# Building Digital Shadows for Production Control

Günther Schuh, Andreas Gützlaff, Judith Fulterer, Annkristin Hermann

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Günther Schuh, Andreas Gützlaff, Judith Fulterer, Annkristin Hermann. Building Digital Shad-ows for Production Control. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.110-117, 10.1007/978-3-031-16407-1_14 . hal-05386036

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# Building Digital Shadows for Production Control

Günther Schuh, Andreas Gützlaff, Judith Fulterer, and Annkristin Hermann

Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Campus-Boulevard 30, Aachen 52074, Germany
{g.schuh,a.guetzlaff,j.fulterer,a.hermann}@wzl.rwth-aachen.de

**Abstract.** Digital Shadows can support stakeholders in production control by providing context-aware and aggregated information serving a specific task and thus supporting in the decision-making. In this paper, we propose a methodology for building Digital Shadows with focus on production control. We identify the building blocks for defining Digital Shadows consisting of purpose, models and data. Furthermore, we suggest an implementation for the methodology as a decision support tool for the stakeholders in production control. Finally, we illustrate the use of our methodology on the basis of a exemplary use-case of production control.

**Keywords:** Digital shadows · Production control

## 1 Introduction

Digital Shadows are a research area within the Internet of Production, a German Cluster of Excellence at RWTH Aachen University. Digital Shadows support the decision process by providing the relevant information. [1] For this purpose, data from different sources is collected, aggregated and analyzed [2]. Models are used to aggregate, link and analyze the data to generate the relevant information [3]. Digital Shadows are a promising approach to improve production systems by providing access to relevant information increasing the decision-quality [4].

Production control is well suited for the use of digital shadows due to the complexity of decisions [5]. The aim of production control is to realise the production plan, taking into account short-term disruptions [6]. However, responding to short-term disruptions is a challenge [7]. The task of the production controller is to decide on appropriate changes in the production processes [7]. These decisions have an impact on the overall system and are difficult to map using existing IT-systems due to the high complexity caused by multiple interactions and dependencies in production [8]. This leads to a local optimization of production control [9].

This paper introduces a methodology for describing and implementing Digital Shadows in production control. First, an overview of the theoretical background of Digital Shadows and Digital Twins is given. In Sect. 3 the methodology to
build Digital Shadows is illustrated. An use case explains the application of the methodology. Finally, a conclusion on the current state is drawn with an outlook on future work.

## 2 Scientific Basics

In the following, the theoretical foundations of Digital Shadow and its application for production planning and control are considered. First, a distinction is made between the terms model, Digital Twin and Digital Shadow. The main differentiating aspect is the flow of information between the digital and the physical object. A digital model is a digital illustration of an existing or planned physical object. It does not include any form of automated data exchange between the physical object and the digital object. The digital representation may contain a more or less detailed description of the physical object. The Digital shadow follows the physical object on the basis of an automatic one-way data flow. The Digital Twin is an extension of the Digital Shadow in that the data flows between an existing physical object and a digital object are fully integrated in both directions. [5] Since the Digital Shadow is to be used for a purpose-oriented application [10], it will be considered in the remainder of the paper.

A Digital Shadow is defined as a set of contextual records and their aggregation and abstraction, collected in relation to a system for a specific purpose in relation to the original system [11]. A Digital Shadow is composed of aggregated data traces as well as models [12]. The goal of using the Digital Shadow is to provide data-based support in the complex decision-making process and only present the relevant information to the user to reduce complexity [4, 13].

The Digital Shadow provides all information needed to successfully perform a task and make a decision. In context on manufacturing the objective of a Digital Shadow is to improve decision-quality and the performance. [4] In addition to this, the Digital Shadow can reduce the increasing complexity and information quantity of information systems for the user, by providing only with the relevant information [4]. To ensure this, it is essential to provide the information at the right time and in the right place [14,15]. The information is generated with the help of a Digital Shadow by linking, aggregating and analysing the data traces from corresponding models [16,17]. Data traces can originate from different sources and consist of time-dependent data, variable data and metadata. They describe a past as well as the current state [17–19]. Sensors used to collect the required data enable real-time bidirectional data communication between the physical system and the virtual system using integration technology that includes communication interface and security. Then the Digital Twin uses the analysis techniques to analyse the data for specific purposes and provides response based on the simulation results and directs further action to the physical system. [20] The application of the Digital Shadow for production planning and control offers enormous as the user receives the necessary information in complex decision situations such as on-time delivery, lead time, capacity utilization and inventory [21]. To this end, initial approaches have already been tested to
support a capacity control decision. As a result, the Digital Shadow provides the relevant information for decision-supporting production control. Therefore data of various systems and domains are aggregated to the required detail level. This relevant information includes configuration, costs and logistical target variables such as on-time delivery, lead time, capacity utilization and inventory [17].

# 3 Methodology to Build Digital Shadows

As stated in Sect. 2 Digital Shadows aim to support the decision process by providing the necessary information. Current research focuses on the description of Digital Shadows but does not provide an approach to implement Digital Shadows in practice. Aim of this research is to develop a methodology enabling production planner and controller to describe Digital Shadows so software engineers can implement them. Using the methodology decisions, models and data trace of Digital Shadows are specified.

## 3.1 Decisions

Each Digital Shadow supports a specific decision, like the termination of the order release or capacity control. In this methodology the elements decision problem, purpose, information needs and assessment dimensions describe a decision.

First, a decision problem of PPC is selected. It is described via the scope of consideration, the potential scope for solutions as well as the goal of the decision. For the example termination of order release all open orders with a specific due date are part of the scope of consideration and solution. The goal of the is to identify which of the orders should be released next to fulfill the logistical target values best.

The purpose of the decision is determined based on the decision problem. The purpose specifies the target of the Digital Shadow and is, either an optimization of a target value or an information on critical disturbances. For the termination of the order release a purpose can be the determination of the optimal release date.

The information requirements can be derived from the purpose. If the purpose is an optimization, the user requires information on the target values as well as the necessary parameter adjustments. So for the order release optimization not only the order release date but also information on material and resource availability are required. If the purpose is to identify critical disturbances users need information regarding the disturbance and the effects of the disturbance.

The assessment dimensions are used to evaluate the provided solutions. In PPC, these are typically the logistical target values and costs. The best evaluated alternative solutions are provided to the users. Based on the quantitative evaluation they can make a decision.
## 3.2 Models

Models represent the link between the information requirements and the data. Models can be either structure models or behavior models.

Structure models describe the relationships of the system and correlation between different models, these are e.g. ontologies or UML models. In the context of the Digital Shadow, structure models serve to identify and link the necessary behavior models. A structure models is described based on structure model type (e.g. ontology), aggregation level (e.g. meta model, application model) and the described system (e.g. actors, interrelationships).

Behavior models display the interdependencies within the system, these are e.g. simulations or AI-algorithms. They model the effect of system state change, e.g. the effect an order rescheduling on the overall delivery dates. To charcerize a behavior model production controller need to describe the model properties as well as the required input data and the output information. The model's input data is characterised via the required data sets (attributes), the data structure and the data quality. Even though behavior models in the digital shadow can exchanged the general goal of the model must be described by the properties. Based on these specification the best fitting model can be selected. The properties include the calculation aim (e.g. minimisation of throughput time), behaviour of the model (online/offline), global quality of the model (precision), and local quality of the model (interpretability, explainability). Additionally, adaptability and robustness of the model can be described. The information output is characterised and described in terms of accuracy.

## 3.3 Data Trace

In order to generate the information needed to make a decision, the relevant data traces of the system must be queried. A data trace consists of data points and metadata. Data points are the parameter values stored in the IT-systems. For the production control, relevant data points are, for example, order number, material availability or the delivery date required by the customer. For each decision specific data points in different aggregation levels are required. However, there are similar requirements for different decisions, for example, almost all decisions require data on the production orders such as the order number, the work plan and the planned end date. Once a Digital Shadow has been described, further digital shadows can be modelled with less effort.

The metadata is used to characterize the data points. Examples for meta data are data attribute characteristics (e.g. order number, customer), data storage location (e.g. ERP System), time of storage, data type, data origin (e.g. recording person, sensor), update frequency (e.g. every second, daily) and data format (e.g. image data).

Figure 1 gives an overview over the elements of the Digital Shadow. After the elements of Digital Shadows are described by production planner and controller a Digital Shadow can be implemented.
<figure><img src="image_4.png" /></figure>

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<figure><img src="image_6.png" /><figcaption>Fig. 1. Building elements of a digital shadow</figcaption></figure>

## 3.4 Implementation

For the implementation of a Digital Shadow, an infrastructure on the basis of a web application can be used to illustrate the building and setting of a Digital Shadow into action. A web application can be represented through two main components: the frontend and the backend. On the frontend, the user has the ability to interact with a graphical interface to build a Digital Shadow and call its output. The backend handles all interactions with the frontend in the background and forward processed results for graphical representation on the user interface. In context of implementing a Digital Shadow, the user has the ability on the frontend to either select from a set of predefined Digital Shadows or define a new one.

Building a new Digital Shadow requires from the user to select the involved models from a model catalogue. The model catalogue contains all possible models, such as behavioral models (e.g. simulation, optimization) along with meta-information on the nature of inputs and outputs that are expected with each model. Considering each model as a black-box, most models rely on data for the input and delivers results as processed data in the output. For example, given a combinatorial optimization problem (e.g. TSP, VRP), an optimization model needs as an input to have access to data from production (e.g. cost matrix) along with the underlying objective, cost function and constraints. The access to the required data is described in the model meta-information by data queries that target corresponding data sources (e.g. the cost matrix is defined in the ERP system, which can be acquired through an SQL query target its underlying database management system). Note that for executing the data queries, it is important that the backend has established data connections to the corresponding data sources.

The user has the ability through a model editor on the frontend to combine different models by linking the inputs to the outputs accordingly and thus chaining between the models. The chaining allows for structuring the execution of the models leading to the results that serve the ultimate goal from defining
the Digital Shadow. The model catalogue is hosted on a database with refer-
ences to models (including where they are stored and what dependencies they
have). Moreover, the model catalogue takes care of providing the required models
as well as their execution to delivers the results back to the frontend depend-
ing on the user interactions. In case inconsistencies (e.g. missing data values,
corrupted queries) are detected in terms of the definition of a model or their
meta-information in the model catalogue, the backend warns the user on the
frontend about the irregularities.

4 Digital Shadows for Production Control

In this section the building of a Digital Shadow is demonstrated for the exam-
ple capacity controlling. First the decision is described. A production controller
needs to decide if a change in the utilisation of the capacities is necessary. The
capacity plan of the next week is the considered scope. The scope of solution are
adaptsions of the production and capacity plan of the next week. The purpose is
to inform the production controller if there is a critical disturbance and which
possible actions can be performed to reduce the impact of the disturbance. The
required information is the necessary capacity and production plan changes. The
evaluation criteria are the adherence to due dates, the machines’ utilization, and
the expected manufacturing costs. To generate the required information different
three behavior models are necessary. One model is required to identify capacity
disturbances leading to critical disturbances of the production plan. The model
provides information indicating the critical resources and orders. The input data
are the capacity plan, production plan and current state of the production. A
data mining a approach is used to identify the capacity disturbances. The sec-
ond model, a optimization model, has the goal to identify possible changes of
capacities and the production orders in order. These generate different capac-
ity scenarios. This model contains for each resource possible capacity changes
like additional shifts or increase of speed. The required input data are necessary
resource changes. The third model is necessary to calculate the production plan
based on the adapted capacities and evaluate the new production plan. In this
use case a simulation model is used. The three models are linked to each other.
The first model generates input data for the second and the third uses the results
from the second model. The required data are determined by the first model and
are all data regarding the planned and current capacity and production orders.
For the orders the current working plan, planned start and end date (current
production plan) as well as is data are relevant. For the resources (machines
and personnel) the shift plan, availability, technical attributes for machines and
qualification for personnel are the relevant input data. In addition the costs are
needed to evaluate the alternative solutions. Necessary costs in the use case are
machine costs, personnel costs, costs for late delivery and costs for additional
capacity (e.g. external production, overtime). Now the production controller can
decide which capacity changes to make based on quantitative information. The
advantage compared to existing ERP solutions is that in the Digital Shadow
different models can be easily linked. Additionally, the if there is a better model the model can be changed. Also the user gets the right information and does not need to search for the information in different IT-Systems.

# 5 Summary

This paper introduced a methodology to build Digital Shadows in Production Control. The main elements of Digital Shadows are decisions, models and data traces. The methodology gives a procedure to describe the elements for production controllers. Based on the description, Digital Shadows can be implemented in software. An use case in the field of production control details the application of the methodology to describe Digital Shadows.

In contrast to existing IT-software Digital Shadows provide the relevant information for a decision to the user. Additionally, different models can be easily combined. In further research more Digital Shadows should be described and implemented to improve the methodology and to ensure comprehensive decision support in production control.

**Acknowledgements.** Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC-2023 Internet of Production - 390621612.

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