DATA MANAGEMENT FRAMEWORK FOR MONITORING AND

ANALYZING THE ENVIRONMENTAL PERFORMANCE

Antti Sirkka

Tieto Finland, Tampere, Finland

Marko Junkkari

Department of Computer Sciences, University of Tampere, Tampere, Finland

Keywords: Life Cycle Assessment, Traceability Graph, Traceability Cube.

Abstract: Monitoring the environmental performance of a product is recognised to be increasingly important. The

stakeholders are pressuring the manufacturers for improved information about the environmental burden

caused by the manufacturing of a product. However, there are problems to accurately quantify the

environmental burden of an individual product because the supply chains are dynamic. In this paper we

present a model that enables calculating and monitoring the environmental performance of products at an

item level in a dynamic supply chain and performing multidimensional analysis of environmental data.

1 INTRODUCTION

A physical product has its own supply history

represented as a supply chain. In practice, the history

of the product is the history of its parts composed in

the supply chain. The precision of traceability of

products depends on how detailed the history of the

components can be traced. This paper deals with the

tracing of the emissions and resources of products

and their components. We give a logical framework

for tracing and analyzing the emissions and

resources even single products but as well larger

patches.

The supply chain can be defined as a network of

autonomous or semiautonomous business entities

collectively responsible for procurement,

manufacturing and distribution activities of a

product. In recent times the importance of

environmental aspects has been widely recognised.

The valuation of environmental impacts caused by

the production of products and services is becoming

more and more important.

The problem with measuring the environmental

impact caused by a product at the item level is that

supply chains are dynamic. A manufacturer can use

various subcontractors and supply various end

manufacturers or retailers in different countries. For

example a product that is transported from another

continent to a supermarket is bound to have different

environmental impact than another product that is

transported to a supermarket from a nearby

producer.

However the common method of calculating the

environmental impact on a product is to measure the

resources used, emissions and production in some

time period and calculate the average environmental

impact on the product. This does not take the

dynamic nature of the supply chains into account.

To be able to track the objects through the

dynamic supply chain, the products must be

identified. The development of an auto identification

enables us to identify an object moving in the supply

chain. This means that we can connect the physical

world objects with their virtual counterparts in

databases. With the traceability we can track the

relationships among properties of processes, in this

case the environmental burden caused by processes,

and actual product instances.

In this paper we demonstrate a model which can

be used to allocate the environmental burden to

individual products. Unlike existing methods our

model enables analyzing environmental impact on

the product level – not only average values. The

model supports for monitoring emissions (e.g. CO

)

and resources (e.g. Energy) in any precision level

only depending on how precisely physical products

Sirkka A. and Junkkari M. (2010).

DATA MANAGEMENT FRAMEWORK FOR MONITORING AND ANALYZING THE ENVIRONMENTAL PERFORMANCE.

In Proceedings of the Multi-Conference on Innovative Developments in ICT, pages 57-62

DOI: 10.5220/0003046400570062

 SciTePress

and patches can be identified and monitored.

Further, our approach enables multidimensional

analyses of data associated with the emissions and

resources of products and their components.

The rest of paper is organised as follows. Section

2 deals with environmental accounting. In Section 3

we introduce the traceability graph which is the

basis of our model. The implementation and usage

of the traceability graph is presented in Section 4.

Then we demonstrate how information associated

with the traceability graph can be used in

multidimensional analysis called the traceability

cube in Section 5. Finally, the conclusions are given

in Section 7.

2 ENVIRONMENTAL

PERFORMANCE

MONITORING

Nowadays the Environmental performance is being

monitored in most organizations at the company

level, resulting a total impact for the whole

company. The most used methodology is the

Greenhouse Gas Protocol which is a guideline for

estimating the greenhouse gas emission of an

organization. This kind of total organisation value

for greenhouse gas emission can’t be used for

measuring an environmental impact for a certain

product or service because all the emissions are

calculated together and the emissions are not

correctly allocated to products. Also the total

emissions for the life cycle of a product are not

calculated.

The product level measuring of environmental

performance is under development and many

different approaches are used for calculating the

environmental impact of a product or service. There

are many studies made about the different

approaches (Usva, Hongisto et. al 2009 and Dada et.

al 2009, 2010). The most important of these are the

international standards of life cycle assessment

(LCA) (ISO 14040 series) and eco-labels (ISO

14020) and verification (ISO 14064) and Publicly

Available Specification (PAS) 2050 which builds on

ISO 14040 and 14044 standards by specifying

requirements for the assessment of the greenhouse

gas emission. The international standards

organization has also started a subcommittee for

developing the standards for Quantification and

Communication of Carbon footprint of product (ISO

14067).

The LCA has four main phases. In the first

phase, the goal and boundaries of the life cycle

assessment are defined. This means that we define

the processes that we will perform the study on. In

the second phase, called life cycle inventory

analysis, input and output flows of the underlying

processes are defined, collected and calculated. If a

process produces more than one product, an

allocation is also needed. The third phase of the

LCA is impact assessment. First, the relevant impact

categories (e.g. Climate change, Ozone depletion or

Acidification) are selected. Then, the results of life

cycle inventory analysis are assigned to the selected

impact categories. For example, the carbon dioxide

is a greenhouse gas and is thus assigned to the

Climate change category. The last main phase is

interpretation where the conclusions of the analysis

are made.

In this paper we present the model for tracing

and storing the life cycle data about product

manufactured in dynamic supply chain. Unlike the

existing methods our model enables analyzing

resources and emissions at the single product level –

not only average values. This is achieved by

allowing gathering real monitored activity data from

supply chain processes.

3 TRACEABILITY GRAPH

The traceability graph is used to model the supply

processes of physical products and resources and

emissions associated with the products and their

components. The traceability graph has the ability to

manipulate products and the transformations of the

products. For example a product may be composed

from many parts or a product may be manufactured

using masses of raw materials. The traceability

graph has also the ability to manipulate the

properties of processes and to allocate them to

products that are handled in that process.

The traceability graph can be presented using

nodes and edges and their properties. A node is used

to describe a supply chain process. An edge is used

to describe a product flow between processes.

3.1 Supply Chain

The supply chain can be viewed as supply processes

following each other in a partial order. A

manufacturing process is an event that transforms

the input elements (raw material, energy) into output

elements (product, waste and emissions).

In a traceability graph processes can be grouped

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based on their process types, i.e. similar processes

are instances of a process type. Within a process

type the specific properties of processes, such as

timing, placing etc., may vary.

In Figure 1 there are seven process types (A, B,

…, G). Process type A has four instances. These

nodes have no predecessor which means that the

traced objects have been created in these nodes. The

objects are transferred forward in the graph. For

example, objects from Nodes A1 and A2 are

transferred to Node B1. Now objects are not

changed but object sets (product portions) of A1 and

A2 are unionized in B1. This also means that the

resources and emissions of A1 and A2 are

cumulated to the new set of objects. The objects

from B1 are transferred to the Node C1 where

products are classified and sent to one of the D

processes.

In the D processes objects are divided into

several objects. A double-headed arrow illustrates

this. For example a physical object is decomposed or

divided into parts. Then, parts may be classified and

sent to forthcoming processes. The E nodes receive

product portions consisting of these parts. In an E

process they are refined and sent to Node F1 which

is a shared process for all products. The products of

F1 are components for the G processes, i.e. in G1

and G2 objects are composed from the objects that

F1 yields. A shared start arrow illustrates this.

Figure 1: A sample traceability graph.

In a traceability graph it is possible to trace the

supply chain of an object, i.e. to find all the

preceding processes where the object at hand has

participated. This also means that all the information

related to those processes can be attached to the

object. Given the running example, let us assume

that we are interested in an object that belongs to

Node G1. Then, the processing history of the object

is a subgraph of the main graph. In Figure 2 the

colored nodes are processes in which the underlying

object, its part or a whole related to the parts has

participated. In the example, parts of the underlying

object have gone through F1, E2, E3 and D2,

whereas the larger objects consisting of the parts

have gone through C1, B1, A1 and A2. This

subgraph is also the supply chain of the underlying

object.

The traceability graph can also be used for

analyzing different aspects on processes. For

example a process type can be selected and we can

see how much some process causes the

environmental burden. Further, this analysis can be

done in a supply chain of one object or a set of

objects.

Figure 2: The supply chain of an objects belonging to

Node G1.

3.2 Data Management Primitives

Next, we introduce the properties of edges and nodes

of the traceability graph. For the reason of the

limited space the detailed and exact definitions of

the primitives associated with the traceability graph

are not given explicitly.

An object is the unit of tracing in a phase of the

related supply chain. This can be a single product or

a patch depending on the precision of tracing in the

underlying supply system.

A process node contains the identity of a

process, the set of product portions and the set of

attributes associated with the process. A product

portion involves the quantity of products, the

identifiers of objects and the ratio of the emissions

and resources compared with the total ones in the

process node. The ratio is calculated by an

application specific method. It can be based on the

portion of mass or used time of machines, for

example. Product portions of a process are viewed

through the end products of a process.

An attribute of a node determines information

associated with a process. In terms of input attributes

it can be described the resources of a process

whereas output attributes can be used for

determining the emissions of a process. Each

attribute has two values: one for the underlying

DATA MANAGEMENT FRAMEWORK FOR MONITORING AND ANALYZING THE ENVIRONMENTAL

PERFORMANCE

process and the other for containing the cumulated

values from the previous nodes. A cumulated value

is calculated based on the ratios of product portions

and quantity that is sifted from the previous nodes

via edges.

Via edges, products are sifted from a process to

another more precisely from a product portion to

another. An edge also determines the mapping of

objects between two processes. The mapping can be:

1. Equivalence: Objects from a start node of a

product portion are sifted to the related product

portion of the end node.

2. Subsetting: Only some objects are sifted to the

related product portion of the end node.

3. Supersetting: All the objects are sifted to the

related product portion of the end node but the

product portion of the end node contains similar

objects from another process node.

4. Division: Objects of a start node are divided

into smaller objects. If an object represents a

single product, this is portioned.

5. Composition: Products of the start nodes are

components for the end node.

In 1-3 the objects maintains their identities but in

4 and 5 the identities must be changed. In case 4 the

identity of a product is mapped with the identities of

parts that are produced from the product. In case 5

several objects are needed used for one composition,

i.e. the identities of components are mapped with the

identity of the related composition. It is worth noting

that a product of an end node may contain

components from several start nodes.

Through an edge the information of sifted

products from a node to another node is transferred

to an end node following the mapping of objects. An

edge involves those objects that are sifted from a

start node to the end node (only some products of a

product portion may be selected from other

processes). This part of the product portion of the

start node is called a sifted product portion. In

transferring products from a process to another, the

attributes must be re-calculated for corresponding to

the sifted product portion. This is based on the

ordinary and derived attributes. The derived

attribute is associated with an edge and it determines

the amount of an ordinary attribute that is related to

the sifted product portion.

4 IMPLEMENTING

TRACEABILITY GRAPH

The traceability graph is mapped to the relational

database as presented in Figure 3. We selected the

relational database because the standard OLAP

(Online Analytic Processing) methods (Chaudhuri

and Dayal, 1997) are used to further analyze the

huge amount of data that is a result for tracing the

individual objects. In Figure 3 PK means primary

key and FK means foreign key.

The information of the traceability graph is

stored into eight relations:

• Node relation is used to store the identities of

process nodes.

• Attributes (e.g. raw materials, energy) are stored

into Attribute relation

• Product types are stored into Product relation.

• The relation NodeAttribute is used to store

the process (Node) specific attributes. For

example 〈Process#1, Electricity, 100 kWh〉

specifies the use of electricity of Process#1.

• The relation NodeProduct is used to store the

information about product portions of a specific

process (Node). The column Ratio is used to

allocate the environmental burden between the

portions of products and by-products. For

example 〈Process#1, Product#1, 0.6〉 specifies

that Product#1 is an end product of Process#1

and the related ratio is 0.6.

• The relation Object is used to store the object

specific information like physical code of the

object and its volume.

• The relation ObjectRelation is used to

store the object mapping when object identities

are changed. The column Transformation

function is used to calculate the cumulated

attribute values.

• The route of the objects through a supply chain

is realised by the Route relation. This

corresponds to the sifted product portion.

Figure 3: Database schema for the traceability graph.

Node

PK NodeKey

NodeAttribute

PK,FK1 NodeKey

PK,FK2 AttributeKey

AttributeValue

NodeProduct

PK,FK1 NodeKey

PK,FK2 ProductKey

Ratio

Attribute

PK AttributeKey

AttributeName

Product

PK ProductKey

ProductName

Route

PK,FK1 NodeKey

PK,FK2 ObjectKey

Time

Object

PK ObjectKey

ObjectCode

Volume

FK1 ProductKey

ObjectRelation

PK,FK1 ParentObjectKey

PK,FK2 ChildObjectKey

TransformationFunction

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The relation model in Figure 3 can be easily

extended to include more product and supply chain

specific information. For example, we can

implement an organisational hierarchy by creating

Process, Site and Organisation relations

(Node→Process → Site→ Organisation).

This kind of extension enables analysis of

environmental data by using the hierarchy as a

dimension in multidimensional OLAP model.

5 TRACEABILITY CUBE

To be able to use the OLAP type operations for

analyzing the information of the traceability graph

we must combine the previous tables as a data cube.

In this work we will use the multidimensional data

model “MD” that is presented in (Torlone, 2003). In

Figure 4 the dimensions are presented as round-

cornered boxes, the facts are presented as boxes and

the measures as circles. The circles with drawn with

dashed line presents calculated measures.

Figure 4: Traceability cube scheme.

Figure 4 presents the traceability cube with some

example dimensions. The Process dimension can

be used to compare the environmental impact

between manufacturing sites and manufacturers. The

Object dimension is used to aggregating the

environmental data for different product groups. The

measure Flow Amount is the attribute amount for

an object. The measure Volume specifies the

volume of an object. In Table 1 some sample

instances over the traceability cube are presented.

The EPC column is the unique identity of an

object.

Table 1: A sample instance over the traceability cube.

The Flow Amount is used for calculating the

calculated measures – amount of emissions (e.g.

carbon dioxide, methane, nitrous oxides) and

amount of key environmental performance

indicators (see e.g. Lim and Park, 2009). The

emission amount is the amount of emissions caused

when using elementary flow (raw material or

energy) in some process. For example, carbon

dioxide emissions when using electricity from

Tampere electricity station in Finland were 194 g /

kWh in the year 2008. There are many

environmental databases that comprise life cycle

inventory data from different supply chain

processes. For example, the ELCD core database by

European Commission - DG Joint Research Centre -

Institute for Environment and Sustainability

comprises more than 300 process datasets (e.g. key

materials, energy carriers, transport, and waste

management).

Table 2: Emission and Impact Calculation.

↓

In Table 2 the emissions and impact category for

objects with code 2 and 4 are presented. Key

environmental performance indicators are calculated

based on the emissions. In this example we use the

global warming potential which is one commonly

used an environmental key performance indicator. It

is calculated based on carbon dioxide, methane,

nitrous oxide and several other emissions. The

measurement unit for the global warming potential is

kg of carbon dioxide equivalent which means that all

the other emissions are converted by using a

EPC Process Site Product Day Month Year

Flow Amount

Volume

1KilnDrying Mill#1 Timber 1 1 2010 Electricity 0,5kWh 0,30m³

2KilnDrying Mill#1 Timber 1 1 2010 Electricity 0,5kWh 0,35m³

3KilnDrying Mill#1 Timber 1 1 2010 Electricity 0,5kWh 0,33m³

1 Transporting Truck#1 Timber 2 1 2010 LorryTransport 100km 0,30m³

2 Transporting Truck#1 Timber 2 1 2010 LorryTransport 100km 0,35m³

3 Transporting Truck#1 Timber 2 1 2010 LorryTransport 100km 0,33m³

4KilnDrying Mill#1 Timber 1 1 2010 Electricity 0,53kWh 0,40m³

5KilnDrying Mill#1 Timber 1 1 2010 Electricity 0,53kWh 0,42m³

6KilnDrying Mill#1 Timber 1 1 2010 Electricity 0,53kWh 0,38m³

4 Transporting Train#1 Timber 2 1 2 010 RailTransport 20 0 km 0,40m³

5 Transporting Train#1 Timber 2 1 2 010 RailTransport 20 0 km 0,42m³

6 Transporting Train#1 Timber 2 1 2 010 RailTransport 20 0 km 0,38m³

EPC Process Site Product D M Year

Elementary

Flow

Flow

Amount

Volume

2 Transp. Truck#1 Timber 2 1 2010 LorryTransport 10 0 km 0,35m³

4 Transp. Truck#2 Timbe r 2 1 2010 RailTransport 200 km 0,40m³

EPC Emission Amount Unit

Impact