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The Team – The Company is comprised of a seasoned team of developers and designers covering the technology spectrum of SaaS, its execution platforms, databases, development languages, and AI and ML adoptions focused on optimizing workforce management, the most immediate problem in the grocery retail sector. The team skills have been applied to create AmpliTask Workforce Optimization (AWO), the most advanced workforce solution for supermarkets. The functional specifications of AWO have been provided by the founder, Les McNeill, who has decades of sector experience understanding the challenges of retailers, while also engaging with various consultancies serving those retailers, taking cognizance of their inputs specific to the impacts on store performance and solution priorities. The launch of AWO is the outcome of more than a year of dedicated development by the team, with the specific objective of applying optimized labor utilization as the catalyst enabling the local stores to function as managed warehouses functioning as profitable omnichannel components of a supply chain where stores are embedded within the chain, and supply execution is the assured fulfilment of purchases by both retail and digital shoppers. As a team, we look forward to the opportunity to solve the previously unsolvable problem of excessive store labor costs.
The Company founder, Les McNeill, has dedicated a career applying information technologies to resolve previously unresolved business problems. Here are just a few highlights.
creating iClarity Solutions and the AmpliTask solution has been the design and development focus of the past 13 months utilizing a team of developers who have had prior history with the founder and a depth of experience in evolving retail specific solutions. The team is comprised of the founder and personnel with skills arranging from subject matter expertise in the sector, executive management, and development capabilities focused across Cloud platform and multiple database technologies, Java applications development, Python applications development that specifically addresses the AI and ML functionality within AmpliTask, and an ongoing cooperation with a leading retail consultancy providing implementation and change management resources.
Ongoing research since 2011 has exposed the impacts of shelf out-of-stocks and overstocks contributing to losses equivalent to 7.5% of total store sales, accounting for worldwide retail losses >$1.7T in the 12 months through mid-2022. Reversing these losses will lift the average retailer’s operating profit by more than 300%; an outcome critical to the survival of the supermarket chains’ local store networks struggling to compete in an increasingly omnichannel sales environment. It is this outcome iClarity delivers.
Way before Java, McNeill led a team in the development of a Cobol environment comprised of a compiler delivering a pseudo code, accompanied by a easily deployed virtual machine (VM) able to interpret the pseudo code in execution wherever the VM virtual machine was installed. This was a precursor to the model later applied for Java and its JVM, but in this case, delivering a multiuse, interactive, Cobol exploiting its CVM to deliver portability of applications across any operating system and hardware combination executing the CVM. Additionally, the Cobol had the ability to manage retrieval and manipulation of data wherever, and in whatever data model, that data might be retained across the network. This Cobol was adopted by IBM for the 4381 mainframes and by Olivetti for its S6000 minicomputers that were a key technology component of the aborted merger between AT&T and Olivetti.
Way before Java, McNeill led a team in the development of a Cobol environment comprised of a compiler delivering a pseudo code, accompanied by a easily deployed virtual machine (VM) able to interpret the pseudo code in execution wherever the VM virtual machine was installed. This was a precursor to the model later applied for Java and its JVM, but in this case, delivering a multiuse, interactive, Cobol exploiting its CVM to deliver portability of applications across any operating system and hardware combination executing the CVM. Additionally, the Cobol had the ability to manage retrieval and manipulation of data wherever, and in whatever data model, that data might be retained across the network. This Cobol was adopted by IBM for the 4381 mainframes and by Olivetti for its S6000 minicomputers that were a key technology component of the aborted merger between AT&T and Olivetti.
Following on from the interactive Cobol, McNeill designed a 4GL capable of converting natural language text defining and generating the Cobol code for specific business functions. Functions that once generated, created both technical and user documentation while taking advantage the Cobol portability of deployment across differing execution platforms. The objective being the acceleration of business solutions.
With Multics that morphed into Unix, the industry saw the emergence of a standard operating system able to be installed on a range of hardware platforms from large systems to PCs. Around the same time, we saw the beginnings of standardized coding languages, with C and then Java, improving the potential execution of portable applications able to be deployed over a standardized operating system. Unfortunately, Unix did not have a file system in its infancy, and McNeill worked on a project introducing Unix support for datafile and database management capabilities essential to the delivery of enterprise-class business solutions.
As focus shifted to the Cloud, the ability to federate ‘live’ legacy applications’ intelligence and expose this for SaaS execution, was imperative. SaaS solutions must be able to access, extract, normalize and load real-time live legacy data feeds from every application silo, regardless of application, data model or type, and expose this live data for SaaS execution. This is a critical requirement where live real-time data is to be applied to AI and ML functions enabling advanced business process executions, a capability heavily exploited in the current solutions developments.
ranging from flat files through to relational databases. Its immediate use was the delivery of content to printed in-aisle promotional messaging able to exploit the research evidencing 70% of buying decisions are made while in-store. Of immediate use being promotional cross-selling and/or up-selling to a larger quantity package of the same product, with particular emphasis on markdown messaging applied to the clearances of overstocks. This ETL capability exploited the store wifi network to access legacy system content and expose the extracted data on electronic shelf edge labels (ESELs) and in-aisle color displays, respectively enabling dynamic pricing and more effective store-specific promotions. The management of these ESEL and displays was then developed as a solution in conjunction with shelf inventory management. There was an emerging recognition this ETL federation of real, or near real, time content from legacy applications exposed the opportunity to innovate a new generation of retail operations solutions where the ETL platform might actually be applied to delivering the implementation builds.
With retail store inventory management mired in the continued use of perpetual inventory that ‘may’ know the total inventory carry of a product within a store, but doesn’t know where that inventory is, or in what item-level counts wherever it is stored, there is no avenue for workforce optimization. Determining whether inventory is on-shelf or in backstock requires a costly ‘visual’ check by store personnel. McNeill designed and led the development of the first location-centric inventory management that knows the addressable space for every product wherever the product is stored, and the accurate count of the products in each storage space. He has added dynamic addressing of backstock to provide >25% more usable storage within the existing backstock storage. The latest advancement being the warehousing capability creating just-in-time supply. These breakthroughs enable more advanced workforce management able to transform the stores into functioning warehouses supporting omnichannel purchases.
This most recent innovation is AmpliTask Workforce Optimization, (AWO), the most advanced ‘store’ workforce management capability within the grocery retail sector. All labor tasks are system initiated, and all tasks are optimized to eliminate all redundant inventory movements, enable volume processing, and do so with the least labor when compared with existing perpetual inventory management (PIM) outcomes. AWO lowers inventory movement labor costs >40%, lifting overall retained margins and delivering store omnichannel profitability at a time when store networks struggle to compete with the pure-play eCommerce providers.
The objective is to halve the existing store labor costs associated with the movement of inventory, from receipt into store from DCs and suppliers, movements from backstock to shelf availability for purchase fulfilments, whether for in-store or online shoppers. Lowering labor costs creates a higher blended retained margin across both retail and digital channels enabling the online (digital) activity to be profitable. By assuring continuous on-shelf-availability, the labor utilization transforms the accuracy of the store Point-of-Sale (PoS) transactions as a guide to true store demand. With this in place, the store can expose forward demand planning intelligence to enable supply to balance with sell-through within product delivery cycles; the ultimate framework for eliminating overstock and waste in fresh and perishables.
