Cross-case analysis of emergent food business operators
| First-order observations (emergent platform food business operators) | Second-order themes (emerging constructs) | |||
|---|---|---|---|---|
| Case #4 | Case #5 | Case #6 | Case #7 | |
| Framework element 1: Platformisation | ||||
| •Inclusion of third-party platforms due to disruption-driven business model shift •Increased network effects leading to rapid service expansion to five more postcode areas (over four months) | •Business model shift from physical-first event catering to digital-first food fulfilment •>60% increase (June 2020) in user interactions via existing and newly introduced digital channels | •Disruption-driven business model shift from single-source, physical-first event organising to multi-sourced digital-first food/service provision | •Bidirectional food supply chain interactions with the disintermediation of upstream actors •Elevated platform-consumer communication, propagating a more seamless consumer-platform experience •Reduced procurement order time | Business model evolution •Disintermediated business models •Digitalised platform-based supply •Streamlined order management |
| •Order system integration for improved food-order convenience and reduced lead and fulfilment times •Challenges with increased feedback functionality resulting in delayed responses | •Greater and more accessible product/service personalisation •Expanded online presence and marketing via third-party-platforms to offer equal online presence where previously unsuccessful | •Integration of analytical capabilities to manage orders efficiently due to higher quality data •Contactless delivery and collection services •Flexible omnichannel communication whereby value-addition occurred two-fold, i.e. via improved consumer experience and closed feedback loop | •Rapid service expansion leading to a more significant online presence and nationwide partnerships with third parties (i.e. cafes and eateries) •Integrated digital customer and order data management software | Technological integration •Improved accessibility to data-driven functionality •Multi-party integration and ordering policy |
| Framework element 2: Structural variety | ||||
| •Expansion-driven collaboration with new delivery partners offering a range of temperature-sensitive products (i.e. cold chain) •Temporary reduced workforce availability (due to coronavirus disease) •Collaboration with third-party food business operators leading to additional product availability •Short supply of baking ingredients (i.e. flour and yeast) during March–April 2020, leading to investigation of alternative supply sources and expanding the procurement of high-demand items | •Limitations in facilitating an in-situ workforce due to COVID-19 pandemic-imposed restrictions and low asset ownership •Streamlining of product offerings occurred •Product differentiation and added user functionality increased demand for personalised items and menu collaborations | •Diversified from limited finger foods to providing a menu of >12 main meals and party bundles •Product offerings expansion, including the additional sale of packaged goods •Flexible product offerings (e.g. fruit and vegetables) •Maximised accessibility and reduced negative externalities (i.e. air miles) •Critical service-based offerings could be substituted due to disruption-related government guidelines (i.e. social distancing) | •Increased menu (six additional main dishes), service offerings (i.e. delivery and collection options), and expansion to four commercial establishments •Workforce diversification with using additional digital marketing expertise •Personalised goods (customer-facing functionality development) Introduction of “cook at home” range due to critical ingredient shortages (reduced menu due to stock visibility issue) | Product variety and product substitution •Demand-driven streamlining of product offerings •Diversification through product bundling/aggregation •Limitations in facilitating an in situ workforce and low asset ownership •Product differentiation and added user functionality •Reduced order turnover (operations cycle) and consumer experience |
| •Highly interchangeable suppliers and greater product variety (baked goods, confectionaries) •Service local expansion (i.e. additional service availability such as expansion to South and North London) | •Localised supply and reduced lead times, providing greater product quality, durability, consumer satisfaction and overall efficiency (i.e. time and cost) | •Following initial A/B testing and three trial products, ascertained initial demand trends using the data-rich platform •Addressing specific product personalisation and preferences | •Order turnover time reduced from >7 days in some cases to <3 days •Process variability via supplier localising and an operating model switch to order pre-booking | Resource substitution and scalability •Structural efficiency through reduced complexity (i.e. localised supply) Interchangeable supply base |
| •Mobilising local actors | •Mobilising local actors | •Enabling leaner processes | •Enabling leaner processes | Reduced supply chain complexity •Mobilising local actors |
| Framework element 3: Process flexibility | ||||
| •Gradual increase in short-term supplier contracts, as well as an increase in more non-conventional suppliers •Acquisition of +4 short-term contracts through collaboration with local brick-and-mortar food providers (during March–June 2020) | •Dynamic/increasing product offerings – expanding variety and choice for consumers •Demand-based direct-sourcing from suppliers disintermediating >3 previously used retailers | •Increased sourcing from local farms and producers •Partnership with a third-party supplier offering additional confectionaries and decorations | •Incurred small financial losses due to a lack of backup sourcing for a best-selling pre-cooked product, leading to three days of order cancellations due to a lack of stock visibility (consumer-food business operators) | Dual/backup sourcing (pricing and contracting) •Ability to adapt product portfolio based on supply-demand fluctuations •Processes to enable short-term supplier and workforce contracts |
| •Shared logistics with partners (i.e. confectionary suppliers) Improved e-commerce functionality concerning platform-user communication and user-platform interaction •Planned logistics development and partnerships (with two large logistics providers) | •Use of geo-netting tools for improved operational efficiency based on planning deliveries to locations that the business can serve •Team expansion (e-commerce expertise) | •User-feedback-based service change •Provision of an “A-Z party bundle”, including an extended menu of food and non-food items such as party bags •Team expansion (food preparation, delivery and e-commerce expertise) | •Real-time orders and delivery mapping leading to increased time flexibility associated with preparing and planning menu alterations (based on supply availability) well in advance •Team expansion (e-commerce expertise) | Coordination •Real-time order analysis by geography •Continuous feedback (e.g. product/promo evaluation) •E-commerce and logistics development |
| Framework element 4: System resource efficiency | ||||
| •Real-time order acquisition •Minimal inventory/deadstock held onsite •Implemented bulk discounts on •leftovers/unsold goods •Lack of purpose-built application | •Frequent overbooking due to a lack of purpose-built technology | •Efficient order-fulfilment process through waste-minimisation (zero food waste operation) facilitated by omnichannel ordering and pre-ordering | •Implemented weekly pre-booked orders, allowing the accurate estimation of the food to order/make | Informing (educating and framing waste) •Real-time order acquisition, omnichannel ordering and pre-ordering |
| •Linking with several local charities and community support initiatives, namely, the Hardship Fund, Gratitude and •Gift – different organisations which work with local families in need | •Reduced waste (i.e. packaging) through the elimination of intermediaries | •Split operations between two sites •Investment in human resources (i.e. digital marketing expertise, chef and two logistics partners) | •Distribution to people experiencing homelessness •Minimised waste given that demand could be predicted with greater accuracy before technology usage | Mobilising •Elimination of dead stock through charitable or promotional means and intermediaries •Higher accuracy and reduced time for complete order-fulfilment |
| •Investment into cold-chain facilities to cater to a larger customer base and product offering (i.e. chilled meats) | •Data-enabled supply targeting (location-based product offerings and improved digital marketing efficiency) •In total, 7% reduction in overhead costs over five weeks (i.e. rent and advertising costs) | •Customer-based growth (i.e. closed feedback loop) •Reduced costs of operation (overhead costs) •Weekly savings of up to £50 due to weekly waste reductions (packaged food/beverages and other consumables) | •Reduced food wastage (e.g. ingredients) due to a more seamless order-fulfilment process •Demand monitoring leading to the elimination of dead stock through promotional means | Measuring •Near-zero inventory model (fresh produce, maximum of one day; frozen produce, maximum of three days) •Investment into the cold chain •Improved demand monitoring leading to the elimination of dead stock •Data-enabled supply targeting |
| First-order observations (emergent platform food business operators) | Second-order themes | |||
|---|---|---|---|---|
| Case #4 | Case #5 | Case #6 | Case #7 | |
| •Inclusion of third-party platforms due to disruption-driven business model shift | •Business model shift from physical-first event catering to digital-first food fulfilment | •Disruption-driven business model shift from single-source, physical-first event organising to multi-sourced digital-first food/service provision | •Bidirectional food supply chain interactions with the disintermediation of upstream actors | |
| •Order system integration for improved food-order convenience and reduced lead and fulfilment times | •Greater and more accessible product/service personalisation | •Integration of analytical capabilities to manage orders efficiently due to higher quality data | •Rapid service expansion leading to a more significant online presence and nationwide partnerships with third parties (i.e. cafes and eateries) | |
| •Expansion-driven collaboration with new delivery partners offering a range of temperature-sensitive products (i.e. cold chain) | •Limitations in facilitating an in-situ workforce due to COVID-19 pandemic-imposed restrictions and low asset ownership | •Diversified from limited finger foods to providing a menu of >12 main meals and party bundles | •Increased menu (six additional main dishes), service offerings (i.e. delivery and collection options), and expansion to four commercial establishments | |
| •Highly interchangeable suppliers and greater product variety (baked goods, confectionaries) | •Localised supply and reduced lead times, providing greater product quality, durability, consumer satisfaction and overall efficiency (i.e. time and cost) | •Following initial A/B testing and three trial products, ascertained initial demand trends using the data-rich platform | •Order turnover time reduced from >7 days in some cases to <3 days | |
| •Mobilising local actors | •Mobilising local actors | •Enabling leaner processes | •Enabling leaner processes | |
| •Gradual increase in short-term supplier contracts, as well as an increase in more non-conventional suppliers | •Dynamic/increasing product offerings – expanding variety and choice for consumers | •Increased sourcing from local farms and producers | •Incurred small financial losses due to a lack of backup sourcing for a best-selling pre-cooked product, leading to three days of order cancellations due to a lack of stock visibility (consumer-food business operators) | |
| •Shared logistics with partners (i.e. confectionary suppliers) | •Use of geo-netting tools for improved operational efficiency based on planning deliveries to locations that the business can serve | •User-feedback-based service change | •Real-time orders and delivery mapping leading to increased time flexibility associated with preparing and planning menu alterations (based on supply availability) well in advance | |
| •Real-time order acquisition | •Frequent overbooking due to a lack of purpose-built technology | •Efficient order-fulfilment process through waste-minimisation (zero food waste operation) facilitated by omnichannel ordering and pre-ordering | •Implemented weekly pre-booked orders, allowing the accurate estimation of the food to order/make | |
| •Linking with several local charities and community support initiatives, namely, the Hardship Fund, Gratitude and •Gift – different organisations which work with local families in need | •Reduced waste (i.e. packaging) through the elimination of intermediaries | •Split operations between two sites | •Distribution to people experiencing homelessness | |
| •Investment into cold-chain facilities to cater to a larger customer base and product offering (i.e. chilled meats) | •Data-enabled supply targeting (location-based product offerings and improved digital marketing efficiency) | •Customer-based growth (i.e. closed feedback loop) | •Reduced food wastage (e.g. ingredients) due to a more seamless order-fulfilment process | |