Authors: Jakob Petri, Lawrence Jongi, and Sandeep Raju
1. Abstract
Over the last decade, the consumer base has shifted adversely towards E-Commerce. Thus, a correlatable increase in the packing material consumption by businesses and its adverse environmental impact. This paper focuses on the CO2 emissions and inefficiencies generated at various processes starting from packaging materials, pack processes, and freight distribution.
A proposal is suggested by scrutinizing the packing and distribution processes of WAGO, a German electrical components manufacturer, aiming to reduce CO2 emissions by minimizing packaging materials consumption by optimizing box size, specifically focused on shipments with multiple goods of different sizes. This decrease in box sizes not only reduces pack material consumption but also leads to a decrease in the number of shipments per load carried out by the freight due to more available container/pallet space.
Keywords: E-Commerce, Packing materials, Optimization, CO2 emissions, Energy mix, freight
2. Introduction
The customer demands for shorter delivery times, traceability of their deliveries, etc. raise the need for improved logistical processes with a higher level of automatization. When this trend is coupled with the rise in e-commerce due to its popularity and other crisis events like the COVID pandemic leads to high packaging material consumption, and thus emissions via pulp production.
Because of the increasing effects of climate change from pulp manufacturing, the European association representing the paper industry (CEPI) developed goals to achieve net zero emissions following the models from the 2015 UN Sustainable Development Goals and the 2045 German government (PIA 2022).
This paper aims to address these developments in an exemplary and operative approach. Therefore, it proposes a new packaging process in a German manufacturing company. A small change, like minimizing the size of freight cardboard consumption can lead to a tremendous increase in the specific CO2 footprint via the principle of the economics of scale.
To make this topic more relatable for the reader of this paper, imagine a day-to-day order from an e-commerce provider like Amazon or Alibaba. Although those companies, due to their big market power and the number of shipments, develop into a technological role model for many, most people still know the following situation: Your order, consisting of a few different products, arrives to you in obviously oversized cardboard, filled up with a tremendous amount of filling material. And most customers can catch their self, giving this amount of material directly into the garbage. The following process will address this practical waste issue as well.
3. Status Quo at WAGO
This abstract analyses the current situation of the chosen logistical process at WAGO. Therefore, the company will be presented briefly. Afterward, based on data provided by the company, the processes will be explained and illustrated, using flowcharts. Finally, the status quo is quantified in an ecological and economical way, by investigating the CO2 footprint and the process costs of WAGO.
3.1 The WAGO GmbH & Co. KG
The WAGO GmbH Co. Kg is a manufacturing company, founded in 1951 in the city of Minden, North Rhine Westphalia, where it’s headquarter still is. The company was able to achieve a turnover in 2021 of 1,19 bn €, which means a tremendous growth of roundabout 25 %, compared to 2020. Today WAGO employs 8.600 people, mostly working in one of the nine manufacturing sites in Europe, China, and India. The company claims the world market leadership in the field of electrical interconnections via spring technology for itself, as the inventor of the so-called “cage clamp”, the main component of the products, which enables plug-n-play interconnections of wires. The company acts mainly in the product- and market segment of electrical interconnection and switch gear cabinet components but is offering interface electronics like relays and optocouplers and programmable logic controllers. Currently, a new business unit for customer-specific, assembled switch gears cabinets is in development. (WAGO 2022)The overreaching sales model of the company is a business-to-business approach, while the variety of typical customers reaches from big wholesale companies in the specific branch, over big companies in different sectors, like retail, building, rail, offshore, etc., to small and medium-sized electrician firms.
3.2 Logistical processes in the current situation
WAGO uses a global logistics hub strategy, except for market-specific products. Their global distribution center and warehouse is in Sondershausen, in the state of the German state of Thuringia. The site was founded in 1990, after the German reunion, and flourished through constant enlargement. To determine the current logistical processes and to get first rough numbers of the logistical capacities and the freight structure at WAGO, which is necessary to estimate the CO2 footprint and the process costs, an interview with a representative of the logistical department was conducted and documented.
Today, the warehouse in Sondershausen is fully automated and has a capacity of over 247.500[1] totes (LE 2022) (name for small load carriers or boxes). The packing and shipment department at WAGO has a capacity of up to 5500 cardboards per day. Those packages are distributed like the following: 40 % of WAGOs turnover is made in Europe except Germany. The second biggest shareholder is Germany in an isolated view, with about 35 % other noteworthy local markets are China, Japan, and the USA. For the delivery of those cardboards to the customer, four different methods of transportation are in use. 7 % of the totes are transported by plane and 9 % over sea by vessel. Most of the deliveries are fulfilled on the road in two different ways: 31 % of the amount is directly shipped from the distribution center to the customer with a forwarding company as a third party. 53 % of the deliveries are forwarded through parcel services, eventually because of the rise of e-commerce activities on the customers but also on the company’s site.
In the following, the current logistical processes of picking, kitting, packing, and shipping at WAGO are going to be analysed, as illustrated in figures 1 and 2.
[1] all confidential numbers have been changed with a specific manipulation factor, this elaboration contains sensitive information of WAGO and is explicitly not intended to be passed on
Figure 1: Logistical processes at WAGO (LE, 2022)
The physical logistical processes at WAGO, from a holistic point of view, can be described as the following. WAGO uses a “goods-to-person” system, meaning that the material is automatically picked from the central warehouse, based on the specific positions of the customer order, the delivery date, and the needed quantities of each position. The so-called “Pick Station” is a manual workplace, using a pick-by-light system (Poke Yoke). The name probably misleading because this workstation consolidates the typical processes of picking, kitting, and packing. The parcels are closed automatically, as well as the delivery note is applicated to the cardboards. In the last two company internal steps, the various parcels are consolidated, regarding e.g., their geographical destination, the forwarding company, the customer, etc., and finally loaded on a trailer. The following figure elaborates and illustrates a specific and critical work step, the combined process at the so-called “Pick Station”.
Figure 2: "Pick Station" work process (LE, 2022)
The workplace is connected to two different conveyor systems. The drain and source of the first system, which can be schematically seen as a circle, is for the boxes (called “totes”) with the products. (red boxes in the picture) Each box contains a certain amount of one specific product. The second conveyor system is for packed cardboards (grey boxes in the picture), as illustrated in figure 1. The worker fulfills the following steps: Supported by software, the worker chooses one of five different cardboard sizes and folds the parcel manually. The suggestion for the optimal cardboard bases on a software, which calculates the delivery-volume, based on product data. The goal value of this calculated volume, divided by the cardboard volume, is 80 %. Therefore, this thesis is based on a current average filling level of 75 %, although the correct value, regarding WAGO, can vary by ~ 5 %. After folding the cardboards and placing them on the second conveyor, the worker extracts the products from the source boxes, until the order is completed. Through this process, the picking, kitting, and packing is consolidated into one single but complex step. The third step is to fill the parcel up with filling material, a paper-based air pillow. According to calculations by the company, WAGO uses 22,356 kg of filling material per year. Finally, the worker applies a barcode for tracking and directing the parcel on the conveyor.
As WAGO announce a new construction of a distribution centre with robot picking solutions which should be in use in 2024, the ongoing changes provides space for a variety of improvements in the three sustainable dimensions of the triple bottom line (economical, environmental, and social aspects).
3.3 Calculation of the current CO2 footprint and the process costs
To calculate the current CO2 footprint of the specific process at WAGO, this paper utilizes various standard values for costing and emissions, based on web research. The following chart shows the consumption of packing material and its emissions of greenhouse gases to the environment. The calculation is based on an average of 1.85 kg CO2 e emissions per kg virgin cardboard material(Sus 2022), and 1.38 kg CO2 e emissions per kg virgin filling material(EEA 2018). We assume, that those cardboards are currently made from 100% virgin material.
Table 1: CO2 footprint - current material consumption (own representation)
The emissions per box size per year are determined through the weight of each size while assuming that the annual shipping amount of each box size is equally distributed. The emissions per year, regarding the virgin material used, is 1.74*106 kg per year.
The next figure shows the annual emissions from transportation, utilizing the four different alternatives, described in paragraph 3.2. It is important to understand, that this calculation is based on the transportation amount for the distribution of finished goods only, but explicitly not for incoming goods and raw material, the transport of semi-finished products, machinery, etc. Typically, the emissions are calculated using the unit ton-kilometers (t*km). The CO2 emissions in kg per ton-kilometer are taken from the following source:(EEA 2018). Various assumptions have been necessary, to calculate the CO2 footprint in a qualitative good, and representative way. Those can be found in the third column of the table.
WAGO emits 7,78*106 kg CO2 per year into the atmosphere, only by their distribution transports. It is noteworthy that, according to our calculations, 94 % of the emissions are released through air freight, although only 7 % of the deliveries are shipped this way. The supply chain or transport management at WAGO should try to shift the amount of fast, but expensive and especially polluting air freight, towards other ways of transportation of decentralized warehouses on a strategic level.
Table 2: CO2 footprint - current transportation/ distribution (own representation)
The last part of this abstract is to calculate the current material and freight costs. WAGO claims to pay 811.80 € per 1000 cardboards. With an average weight of 480g per cardboard, assuming an equal distribution over the five different types of boxes, this leads to a cardboard price of 1.69 € per kg. To calculate the annual costs, the number of cardboards that are shipped in one year is necessary. Calculate with 5,500 boxes per day and 350 workdays per year, WAGO ships about 1,925,000 cardboards to its customers per year. The price for those cardboards would be 1,562,715 €.
For the transportation of those parcels, this paper relays on open-source data for the average transportation costs, provided by the Netherlands Institute for Transport Policy Analysis(Meulen, Grijspaardt, et al. 2020). Meulen, et al. estimate the average costs per ton-kilometer, based on data from the year 2018, which can be found in the third column of the following figure. The calculation in this paper is based on the numbers of ton-kilometers from figure 4, only those for parcel service are summed up. The freight can be characteristically seen as break loads, meaning that we don´t use containers with a heavy tara-weight but single parcels, mostly on euro-pallets.
Table 3: Current freight costs (own representation)
Assuming, that WAGO pays 100 % of the freight costs or at least includes them in their product price calculations, the company spends annually about 3.76*106 € for the distribution of their products.
Finally, to address the triple bottom line of sustainability, factors for social improvements, based on currently occurring issues in the logistical processes are investigated. First, the manual folding and handling of the cardboards, as well as the picking and placing of the items for the customer require the employees to wear gloves, as the material is rough and will affect the workers skin health. In a noticeable percentage of packed customer orders, the worker must change the cardboard because the suggested one, by the currently used software, is too small. This not only leads to and increased manual efforts and resulting costs, but is ergonomically difficult, as the workstations are not designed for this type of processes.
4 Process proposal
This paper proposes two mutually complementing solutions to optimize the packaging process. One is an immediate short-term solution that leverages ITC through optimization, and the latter for the future long term, that used additive manufacturing. The first solution allocates the items into the bin by various orientations, and the latter further decreases the material consumption.
In the real WAGO scenario, practical implementation issues will affect the introduction of the following proposed solutions. For academic purposes, we are excluding the following scenarios.
The item catalog of WAGO is imperfect, but this paper assumes that the item’s dimensions data is accurate. Therefore, the calculations are based on artificial data for products and a list of 100 random, complex customer orders.
In the real company practices, cardboard boxes have cutting waste which this paper provides a solution in the final proposal. Therefore, we ignore this waste in our calculations, but is addressed in the second proposal.
The proposal mainly concentrates on emissions and material consumption but not focuses on economic influences on a third party, like a parcel service. Stacking ununified bins on a pallet or similar load carrier can lead to further optimization problems, which should be further investigated.
4.1 NP-Hard 3D Bin packing optimizer
To reduction of emissions, the cardboard material consumption can be optimized using the py3dbp package licensed by MIT(Erik Dube). This algorithm optimally packs cuboid items into a 3D rectangular box solving an ‘NP-Hard’ problem using computer solvers, which otherwise is practically impossible. The basic premise of the algorithm is as follows. For a finite set of items i, with three dimensions wi, di, and hi corresponding to item width, depth, and height respectively. These items can be rotated orthogonally, meaning swapping the sides with Width (wi), Depth (di), and Height (hi) in specific manners as provided below. The opposite sides are equal, hence there are only 3 distinct facets, and the algorithm achieves all 6 different rotations.
Figure 3: Item coordinates and rotation configuration
The solver problem statement is to fit all items into the biggest box. It is more material efficient to use one big box to fit all items instead of multiple small boxes. The total volume of all items must be lesser than the bin, and the total volume difference should be minimized, there are the only two constraints used.
The algorithm allocates the biggest items to the smallest items in the bin. Algorithm results contain the 3D coordinate location of the bin where the items need to be placed and the orthogonally rotation. A sample result is added in the appendix.
Below is the allocation model’s flowchart. At the first iteration, the model creates a perfect cube and fits all the items with the highest fill rate for each customer. A buffer expansion of 1-2% is added to all items for movement while fitting into the bins.
In the second iteration, the box dimensions are reduced to increase the fill rate, thus reaching a sub-optimal solution.
Figure 4: The model process flow chart depicting Loop 1 for bin assignment, and Loop 2 for fine-tuning
4.2 Model simulation findings and results
We generated Synthetic customer and item data to evaluate this model’s efficiency hypothesis. The generated statistics show the efficiency of the model.
4.2.1 Data creation
For this paper, we create two thousand synthetic article lines with random minimum and maximum lengths, widths, and depths between 100 mm to 500 mm. There were 1567 unique articles with average dimensions of ~300 mm.
We created a hundred unique customers, and the above two thousand lines were randomly assigned, resulting in an average of 22 items per customer. We had to remove nine customers due to unsuitable data generated.
4.2.2 Simulation results
The model allocated all articles successfully for all ninety-one customers, with a maximum allocation fill rate of 81%. Below is a table describing the bin sizes generated.
Bin Length | Bin Width | Bin Height | |
Minimum | 617 | 617 | 487 |
Maximum | 1,102 | 1,119 | 1,119 |
Average | 942 | 946 | 949 |
Table 4: Bin dimensions
Below is an example of article allocation for customer number 99. On the left is the final result, and the smaller images on the right depicts the sequence of articles fitted by the model.
Figure 5: The "Tetris" item allocation model output
4.2.3 Limitation
Firstly, the model generates a unique bin for each customer. This approach would require technology that would fabricate cardboard boxes on-site and in real-time. If we tried to generalize the boxes by ranges of hundred mm, we ended up with twenty-four unique boxes. This solution demands lots of inventory for different box configurations.
Secondly, the current methods for making boxes generate waste when cutting cardboard sheets even with optimized layout configurations.
4.2 Additive Manufacturing
To tackle the multiple boxes inventory or on-site fabrication limitations of the proposed model, and to further reduce the consumption of cardboard material and the emission of GHG, this paper proposes a further step, using additive manufacturing technology for the cardboard production process.
In Austria pulping industry contributes about 7% of Carbon emissions due to high electrical energy, heating, and steam generation needed at very high pressure (Rahnama Mobarakeh, Santos Silva, et al. 2021). With the advent of 3D, will the following machinery: rollers, multistage press at 270 bars, conveyor belts, heat treatment kilns, humidifier, and trim saws from the production plant. Mechanical pulping which we are proposing uses only electricity no chemicals or steam, which the electricity consumption will be at 8.5kWh/m3 wood for debarking and chipping and conveying will be at 30.3kWh/m3 wood, however, these figures will be reduced in the future because of efficient machinery. We are proposing a 100% utilization of packaging space and automation of the packaging process. This process will reduce paper waste during pulping and since we are using additive technology, there will be no waste and we can achieve an ideal filling level of nearly 100 %. The additive manufacturing process banks up on the previous ICT process.
With this combined proposal, WAGOs’ business model will be changed, that is, the 3D printer will be a Product as a Service to WAGO, with no need for the firm to buy or invest in R&D., however, WAGO needs to buy processed pulp to feed into the 3D printer. Since we are introducing a new process that comes with its new machinery, there is going to be a reduction of manual labor due to automation, however, personnel needs to be trained for emerging technologies, e.g. data analytics, machine learning, embedded control systems, etc. These developments also come with it changes in plant layout and probably the building of new facilities. Since it is critical for WAGO to manage its resources efficiently we propose a resource management office, which will look into the water, pulp, and energy management. It is most likely that WAGO will have both lateral and vertical integration, and both closed and open-looped supply chain management to ensure continuity of production and quality. From the calculation and data, air cargo and freight transportation should be avoided, and opt for sea shipment because of its low carbon emissions.
We also propose using recycled grounded cardboard pulp rather than virgin materials, as there are no restrictions on the type of pulp used. Furthermore, we suggest implementing reverse logistics of these printed boxes by providing monetary incentives to the customers returning them. These boxes can be ground and made into pulp that can be reused to make new boxes, thus creating a closed-loop system, further reducing the environmental impact.
5 Discussions of the outcomes
The paper investigates the CO2e, costing estimates, and timber consumption, comparatively in the current state and the proposed state. The entire value chain carbon dioxide emissions are shown in the table in figure 9 below:
Table 5: Carbon Emission of the entire value chain of packaging production
5.1 Impact analysis
CO2 emission due to sourcing using trucks = QXEF/1000, where E is the emissions in tons CO2; Q is the distance travelled in tonn.km; EF is emission factors for articulated trucks in kg CO2 e tonne.km which is at 0.073 kgCO2e/tonn.km. Assuming the average distance will be 310km, we will have 310X0,073/1000 = 22.63kg CO2e/km. Two tons carried by air on a domestic flight for 1,000 kilometres within the EU = Total tonnage X Distance Travelled/km X kg CO2 per km uplift factor = 2000X1898X1.09=4138kgCO2e. 2 tons carried for 1000km around EU is = 2000X0.015 = 30kgCO2e. (Watch 2011).
Table 6: Timber Consumption of current state and proposal solution
Pulp takes 50% of harvested timber, and one tree produces 151.6boxes as shown in figure 7. Of which 1 box will be 0.743 sqm. 2577 boxes/ton/17 trees can produce 2577 boxes. 1914.8sqm of boxes are produced by 17 trees. Since we are proposing mechanical pulping which is at least 95% utility and the packaging to occupy 100% space we expect an increase in timber utility to about at least 98% utility, the 3 percent being wasted on process losses.
5.2 Benefits of implementing the proposals
Similar to the elaboration on the status quo in part 3.3, this abstract provides the outcomes of the proposed solutions for the three elements of the sustainable triple bottom line.
Emissions Energy Nexus: To produce a cardboard box of 0.743 sqm, you need 4.59 MWh/ton, therefore, by putting a 3D printer we are reducing this energy consumption process. A 3D printer using “Tetris” allocation uses about 1.5 kWh/kg (Dwamena 2021). In Germany, 0.485kg per kWh of CO2 emissions (Nowtricity 2022) this means that 20 3D printing if each 3D printer siphons 1.5 kWhr/kg of the extrusion of the microfibers. This will give a total of 30kWhr of electricity, from the Germany energy profile this will give us 14.44kg CO2e per ton (0.485kg/kWhr 30 kWhr) adding this number to the mechanical pulping process to produce kraft pulp 455 CO2kg (0.455kgCO2e*1000kg). This ultimately gives us a total of 469.55 CO2kg, looking into the traditional way of producing kraft will give 1850kg CO2e per tonne of kraft produced. Comparing the two footprints this means a reduction of 74.6% of CO2 emissions.
Figure 11: Scopes for CO2 footprint determination (Bernoville, 2022)
The above analyzed emission improvements mainly occur in scope 3, indirect emissions, like visualized in figure 11. The biggest emission driver is the downstream transportation of goods to the customers with about 82 % of the overall SCOPE 3 emissions. The future state cost on CO2 emissions from SCOPE 3 contributed from inbound, outbound logistics, and reverse logistics (on cardboard production) will be to 99%. SCOPE 2 will contribute 1% of the CO2 emissions, concerning energy consumption in the intra-logistical and cardboard production processes. This has been considerably reduced because of elimination of highly energy intensive production machineries during traditional kraft production. Through an increased filling level from WAGOs perspective on the SC, the emissions through transportation in SCOPE 2 can be decreased as well. SCOPE 1 is not considerably covered by the paper´s main topic.
Also, as proposed, the pulp consumption is reduced further using reverse logistics.
Costs: Since the proposed solution is on 100% efficient utilization of resources, during mechanical pulping, 3D printing, and automation during packaging. The following costs will greatly reduce pulp production, labor cost, water consumption, and energy cost. The current cost of hiring a 3D is $68.49/day (Academy 2022) we also expect this value to reduce since there will be more players within the emerging technology maybe by 30% by 2030. Our future pulping will have improved by 41% hence the reduction of water from 17.034 l/kg to 10l/kg. The Bureau of Labor Statistics (BLS) predicts a 10% increase in overall Transportation and Warehousing industry employment by 2030, Therefore, because of many players in the transport sector the market value will reduce due to competition, maybe by 20% from $0.85/km. Because of automation, we expect the costs of labor to reduce, however another key cost will pop-up e.g training costs, employee injuries, quality and reliability issues and cost, and repeatability issues. (Chain 2021)This will reduce labor costs by 15%. Therefore, if the current manufacturing cost is at 25%, we should expect a reduction of 10% - to 15% of labor cost. The current cost of procuring kraft pulp in Europe is $640/ton, if we look into the future, better production methods will be invented since the industry of cardboard box manufacture is expected to increase due to the advent of COVID-19, green production agenda. Less energy use because of proper energy conservation and efficiency measures will improve productivity. Hence, we expect a reduction in production to reduce by 37%. For costing parameters please see the Excel Spreadsheet on the costing of the proposed solution.
The savings for WAGO, as one component of a return on investment (ROI) calculation, can be achieved through the mentioned decrease of the labor costs (15%), the reduction of the material, energy and water consumptions in the cardboard manufacturing process (37%), the amount of capital expenditure (CAPEX) and finally the costs for transportation to the customers (depending on the final filling levels of cardboards). Those savings should be spent to cover the initial investment costs for software, machinery, and implementation in the WAGO infrastructure as well as the training of personnel.
Social: By utilizing an automated picking and packing process in the new distribution centre, mentioned in paragraph 3.2 which is based on the calculations made by the algorithm, ergonomically threads, addressed in part 3.3 can be entirely removed. In particle terms there will remain some manual processes for so called “outliers”, as not entirely all articles are packed in rectangular boxes, but anyhow the number of manual processes can be decreased tremendously.
For the implementation of the proposed processes, a specific training for the employees is needed, as they have to lear how to work safe in an automated environment. Furthermore, a maintenance team, covering all three shifts is going to be needed. This can be seen as social benefit as new workplaces will be created, but currently existing manual efforts and workplaces will be removed through the automation as well. Generally speaking, the ongoing automation can slightly decrease the number of workplaces provided by WAGO but will change the level of knowledge and specialization of the workers in the logistics department.
6 Conclusion
Although the capital expenditure investment of the proposed solution is intensive, the return on investment and utility for both facilities and materials will be very high. The proposed solution embraces the utilization of digital technologies from design using AutoCAD 360 and inventor to optimize the packaging also since a 3D printer is compatible with AutoCAD software it will be easier to simulate and optimize the proposed design without the expenditure of resources. The future is proposed based on holding every situation constant which in real practice, that seldom happens, hence, some of the things we think are cheap might not be as cheap because of changing circumstances. It is evident that the introduction of the 3D printer and automation will greatly reduce the wage bill and remove many machineries from the production process and the value chain. The Introduction of the 3D printer will reduce power consumption, pulp and timber consumption, and water consumption and consequently reduce carbon emissions. With a much greener production system, we expect WAGO to receive Green Bonds from governmental structures and international organizations like United Nations agencies and World Bank. The overall cost of our proposed solution will decrease production costs by 16.6% from the current operational costs.
7 References
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