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48 CLICKS - An anthology of three short stories: BOUNDARIES, OBSESSIONS CONTINUED and 48 CLICKS! 48 CLICKS - The sound of a click could have many meanings. A click of a camera, you get a picture that tells a story. A click of your computer's.
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- Serp Rank’s Clicks Magazine – Issue 48
- Why YouTube Click-Through Rate Is Important
- Authentication
- Wayne State College Athletics
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The most crucial factor is the up- or down-stream position of the break-open point which defines where online orders are being picked. A downstream break-open point allows operating with lower capabilities in online fulfillment, whereas an upstream position needs high process proficiencies. Setting the break-open point downstream in the supply chain means picking all or most online orders at the store. Instore picking does in general not require complex logistics systems. For last-mile delivery, vehicles and customers are allocated to a store.
This simplifies routing, but results in less synergies in last-mile transportation capacities. Setting the break-open point upstream in the supply chain requires online DCs and therefore significant investments into structures and capabilities, especially in the picking processes for customer units.
Second, the development of logistical capabilities is influenced by the organizational structure. For example, decentralized organizations like cooperatives have more and higher hurdles to cross when implementing centralized online solutions. Regional differences in product variety, affinity of customers and cultural differences e. French customers prefer pick up while British customers prefer home delivery limit a direct transfer of a successful fulfillment concept between markets.
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Another consideration in online grocery retailing is trust in safe handling of products is important. Most OC grocers therefore deliver their products with an own fleet instead of using logistics service providers. As a forth contingency factor store sizes and replenishment frequencies impact the logistical capabilities. In Types 1. This is different in Type 3 , where small stores and customers are supplied in customer units, which allows a combined picking and transportation. Also, the possibility of joint forecasting of online and store orders influences online logistics capabilities and costs.
This can only be operated efficiently if dedicated online DCs are implemented.
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Online order volume: the shift from bricks-and-mortar to online sales forces retailers to reconfigure their networks. The online share depends on geographical location urban vs rural , density and size of stores within the region as well as competition intensity. Furthermore, specific customer requirements for certain product categories have to be considered e. Customer satisfaction with the OC setup, availability and efficiency of different delivery services like pick-up, tight time windows and a high-delivery velocity influence online volume as well as customers willingness to pay for these services.
Figure 8 shows at an aggregated level the network patterns, its dependencies and its developments. Overall, the online order volume drives the network development, and with an increased willingness of customers to buy online, investments into online fulfillment capabilities are made. In a first phase, mainly bricks-and-mortar structures are used for the fulfillment of all channels.
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For the final customer delivery or pick-up, product flows are kept together as long as possible see network configuration 1. In a second phase, channels are separated by opening an online DCs for the fulfillment of this particular channel see network configuration 2. In a third phase, when online volume accounts for a significant share of sales, retailers reconsider merging online and bricks-and-mortar logistics. Certain stores e. The development from Phase 1 to Phase 3 goes along with increasing logistics capabilities and thus efficiencies.
While a general development from Phase 1 to Phase 3 can be observed, this does not necessarily mean that all OC grocery retailers develop in any case from 1 to 3 or pass through all phases in their development cycle. In this section, the main findings are discussed in the light of literature in three areas. First, differences in OC grocery and OC non-food logistics are highlighted.
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Second, the interrelation of different subsystems is discussed e. And third, we discuss the patterns and development options identified. The patterns and development options identified are as follows: We identified six types of logistics networks for OC grocery retailing.
Significant differences between OC grocery logistics networks and OC non-food logistics networks exist. The implication for theory is that characteristics of OC non-food networks can only be applied to OC grocery networks to a very limited degree. While an online grocery order consists of more than 60 different items on average, an average online shopping basket in non-food retailing e.
Additionally, the larger assortment sizes in grocery stores result in larger warehouses, larger picking distances and ultimately in higher picking costs in grocery than in non-food. Hence, picking and the associated processes, number of stages and locations play a much more decisive role in the entire grocery supply chain. A higher number of decentralized warehouses are therefore necessary in OC grocery logistics compared to more centralized DCs in non-food retailing. Non-food orders are exclusively delivered from logistics service providers, while former pure bricks-and-mortar grocers build up their own fleet for last-mile home delivery.
Moreover, in grocery existing bricks-and-mortar logistics structures are partially used for online fulfillment but the ultimate goal for grocery retailers is not — as for non-food retailers — to have one common warehouse with one stock for bricks-and-mortar and online grocery items. This can be the case in a specific retail setup with only small stores, but is not the norm. Logistics is only integrated across channels up to the break-open point. However, existing OC literature sees the integration of both channels as the ultimate goal for retailers e.
Fisher, ; Verhoef et al. This is seen to be necessary in terms of market presence and customer interfaces e. Brynjolfsson et al.
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However, existing literature does not differentiate between grocery and non-food logistics, and neglects grocery specifics such as temperature zones, picking complexity, shorter lead times, etc. What is referred to as OC logistics is in fact mostly OC non-food logistics. As an implication for practice, we identified that a more differentiated picture of logistics integration — identifying the particularities of OC grocery logistics and channel integration — is required.
We show that grocery characteristics prevent the direct transfer of non-food structures to grocery retail logistics networks. Grocers are encouraged to review the different network configurations and to select the most appropriate one for their setting. All subsystems in the supply chain areas play an important role in the network design, costs and service levels.
However, current literature has predominantly analyzed the subsystems individually. An implication for theory from our empirical findings is that warehousing, picking, internal transportation and last-mile delivery are interrelated in OC retailing and therefore need to be analyzed together.
On the last mile, a higher number of shipment locations i. This can also have a positive effect on the transportation costs due to bulk deliveries with larger vehicles to supply these shipment locations instead of direct deliveries of customers from remote warehouses with smaller vehicles. However, if shipped from many decentral locations, there are fewer options to bundle transportation capacities for the last mile and hence lower economies of scale e. The same holds true for inventory efficiency. Decentralized inventories with multi-stage picking processes, low picking volume in each location or less efficient picking in the store drive picking and inventory holding costs.
When comparing deliveries from store with delivery from an online DC, the benefits of fulfillment from stores i. To overcome the high last-mile delivery costs, some retailers only offer pick-up of online orders. However, this results in high investment costs in solo pick-up stations. The resulting multiple inventory locations i. The total travel distance and transportation costs are also dependent on where central and regional DCs, online DCs and stores are set up, which also influences delivery speed and potential frequency.
It is not sufficient to just consider the last-mile costs. A further implication for practice is that a total cost perspective for OC grocery retailers is essential in evaluating their OC logistics without focusing on one aspect of their supply chain i. Retailers can base their network choice on the analyzed dis- advantages of the various configurations. This will help to get a supply chain-wide view on OC structures.
With this in mind, we also show that logistics networks are not only planned based on logistics parameters such as cost savings, transportation distances or operations synergies see, e. Teo and Shu, ; Piotrowicz and Cuthbertson, , but also by considering product, customer and market characteristics see, e.
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Rao et al. The implication for theory in OC grocery retail logistics is the identification of different network configurations, influenced by various contingency factors, as well as the evolutionary process from one network configuration to another. In an evolutionary process the fulfillment of online orders via stores network configuration 1 is an entry model see, e.
Hays et al. In most cases separation processes start as online order volume grows. Online DCs are then used for at least parts of the online fulfillment after online volumes exceed a certain level network configuration 2.