This is a new post by my good friend Toralf Igesund. Toralf works as the head of planning department in BIR, in Bergen, Norway’s second largest waste management company that is responsible for managing the waste of roughly 320.000 inhabitants in the municipalities owning BIR. Toralf has already contributed with two great posts, one about Social Changes and Waste Management, and another about the strong linkages between waste legislation and consumption growth. Today’s post is about the rise of big data sets for waste management and the experiences that have been already gained in BIR. It’s a great, eye opening post – enjoy it. In case you have not already done it, I strongly suggest to read Toralf’s previous posts to understand the details in a more comprehensive way.
“My previous blogposts have talked about how the waste curve in my city, Bergen in Norway, reflects our changing society. If we indicate the different industrial revolutions, we can clearly see that the first industrial revolution, which started around 1850 with steam engines, spinning machines and the first hydropower did not result in more waste from private households. The second industrial revolution represents mass production, first introduced in slaughterhouses and car assembly lines and then spread to other industries. It took decades before it had impact on the economic prosperity in Bergen.
After WW2 the society changed quickly as new industries emerged, many of them employed women. This led to more double income families, with less time for cooking, which increased the demand for more processed food like conserves etc. The waste curve starts to rise in this period because of more packaging waste, but also because a general increased wealth led to increased consumption. Mass production correlates to mass consumption. Waste composition changed also. Two new industries gained momentum after WW2; the chemical-, and the petrochemical industry. They have both had an exponential growth and the continuous innovation has resulted in new products and chemical additives. Around 1960 a series of new manmade product entered the market: plastic. These cheap, versatile, formable products started their journey to conquer the world.
The 70ties continued with higher welfare and higher consumption. The waste curve continues upwards into the 80- 90ties in the time of computerization and Internet.
Waste-curve vs data-curve
The amount of collected data connected to waste handling in BIR started to rise rapidly after 1980. The accumulated data in 2016 divided by the number of inhabitants is 3Mb. This represents the size of a Kindle-novel (300 pages). It is important to clarify that this amount of data does not represent information about any specific person.
Digitalization of collection
Statistical data were first entered as numbers in a book to document the collected amount. In the 1980ties truck scales were introduced to register weight of waste and separated materials for recycling. In 2008 BIR decided to introduce a “pay-as-you-throw” fee. This involved RFID-chips on bins, antennas on the collection trucks and IT-systems. This complex and expensive system was earned back in few years due to elimination of free-riders. More important – PAYT fee gives the household a small economic incentive that has led to better sorting and recycling. Further growth of data collection will happen when sensors are added to other (underground) collection systems. Sensors like the ones delivered by Enevo will report when glass-bins should be emptied and this should give more efficient service and less problems with over-filled containers.
It is, however, important that data is submitted on a format that can be included in the company’s data warehouse. There is a need for standardization.
Use of these data should be done carefully. Data includes duplicates and irrelevant noise, but there are a lot of relevant information that is secured and analyzed. This results in knowledge that leads to actions and decisions.
Data in BIR is organized in data warehouses and statistics are extracted and analyzed with Business Intelligence tools. This results in reports related to performance management or enterprise reporting. It is a goal to improve this reporting so it is in “real-time”. It is also possible to carry out “data mining” and produce special reports. In the future these datasets can be exploited in new ways to improve the services or for research in new fields.
Digitalisation of sorting
The waste industry invests in automatic sorting facilities for paper, cardboard, plastics, metals etc. Useful materials are identified with near infrared radiation (NIR) or other sensors, and are sorted out automatically. These facilities generate huge amounts of data to identify different materials, and could be analyzed to identify trends.
Digitalisation of customer relations
A public waste company has every household as customer, and good information and service is important. Websites, social media and apps substitute customer newspapers and leaflets. Customers expect to find relevant information about their services on the web or on their phone. This means giving the customers access to their own data in a secure way. Questions and complaints that today are answered by a service center in the opening hours, will partly be answered by chatbots (chatting robot – using artificial intelligence) in the future.
Waste handling is about handling waste and resources, but it is also about handling people, – and handling an increasing amount of data. Our industry is facing the common challenge – we have to introduce modern technology, but remember our customers.”