**1. Introduction**

Warehouse systems are elementary components of value creation systems to balance demand and supply with an optimal stock of goods. There is no management of value networks in which decisions do not have to be made about the right location, equipment and management of the warehouse. When design and warehouse management is properly understood, it has been shown to result in higher productivity, lower inventory and higher customer and employee satisfaction [1].

The corona pandemic has caused a rapid acceleration of the e-commerce market while highlighting the vulnerability of supply chains and the availability of materials, transportation, and production capacity. With the increasing importance of e-commerce, the demands on the warehouse are rising in terms of product variety, availability, and delivery times for the customer. It is not surprising that investments are currently being made in the management of value creation systems and their central element, the warehouse system [2]. Storage systems take on elementary tasks in the management of material flow: Goods receipt, put-away, storage, picking as well as dispatch. In addition to the actual storage and inventory management, further and diverse services are added, often resulting from a reconfiguration of the supply chain:

including packaging and labelling, quality controls, repairs, assembly, repackaging, reassortment, and more [3, 4].

In addition to high service performance, high warehouse-productivity is always understood as the goal. The most important factors affecting the productivity of a warehouse system include the number of employees and the degree of automation, which in turn interact with each other [5].

The adoption of technological developments is therefore inevitable for logistics productivity. The evolution of information and communication technology in the last decade resulted in the formulation of industry 4.0, Internet of Things or Cyber-Physical Systems, which we here use synonymously for the latest manifestation of digital development. The development of the digital transformation has been called a quantum leap, the 4th industrial revolution, which will radically change our economy, indeed our society [6]. It is therefore understandable that industry 4.0 will also have an impact on SCM, logistics and warehousing [7, 8].

There is a consensus that industry 4.0 pursues the goal of intelligent networking of products and processes in the value chain to increase process efficiency, improve customer service or offer more individualized products and services. We, therefore, follow the industry 4.0 definition applied for supply chain management of [8]:


Industry 4.0 is thus expected to make logistics systems more decentralized, self-regulating and efficient. In this context, the core term autonomy is used widely, frequently and consistently. Therefore, we use autonomy also for the warehouse as part of the value chain and logistics. Using the term, it also forces a differentiation from automated warehouses or smart warehouses. Whereas the former relies more on central units with little self-regulation and the latter describes above all the efficiency effect in the warehouse process generated by transparency [7]. In the context of autonomous warehousing, it is thus assumed that the warehouse subsystem can be designed as a decentralized system of the value chain, self-regulating and without human interaction. At least during defined periods of time—a shift extension, additional shift, whole day or weekend—autonomous operation would impact positively the overall equipment efficiency. Identifying and understanding the gap between existing warehouse systems and the ideal situation of an industry 4.0 solution would indicate necessary progress and actions to be taken.

Hence, our research questions are:

a.How to describe the maturity level for warehouse systems?

b.How to assess the individual maturity level?

c.Can action and development paths be derived from this assessment?

We structured the paper accordingly to the recommended methodological approach of maturity model development [9], starting with a literature review, followed by the model-building approach and the model description. The paper continues with the model application on a single case study and closes with preliminary discussion of the results and conclusions for research and management.
