sales to Inventory (RSI)	765	587

summarizes the results for the beverage category. This is followed by suggestions to sustain or enhance the performance of strategic products in the beverage category. Considering the projected 5% rise in inputs, the following improvements are desired for the performance preservation of the strategic items under the beverage category, which are tailored to meet the operational needs of the case company.

The current profit margin for item 393-7 stands at 26%. With rising input costs, it is desired to elevate this margin to 27%. This suggestion indicates that product 393-7 has greater profit potential. The current discount rate for this product is 27%, while it is desired to be reduced to 23%. This reduction in discount would improve the product’s profitability, with minimal effect on its demand. The refund period for product 393-7 to the supplier can be maintained at 58 days.

The profit margin for product 724-12 has increased by 2%, indicating enhanced value despite a 5% increase in input costs. The current discount rate for this product is below 10% and can be kept at this level, given its stable demand. Finally, the refund period for this item should be extended by 3 days, highlighting the need for greater flexibility in financial management and improved contract terms with the supplier.

The product performance preservation outcomes for other items can be obtained by developing and solving the optimization problem in Phase II. The same analysis was repeated, and the desired improvements for strategic products in the cosmetics and hygiene, home and kitchen, and dried fruit categories are provided in the Appendix A.

Analyzing the cosmetics and hygiene category, with 127 items, shows that 24 products have an efficiency rating near or equal to 1. Taking product 716-33 as an example of a strategic product in the cosmetics and hygiene category, its profit margin stands at 65%, but it should be raised to 80% to maintain its status in the long term. This indicates the product’s potential for higher profit margins without requiring a compromised quality to lower its cost. The discount percentage for this product should be reduced by 7% to enhance profitability. The repayment period to the suppliers should be lengthened by 13 days to manage the product’s cash flow.

In the home and kitchen products category, 7 out of 63 products are perceived to be strategic. Taking item 1376-78 as an example, the product requires a rise in the profit margin to sustain its desirable performance in the portfolio. With efficiency remaining constant and a 5% increase in the allocated budget and resources, the profit margin for this product should grow from 51% to 54%. The existing discount percentage for this product under the current conditions is acceptable, meaning that a good balance between revenue and market competition has been established. However, the analysis suggests that the refund period for this product should be extended by 3 days to enhance financial flexibility.

Of the 215 items in the dried fruit product category, 16 demonstrated efficiency despite a 5% increase in inputs. Taking 169-398, a 5% increase in inputs is expected to boost the product’s Profit Margin Percentage from 59% to 61%. Sustaining the current discount percentage is recommended to enhance the appeal of shopping to customers while preserving profitability. Finally, the payment terms with the supplier should be extended from 36 to 38 days to improve inventory management and alleviate financial pressures.

These findings have several practical implications. The case company has recently removed several low-turnover products from the beverage category. Following the product performance preservation outcomes, and considering the shift in consumer preferences, the company is recommended to focus on natural beverage offerings, including mineral water, natural soft drinks, and energy drinks. Given the strong market demand for these items, they are strategic to the company’s portfolio, and new offerings of this type should replace the poor-performing products.

The current consumer market landscape for cosmetic and hygiene products shows that customers from the middle- and lower-class income brackets represent the biggest portion of the market. In this situation, offering products tailored to the specific needs of this demographic is essential. The case company should shift from high-end items, which cater to a limited market segment. This shift requires a balance between quality and affordability. Moreover, the global situation and social sustainability are in favor of supporting local producers. This can preserve the cost-effectiveness of the strategic items in this product category.

Concerning home and kitchen products, managing the store and warehouse space has been identified as the major challenge in the case company. Our analysis suggests that prioritizing products that are compact yet high-performing is required to preserve supply chain efficiency. These requirements are well aligned with the needs of urban life, requiring compact and functional solutions.

Finally, the strategic products from the dried fruits and spices category, which show a smaller assortment depth compared to other product categories, should be of premium quality. High-quality and appealing/functional packaging is of particular interest in this category to offer market differentiation and attract new customers. Improving the accessibility of these products in retail stores is another way of increasing demand and preserving the sales performance.

5. Conclusions

5.1. Concluding Remarks

This study developed a systematic decision support framework to analyze the requirements for sustaining the performance of strategic products in a portfolio. For this purpose, DEA and sensitivity analysis of IDEA were adapted for a new application area. The findings showed that the non-parametric peer evaluation of products in a portfolio provides a basis for product categorization. This approach, although novel, is in line with the findings of [39], suggesting that new product development should be carried out in relation to existing and competing products. The competitive and supply chain-related criteria were considered in addition to the financial performance indicators, incorporating a strategic perspective into proactive product portfolio management—a consideration that was previously recognized for successful product deletion and revitalization decisions [7]. The outcomes of such multifaceted analysis can inform decision-making in growth product management.

The decision analysis outcomes support the following solutions tailored to the operational needs of the case company: The beverage category requires a shift of focus on the health and quality parameters of the strategic products, considering the increasing consumer awareness. Establishing a reasonable balance between the price and quality of strategic products is recommended for the cosmetics and hygiene category. This contrasts with the category of dried fruits and spices, where offering premium quality and high-end packaging has become essential for preserving their competitiveness. Finally, the performance of strategic home and kitchen items can be preserved with a focus on the compactness and functionality of the products.

5.2. Limitations and Directions for Future Research

The decision analysis framework developed in this study is limited in that the second stage assumes that efficiency will remain constant, which may not be true in dynamic market situations. Products in a portfolio can be interrelated, and a change in one product’s performance may impact the performance of others. Future research may address this limitation using network DEA models that account for interrelationships within the product portfolio.

The second limitation comes from scalability. Including a very large number of items in the analysis may lead to a loss of discriminatory power. In this situation, it becomes harder to identify meaningful options for improving the item under consideration. Moreover, the so-called “curse of dimensionality” may result in a range of performance levels in the solver. Therefore, analyzing products in different categories may be the best compromise. In addition, the present study can be extended in the following directions:

First, this study is limited because only one company’s products were considered for performance preservation analysis. Considering the products from rivals would provide additional insights. To address this, network DEA and IDEA can be developed and tested for growth product management. Second, the decision framework developed in this study can be extended to simultaneously allow for different portfolio management decisions like product deletion, substitution, revitalization, and product performance preservation, to allow for different tradeoffs. Third, additional and/or different factors may be required in other contexts, a direction that deserves further investigation. Finally, the case company could not provide environmental data, such as the carbon footprint of the products. Future research may complement our decision analysis approach by including life-cycle assessment data as some of the output variables in the model.