Sustainability and digitalization are essential duties for companies to perform in the current socio-economic landscape due to risks caused by traditional manufacturing practices, and rules imposed by stakeholders and governments. Tools that help exploring uncertain future scenarios to address such a complex challenge are of vital importance for both businesses, governments, and financial institutions. This paper presents the IN4.0-SD, a novel system dynamics model to capture the dynamic interplay of industrial innovation, inequality, and inflation. The IN4.0-SD is a closed-economy System Dynamics model composed of three agents: sustainable oriented innovation business (SOIB), digital asset supplier business (DASB), and household. DASB and SOIB are both assumed to supply one product to the economy and fundamentally differ among each other in their business models. While the sustainable oriented innovation business produces and sells capital goods making revenue out of sales, digital asset supplier detaches the concept of production from sales moving toward an intangible economy, charging for a fee licence of their tools that can be distributed via a network economy. Simulations show the level of flexibility of the model in addressing a variety of scenarios, playing at the threshold of technology development, inequality rise, massive unemployment and providing an archetype for sustainable oriented innovation and digital transformation models. The findings suggested by the model analysis are used to infer conclusions for the wider society, including implications for sustainable oriented businesses and digital transformation. These are confirmed by previous studies, around the overall trend in wealth creation for large technology firms’ owners, potential impact for employment in the digital economy, and transformation for the labour market.

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A digitalization of business process through utilizing Digital Twins is an approach that assists companies to align themselves with changes of technology development, and accordingly, improve their outcomes. To take full advantage of implementing Digital Twins, the importance of the creative phase role as pillars of this technology on the performance of the other parts and overall outcome should not be overlooked. This research addresses the lack of an integrated framework for setting up the creative phase of digital twins. To design the proper framework, by relying on a qualitative empirical method, an interview with persons who are experts in the Digital Twin area was organized to collect the information about all obvious and hidden aspects of this phase and manifest what kind of entities participate in this phase, what potential challenges and obstacles exist and what solution is effective to overcome them. The structural feature of the proposed framework continuously prepares the system for changes, aiming to adopt improvement within. The findings of this study can be used as instruction by all companies that want to take the first steps toward the digital representation of physical assets, or for those who deal with Digital Twin and want to improve their systems’ interactions.

In recent decades, manufacturers’ intense competitiveness to suit consumer expectations has compelled them to abandon the conventional workflow in favour of a more flexible one. This new trend increased the importance of master production schedule and make-to-order (MTO) strategy concepts. The former improves overall planning and controls complexity. The latter enables the production businesses to reinforce their flexibility and produce customized products. In a production setting, fluctuating resource capacity restricts production line performance, and ignoring this fact renders planning inapplicable. The current research work addresses the MPS problem in the context of the MTO production environment. The objective is to resolve Rough-Cut Capacity Planning by considering resource capacity fluctuation to schedule the customer’s order with the minimum cost imposed by the company and customer side. Consequently, this study is an initial attempt to propose a mathematical programming approach, which provides the optimum result for small and medium-size problems. Regarding the combinatorial intrinsic of this kind of problem, the mathematical programming approach can no longer reach the optimum solution for a large-scale problem. To overcome this, an innovative agent-based heuristic has been proposed. Computational experiments on variously sized problems confirm the efficiency of the agent-based approach.