5 THE CROSSROADS MODEL AS A REFERENCE MODEL TO EXPLAIN CONTROL APPROACHES

Controlling material and information flows serves to achieve business objectives involving costs, inventories, lead times, and flexibility. The complexity and dynamics of a production and logistics system and its environment determines the suitability of a control approach for achieving these objectives. The profitability of the control system, that is, the relationship between the value of the control result and the use of resources, should be ensured.

The Crossroads model focuses on describing, explaining, and designing real production and logistics systems in the area of tension between lean and digital approaches of Industry 4.0 concepts. As a reference model, it structures the perception of the observer and clarifies the essential elements of different control approaches for material flows. The purpose is to analyze and improve a current situation. Since the model has a low degree of specificity, it can be applied to a wide range of industries and value-creation stages.

The underlying idea of the model is the traffic control of a road intersection.

Typical characteristics of traffic flow and traffic control based on rules and signaling devices such as traffic lights are transferred to the material flow control in a value-creation system. The model’s statements on the entity “road user” can be transferred to the entities of the value-creation system, such as components or end products. The analogy to the daily traffic control situation provides a vivid explanation of key characteristics, advantages, and disadvantages of different control approaches.

The model is not limited to control approaches based on lean principles or a smart factory. Due to the historical development of structures and processes in practice, a mixture of different approaches is common; therefore, four control approaches are presented in the following:

I. Static rules, facilitating a centralized technical solution II. Dynamic rules, utilizing decentralized technical solutions III. Lean principles for decentralized self-control of entities

IV. “Digital lean”: lean principles for decentralized self-control of entities and digitally connected entities and sensors

Figure 2 illustrates an overview of the control system based on heuristics without technical solutions to explain the basic idea of the Crossroads model.

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Rules based on simple heuristics without technical solutions

“Right before left” rule for traffic control

Situation:

 Traffic routes (material flows) “cross” at hubs (intersections)

 Subordinate processes interrupt the main flows according to rules

 Content-wise coupling of network paths via limited capacities per time unit

 Interdependencies with regards to speed and sequence

Undesirable consequences:

 Inefficiencies due to lack of synchronization: no continuous flow, fluctuating capacity utilization

 Downtimes and waiting times (stocks)

 Potential errors

 Lack of prioritization of orders

 Room for interpretation by employees, possibilities to circumvent rules Figure 2: Rules based on simple heuristics without harnessing technical

solutions.

Figure 3 displays the enforcement of static rules facilitating centrally controlled technical solutions such as traffic lights.

Control Approach I Static rules, facilitating a centralized

technical solution

Traffic light signals steer the traffic flow, based on pre-defined time intervals

controlled by a central IT system

Principles:

 Technical solutions for the implementation of simple rules

 Few process variants considered Advantages:

 No room for interpretation: compliance by employees enforced by signal controls

 Low potential for errors Disadvantages:

 No dynamic demand orientation, lacking flexibility: traffic light switches based on fixed, pre-defined time intervals

 No continuous flow: fluctuating capacity utilization

 No synchronized flowing in/flowing out of materials: downtimes and waiting times (stocks)

 Waiting periods for clearing the crossroads: buffer times

Figure 3: Static rules, facilitating a centralized technical solution (I).

Control Approach II in Figure 4 displays how control is based on dynamic rules that are implemented utilizing decentralized technical solutions. In contrast to Approach I, the traffic lights control the traffic flow based on demand. Arriving vehicles are identified, facilitating an electromagnetic induction loop under the paving.

 Arrival of the entities at the processing stage triggers activity: sensors control actuators

Advantages vs. Approach I:

 Demand orientation, flexibility

 Shorter downtimes and waiting times:

reduced stocks

Disadvantages:

 No continuous flow: fluctuating capacity utilization

 Waiting periods for clearance

 Processing sequence suboptimal under certain circumstances: “first-in-first-out”

for prioritization does not necessarily result in an optimal solution

 Potential for blockages

Figure 4: Dynamic rules, utilizing decentralized technical solutions (II).

An alternative control approach is based on the lean principles (see Figure 5), which are aimed at decentralized, demand-oriented self-control, for example in a kanban control cycle.

By contrast, the control of material flow in a smart factory is based on digital networks and sensor technology. As with lean, the focus is on decentralized, autonomous self-management, so that the digital, value-stream-oriented management Approach IV is referred to as “digital lean” (see Figure 6).

Combining well-established lean concepts with innovative digital Industry 4.0 approaches can increase customer value and avoid unnecessary time and resources. Due to the digital, real-time networking of research and development (R&D), sourcing, planning, production, and logistics in a smart factory, for instance, short-term, customer-specific requirements can also be taken into account dynamically during production [8].

Digital Lean – The Crossroads Model for Controlling Material Flows in Production and Logistics

Roads in the form of a roundabout (instead of a crossroads) without

central technical control

Principles:

 Self-control: the entity decides decentrally and autonomously about flowing in/ flowing out of the system

 Dynamic demand orientation Advantages vs. Approach II:

 Continuous flow

 More balanced, higher capacity utilization

Disadvantages:

 Processing sequence suboptimal under certain circumstances

 Potential for blockages

Figure 5: Lean principles for decentralized self-control of entities (III).

Digital lean is the ability of a value-creation system to harness digital technologies in a way that increases the maturity of lean principles in value-adding processes. This improves the efficiency both within an individual process and between processes.

Digitalized and networked means of production and workpieces enable real-time monitoring of processes via a digital image in the IT system. Based on this so-called “digital twin,” processes can be planned and controlled on a dynamic level. The digital control of operations in real time offers a high potential for streamlining [17]. It is possible to react quickly to unplanned events such as the failure of a machine, for example, by an independent, automatic adjustment of the material flow taking into account current capacities [8]. The collection of sensor data from a production machine enables predictive maintenance activities to be identified to avoid unplanned downtime and thus wasted resources.

In addition to the possibilities of digital technologies, the creativity of employees and the experience of the “human information system” should be exploited in solving problems and identifying improvements [20]. To achieve this, ad hoc networking of products, machines, and employees via mobile

assistance systems must be ensured. These systems provide employees with context-sensitive information on the status and performance of the value-creation system in order to further optimize processes.

Control Approach IV

Roads in the form of a roundabout (instead of a crossroads).

Digital network of entities.

Principles:

 Self-control: the entity decides decentrally and autonomously about flowing in/flowing out of the system

 Digital networks: synchronization of the entities; for example decentralized, dynamic adjustment of the speed

 Sensors collecting data on, e.g., speed, distance, geo-location

 Creating a continuous flow Advantages vs. Approaches I, II, III:

 Continuous flow

 Dynamic demand alignment, flexibility

 Consistent, high capacity utilization

 Minimal downtime and waiting times, thus minimized stock levels

 No waiting time for clearing the crossroads

 No blockages Disadvantages:

 “Fit” to existing solutions

 Level of investment Figure 6: Digital lean (IV).

6 THE CROSSROADS MODEL AS DECISION-MAKING MODEL FOR DERIVING RECOMMENDATIONS FOR ACTION

How can concrete recommendations for action be derived for business practice? As a normative decision-making model, the Crossroads model structures the selection of a suitable control approach and derives recommendations for action. The framework in Figure 7 enables the decision-maker to systematically grasp the problem. The structure and transparency of the decision field increase the quality of the decision.

A decision-making model represents the assessment yardstick and the decision field. The assessment yardstick comprises the objectives of the decision-maker. Since the decision of a control approach is accompanied by an investment, profitability is chosen as the target figure. Profitability describes the relationship between the value of the result of an action and the consumption of resources. The decision field describes the set of action

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control approaches of the Crossroads model. The states of the production or logistics system are mapped as a matrix over the dimensions complexity and dynamics. The dimensions “diversity of product variants” and “lot size” serve to operationalize complexity. A distinction is made between “low mix, low volume” and “high mix, high volume.” The level of dynamics is measured by changes in the product variant mix and output quantity over time. To operationalize the dynamics, the variance coefficient can be used for measuring the predictability of changes. This measure of the relative statistical variation of a product variant’s demand is calculated from the ratio of standard deviation and arithmetic mean.

The recommended course of action for a control approach results from the combination of the characteristics of the value-creation system under consideration, described by complexity and dynamics (see Figure 7).

Figure 7: The Crossroads model for deriving recommended actions.