OPERATIONS AND SUPPLY CHAIN MANAGEMENT Vol. 13, No. 3, 2020, pp. 269 – 278 ISSN 1979-3561 | EISSN 2759-9363 Digital Muda - The New Form of Waste by Industry 4.0. Jamila Alieva Department of Industrial Economics, Gavle University, Kungsbäcksvägen 47, 801 76 Gävle, Sweden Email: Jamila.Alieva@hig.se (Corresponding Author) Robin von Haartman Department of Industrial Economics, Gavle University, Kungsbäcksvägen 47, 801 76 Gävle, Sweden Email: Robin.vonHaartman@hig.se ABSTRACT Lean management is an approach where value is created through the reduction of waste. Eight forms of waste were identified by the Toyota Company as worth considering while managing an efficient production process: overproduction, waiting, transport, over processing, inventory, movement, defects, and unused creativity. Modern manufacturing plants are being transformed by Industry 4.0, the fourth industrial revolution, which promotes a wide variety of technological solutions to increase innovativeness and competitive advantages. Technological solutions are created on the basis of data that must be analyzed to enable manufacturers to be more strategic in the decision-making process and generate new profit channels through data analytics. A conceptual framework was developed to investigate if the inefficient usage of data has a negative impact on manufacturing performance through the decision-making process. Semi-structured interviews were conducted in two leading manufacturing companies in Sweden that are following lean principles. A new form of waste, digital waste, was defined. This paper suggests considering digital waste as a new type of muda (waste), which is its theoretical contribution. From a practical perspective, the results of the paper encourage practitioners to pay extra attention to data analytics, work on the reduction of digital waste and establish new revenue channels based on data analysis. Keywords: lean management, digitalization, digital waste, muda, data processing 1. INTRODUCTION In 1990, the book The Machine that Changed the World revolutionized the perceptions of many scientists and practitioners about the manufacturing production process and the role of people in it. Soon, lean production became known worldwide, with the meanings of value and waste always at the center of attention. Waste (also known by Japanese word muda) was defined as an action in the production process that did not add value for the customer (Womack et al., 1990). In 2015, the fourth industrial revolution was proclaimed a movement affecting the manufacturing sector digitally. The academic community was concerned about the synergy of lean and Industry 4.0 and their compatibility (Kolberg and Zuhlke, 2015; Mrugalska and Wyrwicka, 2017). Manufacturing companies have started investing in the technologies aligning the strategy with lean values (Sanders et al., 2017; Tortorella and Fettermann, 2017). Besides traditional product sales, companies offer after-sales services based on data analytics from the product or the production process. According to De Backer et al. (2017), the experience with 20 back-end factories in Asia shows that a combination of lean and Industry 4.0 techniques, can help manufacturers sustain improvement in labor costs and quality. Productivity was increased up to 50% for direct labor and up to 20% for maintenance productivity. The overall equipment effectiveness was increased up to 15%, with an up to 50% decrease in customer complaints. Manufacturing companies are offering more jobs for the specialist with a technical background, while engineers and operators are expected to adapt to the new digital environment (Bonekamp and Sure, 2015; Hecklau et al., 2016). Artificial intelligence (AI) is one of many examples where data analysis plays a major role. According to Tonby et al. (2019), Japan has the ambition to make AI development a strategic priority and recently announced new courses in its universities and technical schools to produce 250,000 graduates annually with proficiency in AI. The importance of data in the manufacturing sector is growing. Factories are becoming smart and the tools are intelligent (Qin et al., 2016; Zhong et al., 2017). Many global manufacturers take previously isolated data sets, aggregate them, and analyze them to reveal important insights. By resetting different parameters, one chemical company was able to reduce its waste of raw materials by 20% and its energy costs by around 15%, thereby improving overall value (Auschitzky et al., 2014). On the other hand, according to the chief technology officer of the Digital Manufacturing and Design Innovation Institute (King et al., 2015), “Manufacturing generates more data than. . . healthcare, more data than retail, finance. . . [Manufacturers] mostly throw away the data. Where they keep it, they don’t know what to do.” Advanced analytics provide a granular approach for manufacturers to analyze historical process data, identify patterns and relationships, and optimize the factors that