Assessing comparative production efficiencies for product line management

Abstract

Product line management involves product expansion or elimination depending on various factors, for example, production processes and demands in the market. This study focuses on measuring product performance for a firm's product line management, using parametric and non-parametric approaches. First, we choose variables related to the production and demand of products and assess the comparative performance of product groups and individual products using data envelopment analysis (DEA). Second, we attempt to detect possible performance differences among the product groups using one-way analysis of variance. Third, we identify the sources of inefficiency using appropriate DEA scores and offer some managerial insights. Last, we try to confirm the determinants of product performance using Tobit regression analysis. The major contribution of this study is the use of a novel approach for product line management by measuring the performance of product groups and individual products using pertinent variables. The approach used in this study is applicable to various manufacturing and service industries. The limitations of this study are the number of product groups selected and examination of performance using cross-sectional data.

Unifying customer, product and brand performance management

“…Product management research involves subjects such as deciding which products should be offered (Carrol and Grimes, 1995), forecasting product demand on stock keeping unit (SKU) level or product category level (Carrol and Grimes, 1995;Ma et al, 2016), analyzing product performance (Joo and Choi, 2015), managing product lifecycle (Wu et al, 2014), managing customer requirements to product lifecycle management (Papinniemi et al, 2014) and providing product customization for each customer (Forza and Salvador, 2008).…”

“…One of the main alternatives to assess and manage product is based on the estimation of the present value of cash flows it is expected to generate in the future (Carrol and Grimes, 1995;Joo and Choi, 2015;Ma et al, 2016). However, as it is also the case in the brand equity literature, usually studies related to product management have not aimed to combine the expected value of products to the expected value of each customer.…”

“…One of the main alternatives to assess and manage product is based on the estimation of the present value of cash flows it is expected to generate in the future (Carrol and Grimes, 1995; Joo and Choi, 2015; Ma et al. , 2016).…”

“…Meanwhile, companies and researchers have not abandoned the importance of accessing the future value of each product a given company sells, of developing products based on the customers' needs and wants, and of monitoring product lifecycle (e.g. Papinniemi et al, 2014;Wu et al, 2014;Joo and Choi, 2015;Ma et al, 2016).…”

Two-Stage Efficiency Structure Analysis Model of Shipbuilding Based on Driving Factors: The Case of Chinese Shipyard

“…In particular, the emergence of the network DEA model provided the possibility of evaluating the efficiency relationship among subsystems within a complex system. Tsaur et al (2017) studied the performance evaluation of Taiwan TFT-LED industry using DEA; Wang et al (2010) studied the application of DEA and grey relational analysis to measure production efficiency and marketing efficiency; Joo & Choi (2015) researched the management performance of different products between different products and production lines based on DEA method; Lee (2012) applied DEA and Malmquist index method to calculate the production efficiency of some shipyards of Korean small and medium-sized shipyards; Chudasama (2010) applied the DEA method to calculate the production efficiency of some of India's shipyards; Donghyun Oh (2014) studied the difference of total factor productivity between individuals of Korean manufacturing enterprises. Almost all of these studies took the production organization as the evaluation object and took the technical change, technical efficiency change, scale efficiency change and total factor productivity change as the sources of efficiency improvement.…”