Operations Management within Sharing Economic Industries

Elham Taghizadeh
Wayne State University

Nearly 40% of customers worldwide have purchased products or requested services through firms such as Doordash, Uber, Amazon situated in the sharing economy. In operation management, the sharing economy is a business model built around the on-demand case to product and service facilitated on an online platform such as ride-hailing platforms that find a driver for individual riders. It can also be considered the business model that provides products or services to many buyers by a single entity, for example, vehicle sharing service, which makes available vehicle rentals to individual users [1].

During the COVID-19 pandemic, tremendous growth has been seen in the food delivery industry and online ordering, attracting leadership attention. The new movements in traditional operation and supply chain models have been seen among decision-makers in sharing economy industries involving: 1) technology, 2) transaction type, 3) platform type, 4) governance model, 5) business approach, and 6) shared resources [2].

1. Transmitting from old to new perspectives

In the sharing economy, every business model needs to apply the new approaches for modeling on three central pillars of OM theory: inventory theory, revenue management, and queuing theory. In the following, I will describe differences in the new perspective of each theory:

1-1 Inventory Theory

In classical inventory theory, the main decisions are around how much needs to be ordered and when. However, in the sharing economy, effective matching between supply and demand is considered a primary priority because sometimes supply cannot be controlled at a short time scale [1]. For instance, in the food delivery industry, the process in which drivers become available on the platform is mainly independent of customers’ arrival process. Therefore, the final decisions are less about orchestrating the supply and finding the best solution to match each unit of demand with each available supply unit [3]. In operation research, the dynamic optimization formulation can be employed to formulate the problem. In the dynamic modeling, the platform can earn a reward as a function of quality matches reported the number of supply units left unmatched (like excess inventory in the classical inventory problem) and the number of unsatisfied customers (shortage in inventory problem). A few articles are discussing using dynamic optimization techniques in sharing economic areas. For instance, Zipkin (2008) minimizes the number of lost sales by providing a new modeling inventory [4]. Chen et al. (2014, 2019) consider the dynamic optimization to reformulate the perishable inventory model for the food industry. They demonstrate how economy sharing needs to readjust their models by considering reward structure to reduce the number backlogs and increase their profits [5,6]. Baccara et al. (2020) demonstrate the vital role of trading off between centralized vs. decentralized matching in the online labor market such as uber and Lyft to minimize the lost demand [7]. Techniques and insights developed for the old concept (classical inventory management) can transfer to the new perspective (economic sharing). All sharing economic business managers and researchers are expected to use many inventory techniques and computational methods to move to a new platform [8].

1-2 Revenue Management

Classical revenue management focused on how prices should be adjusted to sell a fixed amount of capacity on the finite time horizon. The process should be dynamically modified based on available capacity, which means less capacity can be translated to a higher price [9]. However, economic sharing has embraced classical revenue management’s basic idea, which needs to be considered that capacity is crowdsourced and extremely sensitive to the paid wages [10]. Overall, the two-sided dynamic pricing (wages paid to workers and prices charged to the customer) can be considered the best approach for sharing the economy and spatially distributing the nature of supply and demand. In the Doordash or uber platform, higher wages in one location draw supply away from other locations and low demand in one location and force supply to search elsewhere. There are some examples in academic applications; for instance, Zhang et al. (2018) discussed the role of value co-creation in the sharing economy, especially when customers want to pay an affordable value to receive the ontime service, but the providers face capacity limitations. They present how pricing adjustment can help to satisfy customer demand with limited capacity [11]. Another example is Zipcar, which could be competitive with other rental car companies by providing an efficient pricing method to attract customers [12]. Zipcar makes the cars available for customers in public parking lots, and customers could reserve the car for short or long trim at an affordable price [12]. Therefore, based on these businesses’ complex behavior, new evolution is needed to apply operation research techniques and methodologies to optimize the current economic sharing models.

1-3 Queuing theory

Queuing theory is a mathematical formulation to examine the congestion and delays of waiting in line, and it studies every component of waiting in line and needs to be served, such as arrival process, service process, number of capacities, and customers [3]. Queuing theory is a natural tool for sharing economy applications because of its on-demand access to products and services[3]. The queuing theory needs to be extended to employ in sharing economy businesses in some directions: 1. Sharing economy relies on when and how much workers decide to work, therefore a multi-server queueing system considering that the number of servers is not fixed and can be varied over time. 2. A double-ended queue modeling can be suitable because arrival customer and servers’ functions are independent. The server (workers) will not necessarily return to the original location after service completion. For instance, Benjaafar et al. (2019) provide a multi-server queuing system to study product sharing in a food industry case study [13]. Kim (2019) applies a bulk input G/M/1 type queuing model with stochastic congruent properties to find the optimal solution for a car rental company[14]. It needs to be modeled very dynamically over time-of-service providers to be available or not. Operation Research provides an acceptable solution to the model, formulation and solves this type of problem.

2. The role of ORMS to overcome challenges in the sharing economy

These business models face various challenges, and ORMS techniques and methods can be practical solutions. One challenge that needs to be discussed is the cannibalization effect, which can curtail ownership because any individual can rent instead of the owner. ORMs techniques can help decision-makers find a suitable solution regarding how a platform should price its products and under what conditions a platform would benefit from resource and product sharing. For instance, Doordash Inc starts to benefit from operation research and machine learning to identify optimal prices for services and products. The first challenge can come up with managing available resources and leveraging capacity. Compared to the traditional service system, capacity can be controlled in a different way which is indirectly by wages and prices. In these businesses such as Doordash and Uber, capacity is related to independent workers’ decisions (meaning drivers in Doordash and Uber) because they will decide when, how much, and where to work and accept the demand and wages. Operation research algorithms show that the optimal price can be a trade-off between market size and labor pool size or the waiting cost [1]. Resource allocation and price management are challenging parts of economic sharing that can be handled by implementing the operation research concept in optimization formulation and algorithms. Gan (2019) proposes a novel distribution logistic network considering Big Data analysis to optimize resource allocation by investigating customer preference. The author assumes that all logistic partners follow their policy, and the idle logistic resources can be recognized and assigned to distribution needs. The author provides an objective function model based on high speed, low cost, and low pollution for different logistic preferences and uses the heuristic algorithm to solve the model [15,16]. Kim et al. (2015) introduce three resource allocation methods for a sharing economy industry by minimizing transaction costs and the level of idle resources. They use game theory to solve this complex problem and find more realistic solutions [17].

Another challenge that can be discussed is matching with minimum cost and high trust between customer and ownership, how the sharing economy platform can match customer demand and supply with max satisfaction and minimum cost. Most platforms benefit from the recommendation algorithm and machine learning to match the best supplier (for instance, for Doordash best restaurant) to customer demand. Operation research opens a new perspective in this area by introducing a complex business model. In the sharing economy, much information is available to help decision-makers optimize the decisions and strategies if they implement the appropriate algorithms and tools with high security [18].

How can we deal with Uncertainty (Demand and Capacity) We are living in a significantly fluctuating environment, and uncertainties are surrounding the businesses and personal life. One example is COVID 19 pandemic, in which everything has been changed. All customers were forced to switch to the online platform from using restaurant services to regular shopping tasks. Online platforms such as Doordash, Grubhub, and amazon were faced with vast and unpredictable demand with a lack of resources (workers). So, they started to adjust the pricing management to attract more suppliers (resources) because some other workers lost their jobs who never thought about working at the online platform. So, these businesses benefited from these opportunities and tried to attract more resources by offering acceptable prices. On the other side, restaurant and shopping center owners who never collaborated with the online platforms, and now pandemic gave them the chance to move to the new online platform [19,20]. For instance, Doordash introduced the Main Street Strong plan for restaurant owners to attract them to join its online platform. The main Street Strong plan aimed the restaurant owners to build their brands, launching various programs to attract customers, and handling customer reviews to boost their qualities. This platform can be helpful for businesses because they could easily adjust their traditional approach based on customer demand and move to online platforms. However, this platform still needs to be optimized by employing operation research methods for providing the best solutions in revenue management and resource allocation. Still, business owners are not satisfied with available resources, and they lost their customers because of the lack of resource sharing provided by doordash. Pandemic verifies that uncertainty can impact all sides of different businesses, and using operation research to optimize the best strategies with minimum cost and maximum revenue, all businesses can eliminate the consequence of uncertainties [21].



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