Smart Business Models in Intralogistics |
A service-oriented approach to customized logistics solutions
| Journal | Industry 4.0 Science |
| Issue | Volume 41, Edition 4, Pages 30-35 |
| Bibliography | Share | Cite | Download |
Abstract
Keywords
Article
Equipment-as-a-Service (EaaS) is a unique product-service system in which the customer no longer purchases the equipment but only pays for its use or the service provided. Only by exploiting the potentials of digital change and the transformation to Industry 4.0 can usage-based business models be implemented efficiently. Industrial trucks such as forklifts and order pickers are equipped with sensors that transmit usage data to billing systems via cloud technologies. Processing via distributed ledger technology and smart contracts is another option, which enables machine usage data to be recorded and billed in real time [2].
In the future, programmable payments could help to fully automate the billing process for flexible machine usage, right through to payment receipt while simultaneously enabling efficient processing of bills for low usage, known as micropayments [3].
In order to successfully implement and scale an EaaS business model, it is an essential prerequisite that the equipment is technically integrated into existing systems via the Internet of Things (IoT) [4]. At Jungheinrich, for example, customers can use the Power Buy The Hour pay-per-use model [5]. They can rent a machine for up to 12 months and receive a monthly usage-based invoice. The machines are equipped with a telematics box that stores the operating hours in a data cloud every day. From there, the data is then regularly transferred to the ERP system. This enables a monthly invoice to be issued and sent to the customer based on exact usage via an automatic billing process.
In the future, even more sophisticated models could be possible. For example, a sharing model could be used at wholesale markets or larger logistics centers to enable usage- and user-based billing via micropayments. Users could use the machines for just a few hours or even minutes and receive a bill on the same day. In this case, the machine would have to receive a signal, for example via an access system, that a specific user has activated the device. The exact usage time could then be determined via the login and logout time stamps and this could in turn be processed via the above-mentioned billing process.
Necessary changes to the business model
In addition to the technical requirements, the development of usage-based business models for logistics equipment providers requires further adjustments from the previous transactional model: EaaS requires a higher level of service orientation on the part of the logistics provider, as the equipment is no longer sold to customers as part of a one-time transaction. Instead, an ongoing customer relationship is developed; The provider transforms into a service provider who understands individual customer needs and guarantees the availability of the object or of a specific service [6].
In addition, risk allocation changes. While the sale of a machine to the customer transferred the risk of business interruption, residual value risk, and utilization risk to the customer, these risks remain with the service provider in usage-based models [7]. Business interruptions can be covered by insurance solutions, but the risk of underutilization is generally not insurable. In selecting the right revenue model for his services, the service provider can either assume the risk entirely on his own, as is the case with pay-per-use models or share the utilization risk with his customers within the framework of subscription models [8].
This can take the form of a minimum purchase quantity of operating hours or a fixed provision fee. Short-term rentals also offer customers a high degree of flexibility while securing predictable revenues for the manufacturer. The residual value risk is manageable for logistics companies that have their own sales companies, as wear and tear and repair costs can be monitored effectively via the IoT connection, enabling prices to be calculated for the sale of used machines that reflect their wear and tear and sales to be organized via the sales company.
However, a much greater challenge is convincing customers of the benefits of a service-oriented business model, as they are accustomed to the transactional sale or purchase of machines over many years [9]. Other challenges include ensuring data security and covering the high initial investments in digital infrastructure. In addition, scaling the business model is more difficult than with software-as-a-service, as physical goods must be produced for EaaS. To finance the model as it scales, usage-based financing solutions should therefore be considered [10].
Advantages for providers and users
Service-oriented business models offer numerous advantages for customers: They use the machines and systems instead of buying them, paying only for the duration of use or the output generated. That way, they can concentrate more on their core business, resulting in significant customer benefits. The service provider takes care of the maintenance and servicing of the machines and guarantees their usability. In addition, machines can be returned if they are not used, which lowers the amount of unused tied-up capital. This shifts risk from the customer to the provider.
Since this flexibility comes with higher costs, usage-based billing models are particularly attractive for machines and systems that are not intended for continuous year-round use but rather for difficult-to-plan, irregular, or seasonal use. By assuming the utilization risk, the provider can in turn achieve a high utilization rate by renting out its machines to different customers at different times. This means that it can generate even more revenue than with traditional leasing.
At the same time, such sharing solutions enable sustainable (multiple) use and thus conserve resources. The service provider achieves greater customer loyalty through the “use instead of buy” business model, as he is in regular contact with customers and can offer additional services such as data analysis, predictive maintenance, delivery of operating materials, and fleet management, which are often tailored to individual customer needs and further increase customer benefit.
Potential use cases
The advantages of service-oriented business models are further explained below using various fictitious use cases involving industrial trucks (forklifts, order pickers). The calculations are based on unpublished real data from Jungheinrich AG, specifically on the price lists of various foreign companies within the European Union, all valid at the time of writing.
To give customers a greater incentive to use the service for longer periods, the service provider can offer discounts for longer usage periods. The average rate for a one-month short-term rental of highly in-demand equipment is €940, while the rate for three months is €750 per month. To compare use cases with a year-round lease, a lease rate of €651 per month was assumed. The smaller monthly price is due, among other things, to the higher assumed usage for shorter-term rentals (specific demand) and the lower utilization risk for longer rentals.
Use case 1: Customer with high basic utilization and seasonal peaks
Customer A operates a fleet with high and consistent basic utilization but has seasonal peaks, for example during the Christmas season. He wants to concentrate his capital commitment on the core business. For this reason, he wants a mixture of long-term leasing, short-term rental, and pay-per-use that is optimized for his specific case and continuously updated according to usage data from telematics. Until now, he has financed their ten devices via a long-term lease with a monthly rate of €651 per device on average.
As only six devices are used to full capacity all year round, customer A would like to rent two devices on a short-term lease for three months at a monthly price of €750, to cover the additional Christmas business. Transport costs of €276 (including delivery and collection) are added on top. He would like to use two additional devices for 12 months on a pay-per-use basis to remain flexible in view of the difficult-to-predict business development in the coming year. This means that he pays less for the devices in the event of weaker business development without being unable to meet logistical requirements in the event of good development. He realistically assumes that the pay-per-use machines will be used for 600 hours per year.
For the provider, this means less expected wear and tear and therefore lower maintenance costs when compared to leasing. In addition, the expected residual value for a potential later resale as a used machine increases. As a result, the customer pays a lower monthly rate of €591.
This includes a fixed monthly fee of €200 to cover fixed costs and an hourly rate of €8. Billing takes place monthly based on actual usage.
Figure 1 illustrates the savings potential of flexible use compared to pure leasing in a mixed fleet: While the customer had to calculate €78,120 per year when leasing all ten devices, the flexible arrangements introduced in this example, involving short-term rentals and pay-per-use solutions, would cost €66,324, thus resulting in potential savings of €11,796 (approximately 15%).
Use case 2: Event management customer
Customer B is active in event management and needs five devices three times a year for one month.
Until now, the devices have been kept in the company on a permanent lease (annual costs €39,060). The customer switches to a more flexible model and starts renting the devices on a short-term basis for one month. The provider charges a monthly rate of €940 and transport costs of €276 per device. The potential savingsin this scenario amount to €20,820 (approximately 53%), as shown in Figure 2.
Use case 3: Sharing model in a wholesale market
Customer C organizes logistics in a wholesale market, operating an in-house fleet of equipment. The equipment is used interchangeably by different market players within the wholesale market. Customers only need a forklift truck on an ad hoc basis. Due to the volatile usage requirements, the provider must factor a risk premium into the pricing. For the calculation, the expected annual turnover for a classic individual lease is used as a comparative value and adapted to a conservatively calculated target utilization of the equipment at this location. In the example, this corresponds to 500 hours per year instead of 1,000 hours usually estimated for a lease.
This means that, even with lower usage, the provider is on an equal footing with leasing providers and can cover the risk of underutilization and potentially higher damage risks due to user changes. The calculation results in an hourly rate of €16 for each individual customer. This is then multiplied by the actual usage to arrive at the total cost. Each customer receives individualized device access, which allows the machine to recognize which customer is currently using it.
By processing the telematics data, usage can then be allocated to the various users according to operating hours, so that billing can take place on a usage-based and customer-specific basis. The calculation was initially based on one device and can be scaled to any number of devices. In this case, to allow for comparison with long-term leasing, a pay-per-use model with no basic fee was calculated. Charging a monthly subscription fee per customer would also be possible to further reduce the risk for the service provider.
If the expected 500 hours of use are divided among several customers, this results in a significant price advantage. The potential savings for individual customers with, for example, 100 hours per year (approximately two hours per week) amount to approximately 80%, or €6,212, with total costs of only €1,600. The hourly rates were rounded up in the calculation.
If the pay-per-use model is better received than expected, for example if new customers who had previously leased or purchased forklifts register for pay-per-use, there is significant scaling potential. If the provider manages to increase the use of the equipment to 600 hours per year, for example, the revenue would already amount to €9,600 or €800 on average per month (+23%). It is important to emphasize that pay-per-use models are highly customized and depend on the location conditions, costs, margin expectations, and the provider’s openness to risk.
Prospects for intralogistics providers
This article has highlighted and described some of the advantages of usage-based business models. If providers manage to implement such business models successfully, further advantages are likely to follow. For example, having a larger fleet of rental machines in use can be helpful for proactively managing machine renewal and thus actively controlling plant utilization or the stock of available used machines. The comprehensive implementation of telematics-based fleet management solutions can further increase customer benefits and thus loyalty via customization.
However, to benefit from these advantages, many providers will have to change their traditional, predominantly transactional business models and incentive systems. In addition to the purely technical and procedural implementation of usage-based models, cultural change is also necessary. If this is successful, strategic realignment will generate attractive competitive advantages. Companies can thereby become pioneers in their industry. In doing so, they must not be guided by the short-term optimization of key figures but must keep their eyes on long-term perspectives, thinking in terms of customer solutions before the customers themselves even develop the need.
Bibliography
[1] Kett, H.; Evcenko, D.; Falkner, J.: Equipment-as-a-Service (EaaS): Canvas zur Entwicklung von EaaS-Geschäftsmodellen. Diskussionspapier des Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO, Fraunhofer Verlag, Stuttgart 2023.[2] Wiebusch, A.: Resiliente Produktion durch Pay-Per-Use Modelle – Mit Pay-Per-Use und Asset-as-a-Service zu mehr Flexibilität in einem unsicheren Geschäftsumfeld. In: Industrie 4.0 Management 4 (2023), pp. 55–58.
[3] Forster, M.; Groß, J.; Kamping, A.; Katilmis, S.; Reichel, M.; et al.: Der Zahlungsverkehr der Zukunft: Programmierbare Zahlungen im Bereich IoT. White paper by the Frankfurt School Blockchain Center, PPI, DEA, and CashOnLedger.
[4] Kett, H.; Evcenko, D.; Falkner, J.: Equipment-as-a-Service (EaaS) – Necessary Changes for Service-Based Business Models. In: Human Systems Engineering and Design 112 (2023), pp. 491-500.
[5] URL: https://www.jungheinrich.ch/produkte/mietstapler/power-buy-the-hour-1150048
[6] Stojkovski, I.; Achleitner, A.; Lange, T.: Equipment as a Service: The Transition Toward Usage-Based Business Models. SSRN Electronic Journal 2021.
[7] Rüsberg, L.: IoT-based Finance – mit Daten Risiken managen. In FLF 69 (2022) 3, pp. 127-133.
[8] Gassmann, O.; Frankenberger, K.; Choudury, M.: Geschäftsmodelle entwickeln. Der Business Model Navigator, 3rd edition. Munich 2021.
[9] Wiebusch, A.; Wilkowski, N.: Wie EaaS die Intralogistik verändert. In: Logistik heute 6 (2023), pp. 34-35.
[10] Wiebusch, A.: Finanzierungskonzepte für Equipment-as-a-Service und Pay-Per-Use-Geschäftsmodelle. In: Zeitschrift für das gesamte Kreditwesen, 76 (2023) 7, pp. 28-33.
Potentials: Business Models Services
You might also be interested in
