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2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 
19 – 22 June 2019, Coimbra, Portugal 
ISBN: 978-989-98434-9-3 
Information Flows Improvement in Production 
Planning using Lean concepts and BPMN 
An exploratory study in industrial context 
Ana Rita Arromba 
DEGEIT 
University of Aveiro 
Aveiro, Portugal 
ana.rita.arromba@ua.pt 
Leonor Teixeira 
IEETA/GOVCOPP/DEGEIT 
University of Aveiro 
Aveiro, Portugal 
lteixeira@ua.pt 
Ana Raquel Xambre 
CIDMA/ DEGEIT 
University of Aveiro 
Aveiro, Portugal 
raquelx@ua.pt 
 
Abstract — Technological evolution is currently triggering changes 
in companies’ environments. There is a continuous need of 
adapting processes to achieve competitive advantage. This paper 
describes a study developed in a sanitary systems multinational 
manufacturer that focuses on improving its global performance, 
especially in the production planning and control department. The 
main goals are to emphasise the importance of process modelling 
during the data collection, analysis and visualization procedures 
and to show the importance of inefficiency reduction in 
information flows. BPMN 2.0 was used to identify the data sources 
and information flows and Microsoft Power BI was used to show 
that it is possible to reduce waste in the processes. A combination 
of Lean Thinking, Information Systems and Industry 4.0 
principles and methods is used. The foreseen result is an 
information system that connects people, data and processes in a 
way that efficiency is gained in the daily tasks, in the decision-
making processes and in the identification and elimination of non-
value-added activities. 
Keywords – Information Systems, BPMN, Lean, Industry 4.0. 
I. INTRODUCTION 
Companies seek to improve business processes to facilitate 
information flows, create value and achieve strategic results, 
connecting all of their departments [1]. This study focuses on 
the production planning of a sanitary systems firm, where the 
production plans are constantly being adjusted. Despite having 
tools to analyze large amounts of data, production planners 
need a thorough organization of information into Key 
Performance Indicators (KPIs) to enable an efficient decision-
making [2]. In this case, the problem lies on an excess of files 
and manual tasks that impair data analysis and the decision-
making process. The goal is to boost the business processes’ 
performance, combining ideas from Information Systems, Lean 
Thinking, Industry 4.0 (I4.0) and KPIs. In increasingly complex 
environments, companies have to improve their information 
flows to avoid all type of inefficiencies. In this exploratory 
study, the data sources and the information flows will be mapped 
and identified, using BPMN 2.0, helping identify and thus 
eliminate the non-value-added tasks. An information system 
supported by a business intelligence tool that centralizes and 
quickly shows the information in real time will be specified. It 
will help the planners in their daily tasks and bring efficiency to 
the processes. The feasibility of this study will be discussed, as 
well as the obtained results and future work. 
II. THEORETICAL BACKGROUNG 
In this section the main concepts behind the development of 
this work are briefly presented, namely: Lean, Information 
Systems and Industry 4.0. 
A. Lean Information Management and BPMN 
Lean is a philosophy that aims to reduce waste and non-
value-added activities using the same or a smaller amount of 
resources [3]. Despite having its origins in production, Lean 
Thinking can, equally, contribute to information processes and 
flows, being therefore called Lean Information Management [4] 
[5] [6] [7]. Lean is based in 5 main principles [8]: specify value, 
identify value streams, make value flow, let the customer pull 
value and pursue perfection. Additionally, the 7 types of waste 
are also important in this context: Overproduction, Waiting, 
Transport, Extra Processing, Inventory, Motion and Defects [5]. 
More recently, an 8th type of waste was identified, Non-Utilized 
Talent. 
Within the context of Lean Information Management it is 
also important to understand that every company has its own 
business processes, and being able to model and represent those 
processes can help to achieve process efficiency [9]. Business 
process modelling allows the analysis, identification and 
specification of business processes [10] [11] and characterizes 
the flows between internal and external environments of a 
company [12]. In this context BPMN 2.0 arises as language 
that, according to [9], is a graphical readable and 
understandable modelling notation that shows the properties 
and execution information of a process. 
By using this graphical notation, it is easier to identify 
where the inefficient information flows are and thus apply Lean 
principles to improve the outputs of business processes. The 
determination of essential KPIs is an excellent example of this 
application because they represent quantitative crucial 
information in business environments and in planning and 
controlling. They help to construct information, promoting 
transparency and supporting decision makers [13]. Also, [14] 
describe a tree-like structure of KPIs to define the Performance 
Measurement System (PMS) of a Lean Production System. The 
 
2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 
19 – 22 June 2019, Coimbra, Portugal 
ISBN: 978-989-98434-9-3 
KPIs are identified and categorized in a multi-level hierarchy 
designed to give answers at strategic, tactical, and operational 
level. 
B. Information Systems 
An Information System (IS) is a tool that captures, monitors 
and displays information, “supporting people, organizations or 
other systems”. It is about helping people to find the 
information they seek without limit on its sources [15]. It also 
offers a diversity of opportunities for a company to automate 
and share its knowledge efficiently [16]. 
The idea is to apply an information system to the production 
planning, which is an area that requires cooperation between 
multiple units of a company. It is also argued that a strategic 
advantage is achieved by making the right decisions. Therefore, 
there is a need to have tools that quickly share information and 
support production planners in their decision-making. [17] 
propose a Hierarchical Decision Support System for production 
planning that enables planners to use structured planning 
algorithms in an interactive way. There is also a relation 
between information systems and the way they can support 
business processes. [18] suggest an approach that uses business 
process diagrams to manage and visualize its execution directly. 
This provides handlers with “means to control process 
execution clicking on graphical diagrams that represent 
executable business processes”. The execution mechanism 
opens new ways to follow-up the executed business processes. 
It is beneficial in terms of usability as the entire business 
process is visible to the users in a graphical form. 
Such tools that improve business processes of a specific area 
of a company are the proof that the proper use of information 
systems “enables the institutions to access their information for 
better and effective decision-making” [19] [20]. 
C. Industry 4.0 
[21] and [22] state that I4.0 characterizes the fourth stage of 
the industrial revolution. The main features are the increased 
flexibility in manufacturing, the mass customization, improved 
productivity and better quality. I4.0 is associated with all the 
mechanisms that promote connectivity between machines, 
smart production systems and integrated solutions [23]. 
With the boom of I4.0 comes a large and complex amount 
of data: Big Data that disrupts the senior management of 
organizations, pressuring directors to make decisions more 
rapidly and to shape their capabilities to address environmental 
changes [24]. This type ofdata comes in large quantities, is 
unstructured, has no direct relationship and must be analyzed 
within a very short time to create value [25]. This requires 
integrated technologies with agile information systems to 
enable an adaptation to the changing scenarios that characterize 
the industrial environments in I4.0 [26]. [27] built a model that 
evaluates data productivity reducing its waste and considering 
data availability, data quality and the performance of the 
decision-making. 
With regard to the benefits of I4.0, [28] showed that the 
development of new systems helps to create tools that boost the 
processes of a company. They state that smart manufacturing 
from I4.0 requires the gathering of information from intelligent 
devices and a use (KPIs) that consolidates and shows the 
information in real-time, improving the performance of the 
processes. [29] propose a solution that considers a IS connected 
to several parts of a supply chain and uses advanced 
optimization algorithms to generate process plans, orders of 
operations and schedules automatically. The IS establishes the 
link between smart products and an ERP. 
Based on the analysis of the theoretical background and the 
existing literature, the idea supporting this work is to better 
explore, based on a practical case, the relationship between 
Information Flows and Lean philosophy, considering also the 
automation of some processes in a I4.0 context, and how this 
can help the determination and use of critical KPIs within the 
production planning department of the company. 
III. PRACTICAL CASE IN INDUSTRIAL CONTEXT 
A. Problem Background and Goals 
The main goal of this study is to show how process 
modelling can improve efficiency in the procedure of updating 
the production plans. Production Planning and Control (PPC) is 
an important area in the scope of manufacturing companies, 
because it defines the course of production. PPC is defined as a 
work process that seeks to allocate human resources, raw 
materials, and equipment/machines in a way that improves 
efficiency [30]. Thus, in order to increase efficiency, it is 
important to reduce the non-value-added activities in the 
process. 
In the case that was analysed the PPC department develops 
3 plans that depend on the stage of production: Pre-Process, 
PVD (physical vapour deposition coating) and Assembly. Each 
plan has its own specifications and needs a set of detailed 
information to be completed. Currently, to get all the 
information that is required, a lot of time and resources are 
spent, which contributes to increased waste. The planners 
retrieve data from different sources and analyse it to decide 
what is going to be produced. As there is a lot of unstructured 
data during this process, there is also noise and entropy that 
cause disturbances, making the process unproductive. This 
entropy is also associated with a lack of quality in the 
visualization of data and some confusion with the information 
flows. The data needs to be analysed to have a meaning and 
consequently, the entropy blocks the data handling and the 
decision-making process of the planners. 
In order to solve this problem, the first phase was to map the 
processes, using BPMN 2.0, clarifying the relevant information 
flows, the data sources, the people involved and aligning the 
referred elements. After that, the main sources of waste, namely 
the non-value-added activities were identified. In this 
inefficient process an opportunity for improvement emerged, 
by creating an autonomous information system that streamlines 
the process, and gathers, centralizes and shows the wanted 
information in real time, supported by the features of a business 
intelligence tool – Microsoft Power BI. 
B. Methodology 
The methods and techniques used to achieve the solution are 
explained in this section. It was essential to gather information 
 
2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 
19 – 22 June 2019, Coimbra, Portugal 
ISBN: 978-989-98434-9-3 
about the processes, identifying the ones that needed to be 
improved and how to manage them, resulting in an architecture 
of the processes. 
Initially, top management established that the KPIs 
supporting the production planning needed to be improved, as 
well as the information flows’ quality. Hence, it was imperative 
to track the processes’ streams that include the determination of 
the KPIs and the development of the production plans. The 
information was collected using 3 different techniques: 
observation, documental analysis and informal interviews. 
First, to clarify and identify the processes, the planners’ daily 
tasks were observed and, while doing that, the boundaries of the 
processes were established, as well as its general and specific 
steps, the level of data processing and treatment. Second, as 
there is a considerable number of files, a documental analysis 
helped to understand the data and all the complexity associated. 
Additionally, through informal interviews done to the planners, 
it was easier to discuss the problems and identify what could be 
improved. The results were organized using BPMN 2.0 
diagrams that represent the process mapping through an AS-IS 
Model. The diagrams display information sources, flows, tasks, 
files and people involved. 
As it was an already-known tool amongst the supply chain 
team, Microsoft Power BI, a business intelligence software, was 
then used to obtain the final solution. 
C. Results and Discussion 
The first plan that is developed is the assembly plan (the last 
part of the production process) that, when finished, supports the 
PVD and pre-process plans. Each plan has a responsible 
assigned, except for the assembly plan that, due to its 
complexity, is divided into 3 parts allocated to 3 planners. The 
planners get data from the Enterprise Resource Planning (ERP) 
system of the company (SAP). Since it is a multinational 
company, there is a dependency between this department and 
the Central Production Planning and Control department. This 
department sends a file to the company every morning, called 
KPIs, to identify the daily production priorities. The KPIs file 
gives information about the products, the required safety stocks, 
availability of materials, backlogs (orders that are not shipped 
yet), backlog in transit (products that are on their way to the 
client), and performance measures like OTIF (on time in full, a 
delivery indicator), ageing orders (orders that have serious 
delays) and spare parts (for products that are already sold but 
have a piece that is broken). These performance measures have 
targets to guarantee the correct control of production. 
The plans require several inputs, from clients’ orders to 
forecasts or stock, as well as some other considerations that are 
also critical, namely the materials availability, the production 
capacity, how many pieces from a specific product are 
produced in each shift, among others. What is planned is 
transmitted to the production floor through Production Orders 
(POs) that are released by the planners using SAP. The POs 
represent the “green light” to start producing. If necessary the 
planners use SAP to make the required adjustments to the plans. 
Figure 1 –Production Planning Business Process Model.
 
 
 
 
 
 
 
 
 
Figure 2 –Assembly Plan Business Process Model. 
 
Figure 1 shows the general process required for updating the 
plans, which happens every week, and Figure 2 shows, in more 
detail, the process used to define the assembly plan. Once the 
plans are ready, one of the assembly planners places the 
information from the assembly plans in a file called Weekly 
Production Plan (WPP). After that, the pre-process planner 
 
2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 
19 – 22 June 2019, Coimbra, Portugal 
ISBN: 978-989-98434-9-3 
gathers the information from the WPP file, and adds the 
information from the PVDand Pre-Process plans to a file called 
Balance Sheet. This file compares what is planned with what is 
being produced, identifies what are the problems and how to 
solve them. Finally, each planner shares their plan in 
SharePoint, a system present in the intranet of the company. 
After understanding the procedure of the production 
planning, several problems were identified and, in order to 
better understand the root causes of the problems, an Ishikawa 
Diagram was made (see Figure 3). This diagram shows the 
“systematic relationship between a result or an effect and its 
possible causes. It is an effective tool to systematically generate 
ideas about causes for problems and to present these in a 
structured form” [31]. After observing and understanding the 
processes that the planners must follow to obtain the production 
plans, and with the help of the diagram, several possible causes 
of the problems were identified. The main causes of the poor 
decision-making and, consequently, of the ineffective 
performance of the production planning were identified.
Figure 3 – Ishikawa Diagram 
It can be observed in Figure 4, the main causes that were 
extracted from the Ishikawa Diagram. Among them, the high 
quantity of support files was considered critical because it not 
only creates a dependency of splitting the information but also 
causes dispersion and redundancy of data. 
 
Figure 4 - Hidden problems of production planning 
 
In addition, many of the information flows are happening at 
the same time, making the processes confuse and causing a lack 
of data quality. Moreover, there is an excess of manual work 
like, for example, organization and manipulation of data 
provided in Excel files and transferring data between systems 
that are not connected. Those situations should not happen, 
since it increases the time wasted and the probability of 
occurring errors. This leads to the need for carefully selecting 
and filtering the data and increases the complexity required in 
order to have the necessary information in a speedy way. 
Finally, there is also an unappealing visualization of files and 
indicators, which also contributes to increase the effort required 
to obtain the necessary information, and provides a confusing 
and disorganized environment. 
With regard to the improvements that have already been 
implemented, the proposed solution was developed using a 
business intelligence tool: Microsoft Power BI. This system 
imports several data by connecting to different data sources, 
using queries. It is also possible to create customized 
dashboards that show various types of information, based on a 
user-friendly interaction. The system improved data 
visualization and centralization and, moreover, the access to 
data is instantaneous, since the dashboard is easy and fast to 
update. It also gives the user the possibility to select only the 
required information, while having access to it in nearly real 
time. Depending on what the user wants to view, it is possible 
to click in the displayed graphics that will expand and drill-
down, for example, to the wanted year, month, day or to a 
specific value. Additionally, almost anyone can have access to 
the dashboards, if an authorization and a link are shared, making 
it possible to spread information in an broader way. 
A prototype was developed to show the potentiality of the 
features of this business intelligence tool. The KPIs used in the 
production planning, namely the OTIF, backlog and safety 
stock, were studied. Firstly, an history of those indicators, since 
2013 to the present, was built in an Excel file. That file was 
 
2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 
19 – 22 June 2019, Coimbra, Portugal 
ISBN: 978-989-98434-9-3 
connected to Power BI and the several different graphics were 
select for each of the KPIs. This is an interactive dashboard: 
despite having data from 2013 to the present, the user can drill-
down and select only data from a specific year (see Figure 5). 
This information system was evaluated using 3 metrics: 
Coverage, Response Time and Effort. This prototype covers all 
the KPIs used before, not adding any new ones, however. It 
shows a considerable gain of time regarding the provision of the 
wanted indicators and, as it is easy to use, it reduces the 
planners’ effort to access information. The results point out an 
improvement on the quality of the information flows 
throughout the production planning process. However, to better 
understand, quantify and enhance those improvements, it is 
important to combine Lean’s 8 types of waste with its impact 
on the information flows. To do so, Table I and II compare and 
make an analysis of the initial and the future situation, as well 
as the results and the improvement of quality resulting from the 
implemented information system. 
It can be said that the general improvement of the 
information flows directly contributed to a better performance 
of the production planning department. The fact that the data is 
more transparent also boosts the information flows performance 
and reveals efficiency not only in the production planning 
processes, but also in the production itself. 
TABLE I. IMPACTS ON INFORMATION FLOWS 
Type of Waste Impact on Information Flows 
Defects The excess of data sources sometimes makes the information 
created not reliable 
Overproduction The excess of non-value added data creates an excess of non-
value added information flows 
Waiting Planners have to wait for the right information because 
resources are not easily available 
Non-utilized Talent Planners do not innovate or create new ways of efficiently 
sharing information 
Transportation Excess of steps to achieve the right information and excess of 
movement of the non-value added one 
Inventory Excess of files that provide the same data, causing redundancy 
on the information created 
Motion Decentralization of data that creates dispersion as it is not well 
organized 
Extra work As the files are confuse, there is a need for creating new 
information to obtain another 
As it can be seen when comparing the two tables, by applying 
Lean to the information flows it is possible to improve data 
quality and data productivity, since it is more structured an 
organized. 
TABLE II. EXPECTED IMPROVEMENTS ON INFORMATION FLOWS 
Type of Waste Impact on Information Flows 
Defects There is a common data source, improving data quality, 
productivity and reducing errors 
Overproduction The easy access to Power BI and its information leads to less 
but more efficient information flows 
Waiting Better visualization of data leads to faster and better decision-
making 
Non-utilized Talent Planners can think about different ways of displaying the same 
information in the same platform 
Transportation The data is centered in the same IS, reducing the steps to 
achieve the right information 
Inventory The standardization of the dashboard reduces the number of 
files with redundant data 
Motion The data is structured and organized in the same place, 
reducing the unecessary movements 
Extra work Interactive and user-friendly dashboard that substantially 
reduces the effort of the planners 
There is a rational management of the files that are associated 
with the production planning, as well as a decrease in the human 
resources that are needed to do the data collection, treatment 
and analysis, making use of I4.0 principles in the way data is 
acquired treated and displayed. This dashboard is essential to 
reduce the number of files of support the planners used, 
reducing the effort they put in their tasks. As it is the user that 
defines what he/she wants to analyze, there is a reduction in the 
excess of information that is created. It is also an interactive and 
user-friendly platform with some important visual 
improvements in the format of the visualization of the graphics. 
The 8 types of waste were addressed and somefurther 
improvements on the information flows are expected. 
IV. CONCLUSIONS AND FUTURE WORK 
Business Process Modelling was crucial because it 
represents comprehensive solutions whenever talking about 
processes that need to be improved. It helps to understand the 
processes but also people, flows, tasks and files involved. It also 
makes it possible to identify the problems, especially with 
respect to the information flows, and points that require 
improvements, as well as the requirements needed to get from 
the problem to the desired solution. Hence, it is possible to 
detail and identify the necessary data for each step of the 
processes. BPMN 2.0 facilitated this exploratory work as it 
establishes the connection between processes and systems, and, 
in this case, it helps to understand where a process can be 
automatized thus serving as a basis for a future work related 
with the automation of processes. The use of Power BI helped 
to reduce the manual tasks performed by the planners, 
particularly in the updating of the information that is visualized 
and used. However, as Industry 4.0 is still a growing concept 
and has a variety of features, there is still a lot to be explored in 
the information flows perspective. 
An information system that centralizes the data was 
established. To develop a new system, the requirements defined 
need to be tested to understand whether they are feasible or not. 
One of the main challenges was precisely to integrate all the 
sources of data so that the information would be available on 
the same platform. Therefore, this solution represents a 
connection between several areas of knowledge used to solve 
the identified problems: Lean Thinking, to understand how to 
improve the information flows’ quality; Industry 4.0, to 
Figure 5 - Power BI prototype 
 
2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 
19 – 22 June 2019, Coimbra, Portugal 
ISBN: 978-989-98434-9-3 
understand the powerful features of Power BI; and Information 
Systems, to understand how to support the production planning 
process. 
To test if the system contributed to gain processes’ 
efficiency, 3 main performance measures were proposed: 
Coverage (if the KPIs are the same or if there are new ones); 
Response Timing (if the tool is considerably reducing the time 
the planners spend when accessing the wanted data); and Effort 
(if the planners still have to struggle to get the right information 
at the right time). Power BI is a software that provides visibility 
to the information flows, applying Lean concepts to 
Information Management, making it easier to understand if 
everyone is on the same page. A suggestion for future work is 
a more detailed feasibility analysis of this system, both in 
technical and economic aspects, to make sure this is a right 
solution for this manufacturing company. The definition of new 
indicators is also a suggestion, depending on the needs but 
focusing on the customer service. The quality of information 
flows gets naturally deteriorated over time and, bearing that in 
mind, for future work it is suggested that this system is 
monitored and maintained, also considering that the external 
environment of the company is changing, as well as customer 
requirements and the planners’ focus. 
To conclude, it is also important to point out that improving 
a process in a company is not easy, no matter if it is in the 
Production Planning or in another department. It requires 
change to the way people perform certain tasks, and changes 
sometimes can be hard to implement. It is recommended that 
the solution developed should not be too complex or it will 
become something that the planners will not use. If this new 
system is seen and showed as a way of improving and having 
better results, changes like the one proposed in this exploratory 
study can be successful. 
ACKNOWLEDGMENT 
This work was supported by Fundação para a Ciência e a 
Tecnologia (FCT), through CIDMA - Center for Research and 
Development in Mathematics and Applications, within project 
UID/MAT/04106/2019 and through IEETA in the context of the 
project UID/CEC/00127/2013. 
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