Stefan Lier

Business Models and Product Service Systems for Transformable, Modular Plants in the Chemical Process Industry

This paper examines business models and product service systems in the chemical industry. Three main players are currently involved on the market (equipment manufacturers, plant engineering companies and plant operators). Business models between engineering companies and plant operators are transaction oriented. Services such as maintenance and continuous improvements are not standard practice. As modular transformable production concepts emerge, the market structure is due to change. A new player, the module manufacturer, joins the market and competes with plant engineering companies. A modular and decentralized production concept requires different business models and service offers such as remote control or module substitution. Business models between operator and module manufacturers have to be more service orientated with risks shifting more towards the module manufacturer’s side.

Optimized Modular Production Networks in the Process Industry

One innovative production concept is currently highly discussed in the process industry: transformable, modular plant designs implemented in standardized transportation iso-containers Buchholz (Chem Eng Process: Process Intensif, 49(10):993–995, 2010, [3]).

Roadmap for a Smart Factory: A Modular, Intelligent Concept for the Production of Specialty Chemicals

Digitalization and increasing the flexibility of production concepts offer the possibility to react to market challenges in the field of specialty chemicals. Shorter product lifetimes, increasing product individualization, and the resulting market volatility impose new requirements on plant operators. Novel concepts such as modular production plants and developments in digitalization (Industry 4.0) are able to assist the implementation of smart factories in specialty chemicals. These essential concepts will be presented in this Minireview.

Tactical Planning of Modular Production Networks with Reconfigurable Plants.

Process industries are facing strong global competition in recent years. In order to stay competitive, it is important to quickly react to new market developments and to serve specialized product demands. To meet these requirements, flexible production capacity is required. Recently, the use of modular plants has received much attention as an answer to these new challenges. Modular plants consist of standardized process modules, which allow for quick assembly, disassembly and relocation of production plants. Using modular production concepts, the flexibility of the production network is greatly increased (Seifert et al., Chemical Engineering and Technology 37(2):332–342, 2014, [6]). The technical feasibility of modular plant concepts has been proven by several projects, like the EU funded “\(F^3\)-Factory” project and the “CoPIRIDE” project (European Commission: Final report for flexible, fast and future production processes, 2014, [3]). Modular plants offer an increased manufacturing flexibility in type, volume and location of production capacities.

Optimization of Modular Production Networks Considering Demand Uncertainties

In the process industry markets are facing new challenges: while product life cycles are becoming shorter, the differentiation of products grows. This leads to varying and uncertain product demands in time and location. As a reaction, the research focus shifts to modular production, which allow for a more flexible production network. Using small-scale plants, production locations can be located in direct proximity to resources or customers. In response to short-term demand changes, capacity modifications can be made by shifting modular units between locations or numbering up. In order to benefit from the flexibility of modular production, the structure of the network requests dynamic adaptions in every period. Subsequently, once the customer demand realizes, an optimal match between disposed production capacities and customer orders has to be determined. This decision situation imposes new challenges on planning tools, since frequent adjustments of the network configuration have to be computed based on uncertain demand. We develop stochastic and robust mixed-integer programming formulations to hedge against demand uncertainty. In a computational study the novel formulations are evaluated based on adjusted real-world data sets in terms of runtime and solution quality.