Artificial intelligence-driven technology increasingly shapes work practices and, accordingly, employees’ opportunities for meaningful work (MW). In our paper, we identify five dimensions of MW: pursuing a purpose, social relationships, exercising skills and self-development, autonomy, self-esteem and recognition. Because MW is an important good, lacking opportunities for MW is a serious disadvantage. Therefore, we need to know to what extent employers have a duty to provide this good to their employees. We hold that employers have a duty of beneficence to design for opportunities for MW when implementing AI-technology in the workplace. We argue that this duty of beneficence is supported by the three major ethical theories, namely, Kantian ethics, consequentialism, and virtue ethics. We defend this duty against two objections, including the view that it is incompatible with the shareholder theory of the firm. We then employ the five dimensions of MW as our analytical lens to investigate how AI-based technological innovation in logistic warehouses has an impact, both positively and negatively, on MW, and illustrate that design for MW is feasible. We further support this practical feasibility with the help of insights from organizational psychology. We end by discussing how AI-based technology has an impact both on meaningful work (often seen as an aspirational goal) and decent work (generally seen as a matter of justice). Accordingly, ethical reflection on meaningful and decent work should become more integrated to do justice to how AI-technology inevitably shapes both simultaneously.
Distribution structures, as studied in this paper, involve the spatial layout of the freight transport and storage system used to move goods between production and consumption locations. Decisions on this layout are important to companies as they allow them to balance customer service levels and logistics costs. Until now there has been very little descriptive research into the factors that drive decisions about these structures. Moreover, the literature on the topic is scattered across various research streams. In this paper we review and consolidate this literature, with the aim to arrive at a comprehensive list of factors. Three relevant research streams were identified: Supply Chain Management (SCM), Transportation and Geography. The SCM and Transportation literature mostly focus on distribution structure including distribution centre (DC) location selection from a viewpoint of service level and logistics costs factors. The Geography literature focuses on spatial DC location decisions and resulting patterns mostly explained by location factors such as labour and land availability. Our review indicates that the main factors that drive decision-making are “demand level”, “service level”, “product characteristics”, “logistics costs”, “labour and land”, “accessibility” and “contextual factors”. The main trade-off influencing distribution structure selection is “service level” versus “logistics costs”. Together, the research streams provide a rich picture of the factors that drive distribution structure including DC location selection. We conclude with a framework that shows the relative position of these factors. Future work can focus on completing the framework by detailing out the sub factors and empirically testing the direction and strength of relationships. Cooperation between the three research streams will be useful to further extend and operationalize the framework.
Research statementOur study analyses the factors that drive decision-making on distribution structures, including the layout of distribution channels and the locations of distribution centres. Distribution is a primary firm activity, which strongly influences logistics costs and logistics performance. Distribution is a challenging activity as customer demand is often volatile and unpredictable. Consumers continuously expect higher service related to distribution, e.g., same day delivery and more flexibility in delivery locations. Therefore, it is of strategic importance to shippers and Logistics Service Providers (LSPs) to decide which distribution channel layout to use and, accordingly, plan distribution centre location(s). Distribution structure selection concerns the number and locations of distribution centres, as part of the larger corporate planning process. The main questions we strive to answer in this paper are: (1) what are the main criteria that determine the spatial layout of distribution structures? and (2) how important are these criteria, relative to each other?Methodology The literature on distribution channel design mostly revolves around optimization methods; we are not aware of literature that takes a descriptive approach. We therefore develop a descriptive conceptual model that includes these factors, developed from the contextual literature around this decision. The second part of the study concerns the measurement of the relative importance of these factors. We implemented an elaborate survey and used the Best-Worst Method (BWM) to identify these weights. The survey considers different experts (e.g., logistics managers versus logistics professors) and population segments (e.g., in-house versus outsourced distribution).Data and resultsCurrently we are completing the survey dedicated to evaluating the above factors. We have received sufficient response to estimate a first model. These first estimations already provide useful results. Final estimations will be completed and reported in June 2017. At the I-NUF conference we will be able to present the results and analysis of all factors when comparing respondents and parameters.Preliminary conclusionsBased on literature review, eight main factors – divided into 33 sub factors – are included in our research: 1) Demand factors, 2) Service level factors, 3) Product Characteristics factors, 4) Logistics costs factors, 5) Proximity-related location factors, 6) Accessibility-related location factors, 7) Resources-related location factors and 8) Institutional factors. A number of hypotheses were built from the literature analysis relating, for example, to the relative importance of service- and cost- related factors within different industries. We will revisit these hypotheses and provide the quantitative results of the importance of the individual factors in our paper and at the conference.
Service logistics in de vliegtuigonderhoudindustrie is een zeer kennisintensieve en concurrerende markt. De meest cruciale factor in deze industrie is het laag houden van de downtime tijdens maintenance, repair en overhaulactiviteiten. Met name opslag, distributie en het managen van spare parts spelen hierin een belangrijke rol. Om tijdig vliegtuigen te kunnen onderhouden, hebben onderhoudsbedrijven vaak duizenden onderdelen, van kleine ophangpennen tot zeer dure motoren, op voorraad. Hierin zit dan ook de paradox: onderhoudskosten dalen door lagere down time en grote voorraden zorgen op hun beurt weer voor hoge warehousing kosten. Het lectoraat Aviation Engineering voert thans een RAAK-MKB project uit waarin primair wordt onderzocht of historische onderhoudsdata kan worden gebruikt voor MRO-onderhoudsplannen die de downtime verlagen. Gaandeweg de uitvoering van dit project is echter gebleken dat niet alle onderhoud van te voren gepland kan worden en dat juist real time data tijdens de vlucht erg relevant is voor snel en efficiënt onderhoud. De doelstelling van dit KIEM-project is enerzijds het vergaren van nieuwe kennis en inzichten over service logistics en het daarmee aanjagen nieuw onderzoek waarin wordt onderzocht of de inzet van real time big data bijdraagt aan het verminderen van de downtime. Anderzijds wordt onderzocht of nieuwe samenwerkingen (met IT-bedrijven) mogelijk zijn die voorraadkosten verminderen. Onderzoek wordt gedaan naar: 1. Knelpunten voor de inzet van real time big data in relatie tot MRO-activiteiten. 2. Vraagarticulatie en samenwerkingsmogeljkheden met nieuwe mkb-bedrijven. 3. Spare part warehousing efficiëntie (parts pooling). 4. Infrastructuur en standaarden voor opslag en toegankelijkheid van gezamenlijke en individuele (bedrijfsgevoelige) data. De HvA, NAG en JetSupport verwachten dat met dit project nieuwe mkb-onderhoudsbedrijven, vliegtuigmaatschappijen en overheden gaandeweg het project gaan aanhaken. Uitkomsten zijn enerzijds nieuwe kennis en inzichten op het gebied van service logistics en anderzijds commitment voor een vervolgonderzoek op het lopende RAAK-project.
In September 2018 a gaming dashboard is implemented and reviewed on effect at Jan de Rijk, Gebroeders Versteijnen and Merba. The dash board should give insight in the individual and team performance of employees in the their work processes through a gamesome modern visualisation‘In what way is it possible to design and apply ‘game design techniques’ and ‘game elements’ in performance dashboards, so that employees are constantly motivated to improve productivity, quality and safety of their individual proceedings and learning, so that the investment in gamification is profitable?’
SHAREHOUSE is een ruimte voor bedrijven in het STC om te experimenteren met eigen technologie. De experimenten worden wetenschappelijk gestroomlijnd en gericht op dataverzameling ter verbetering van magazijnwerk. Moderne technologieën, mens-technologie interactie, technologieadoptie en sociale innovatie, veiligheid, ethiek en duurzaamheid en de benodigde skills voor (toekomstige) medewerkers in de logistiek staan hierin centraal. SHAREHOUSE creëert tevens een open leeromgeving voor studenten en (MKB-)bedrijven, zodat ze in een praktijkomgeving ervaren hoe zij automated guided vehicles, virtual/augmented reality en wearables en exoskeletten voor goederenverwerking in een magazijn kunnen implementeren en beheersen. Publiek-private learning communities zorgen voor duurzame samenwerking tussen de belangrijkste stakeholders.
