elfware’s agile approach to implementing complex applications such as Oracle Retail is underpinned by our ability to define scope, orchestrate dependencies and map decisions using a no-code approach.
"With elfWare, we improved our IT team and their process, we can't be more satisfied. They change for better our life"
"With elfWare, we improved our IT team and their process, we can't be more satisfied. They change for better our life"
"With elfWare, we improved our IT team and their process, we can't be more satisfied. They change for better our life"
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Oracle Retail released their newest patch, Oracle Retail v19.0, in January 2020, marking the first major update to the Retail Merchandising System since v16.0, a touch over 3 years prior. The extensive update begs the question, should a retail organisation migrate to v19?
The answer will differ heavily based on your use of the prior Oracle Retail system. One needs to consider the notable additions and changes prior to making a decision. We’ve gone through and summarized a few of the more significant ones.
It’s a difficult question to answer holistically. Really, it’s going to depend on your organisation’s use of the RMS right now, and in the future.
For full documentation and more analysis of new Oracle Retail features in V19, see Trevor North’s article in the Cloud Generators blog here - http://cloudgenerators.com/oracle-retail-v19-is-out/
elfware Automation is an IT company specialising in the use of no code and low code to resolve IT Bottlenecks and reduce Total Costs of Ownership for Oracle Retail clients globally.
Coding is a notoriously laborious task, so it is no wonder that the emergence of the development concept of no-code and low-code approaches - jointly referred to as "codeless" - has numerous companies excited about the opportunities it poses for agile development. Important to a business’ decision making in this area though, is a comprehensive understanding of the differences between the two, and where they can each have the greatest positive impact.
Low Code refers to a development approach whereby an engineer can use minimal hand-coding while still delivering applications quickly and efficiently. Still not completely understanding? Here’s an analogy from another article I wrote here …
Imagine traditional coding like walking. Sure, you get where you’re going, but it’s going to take a while. Low code development is more like driving. You have a vehicle (typically an LCDP - low-code development platform), and obviously you still need an experienced driver behind the wheel who’s going to help direct the vehicle, but you’re going to get to your destination a hell of a lot quicker.
No Code is similar to low code in a lot of ways, but is really differentiated by the complete lack of coding experience required. Applications can be developed purely based on pre-existing components, meaning that development becomes a logic issue, as opposed to a coding one. This is a key factor that ensures agility within tech teams at elfware.
A No Code application is quintessentially a piece of software that writes other software.The system for no code development is similar to the use of blogging, e-commerce, and other drag and drop website builders.
For a more basic understanding of no code, we can use the analogy previously used in the low code section above. No-code development can be (like low-code development), compared to a car, in that it gets you where you need to go faster. Except this time, there is one, straight road going all the way to the destination. You'll arrive faster, but it leaves very little room for making adjustments during development. A developers adjustments are limited unless they change the original no-code application - almost like changing the road.
Forrester predicts that no-code development platform market will grow from $3.8 billion in 2017 to $21.2 billion in 2022.
As we’ve just established, the key difference is how much hand-coding is required between the two concepts. The differences certainly don’t stop there, and a team needs to consider many of these distinctions before deciding to use either no code or low code;
Where better to conduct our case study than within our own doors? At elfware, we’re an IT Automation company that uses both low-code and no-code in providing Retail Automation solutions to our clients. On a daily basis, we need to decide which one is more applicable to a project in progress.
Internally, for each of these development principles, we create a mapping template, which is later adjusted based on logical decisions suited to the project.
The main differentiation point within elfware for these two concepts is that where no-code is fully automated in carrying out its role, low-code rather provides a greater degree of control to an analyst due to opportunities to insert code that can be replicated throughout the system, or it provides a series of ‘macros’ (macroinstructions) that specify a task that an analyst can assign to different areas of the template.
A case study like ours’ shows that a company is capable of utilizing the benefits of both no-code and low-code, significantly reducing requirements for hand-coding and resulting in a logic focused IT team, as opposed to one that’s focused on manual development.
A now heavily discussed topic with regard to innovation and technology, big data has become a focus for some of the world’s largest retailers, proving to be a viable solution to margin pressures and ever-increasing retail competition.
Big data is a relatively broad phrase to describe large data sets that are too large, fast or complex to be processed using traditional methods. But the phrase has come to be synonymous with the field of systematically processing and analyzing the information to reveal patterns and trends, particularly relating to human behaviour.
What is important is what organizations can do with this data, optimizing for cost efficiency and revenue increases in efforts to increase margins.
While the phrase ‘big data’ was coined in the early 2000’s by industry analyst Doug Laney – who attributed to big data three main attributes, volume, velocity and variety – the field has really hit its stride in conjunction with advancements in machine learning.
For the retail industry specifically, Big Data has the capacity to track consumer trends in order to attract new customers and, arguably more importantly, increase the lifetime value of existing customers through targeted offers and loyalty programs.
However, critical to a retailers ability to utilise the benefits is “a robust and methodical way of collecting, managing and interpreting data, then linking that insight to the overarching business strategy,” according to KPMG. With this capacity, a retailer has numerous opportunities.
The extensive analysis of a large consumer dataset allows a retailer to predict what a customer is likely to purchase next. A retailer can study a consumer’s previous purchases, purchases of similar customer segments, how they interact with online stores/apps/social media accounts, and even significant other factors like the weather. Using this data, they can personalise recommendations and advertising to ensure the greatest rate of return on marketing expenditure possible.
Brands like Walgreens and Pantene in the United States partnered with the Weather Channel to anticipate weather patterns and shift marketing strategies accordingly. Seeing a period of high humidity coming, Walgreens began to market anti-frizz products more heavily to women due to the increased demand during these conditions, resulting in a 4% increase in hair product sales over a 2 month period.
Similarly, with regard to the point about studying purchases of similar customer segments, Amazon attributes 29% of sales to their recommendation engine. The engine studies the data sets of 150 million customers to recommend products to customers throughout their website.
In a similar manner to targeted recommendations and offers, a retailer can study data extensively to optimize stock variety and the availability/presentation of stock between outlets.
At the most basic level, a retailer can study data down to the day of the week to determine their allocation of stock. By optimizing stock allocation, the retailer can simultaneously avoid lost revenue as a result of a lack of in-demand products and reduce the costs associated with holding stock, such as storage costs (utilities, warehousing etc.) and shrinkage.
Walmart, the world’s largest brick and mortar retailer by both revenue and number of stores, is investing heavily in what will be the world’s largest private cloud system, capable of managing 2.5 petabytes of data an hour. One of the major focuses of the Arkansas based analytics hub analyzing and optimizing stock levels.
The analysis and response to consumer trends has proved to be a lucrative option for industries globally. A study of the car industry and Tesla’s rapid growth as a result of a growing number of drivers turning to eco-friendly options, provides ample proof of this fact.
The issue then is the reactive stance retailers have often taken; adapting to trends too late and missing out on revenue opportunities. Big Data provides a retailer with the capacity to be proactive with regard to trends. Studying large datasets gathered from social media, forums and existing customers, can reveal growing trends before they really accelerate, unlocking vast revenue opportunities.
The heavily publicized Dollar Shave Club capitalized on the consumer’s increased engagement with subscription boxes for items they would otherwise have to purchase regularly. Combined with a remarkably effective satirical marketing approach, the company has managed to acquire 3.2 million subscribers, including 12,000 customers in its first 24 hours. In 2016 the company was acquired by Unilever for an estimated $1 Billion USD.
The field of Big Data is still relatively new to the retail sector; but is becoming exponentially more important. The revenue and cost reduction potential of Big Data is growing exponentially, and numerous retailers and startups are running extensive experiments in hopes of utilizing Big Data for new benefits. This is what we can expect to see from Big Data in the future.
Retailers have made significant inroads to the study of unstructured data such as that which comes from social media; however, there is still a way to go.
Natural Language Processing (NLP), is a branch of artificial intelligence with the objective of understanding how humans communicate; from reading to understanding. It becomes remarkably difficult for computers to understand human communication due to numerous abstract tendencies humans undertake in order to pass information. One such issue is sarcasm.
A comprehensive understanding of the human language involves connecting the words spoken/written and associated concepts.
As major developments are made in the area of artificial intelligence, computers may be able to use syntax (how a sentence is structured), and/or semantics (the meaning conveyed by a text), to process natural language.
This will result in a significant improvement to the capacity for computers to make sense of unstructured data, as previously mentioned, providing more accurate datasets from information-rich environments like social media, linking closely to trend analysis and targeted recommendations.
One of the most important benefits of the study of large data sets is predictive analytics. Retailers of all sizes already do this – with or without the help of computers – when conducting general tasks like financial planning or stock purchasing. Predictive analytics links directly to the benefits previously established.
However, the future of Big Data holds large potential for predictive analytics to improve its accuracy, predominantly due to the impact of machine learning. The nature of effective machine learning algorithms dictates that they will only become more influential in retail planning, particularly in areas like merchandising. Greater accuracy in predictions will result in more effective optimization for revenue and cost reductions.
Similar to the use of Machine Learning and Predictive Analytics, in store customer identification has the capacity to enhance how data can be used to drive revenue. Retailers can combine targeted offers and advertising in stores with customer identification to drive in store sales.
The highly publicized Amazon Go concept has drawn attention toward in-store customer identification; using body mapping technology to track dozens of customers and recognising what a customer picks off the shelf and purchases.
If retailers can link this customer identification with existing customer databases they can link online and offline activities, they can increase average customer spend; similar to Amazon’s remarkably successful recommendation engine.
McKinsey, in their report 'Big data: The next frontier for innovation, competition, and productivity,' found that, "a retailer using big data to the full could increase its operating margin by more than 60 percent". Therefore, while it is important to consider that Big Data may have limits in terms of its effectiveness, on its current trajectory, it will have numerous benefits in the near future; from highly targeted offers/advertisements to predictive analytics.
Retailers who capitalize on Big Data may be able to stay ahead of competitors, fighting off ever increasing margin pressure by lowering costs and improving revenue. Big Data may soon not become an option for retailers, but rather a necessity.
Retail margin pressure is ever-mounting due to increasing competition, the meteoric rise of e-commerce, and rising wage demands. Thus, the most successful retailers of the decade have evidently looked to stay ahead by improving customer experience and internal efficiency. Just look at Amazon Go.
Where many retailers have jumped on the self-service checkout trend, Amazon leapt ahead of the automation game with their brick and mortar play; a convenience store that uses computer vision, deep learning algorithms and sensor fusion to completely remove the need for a checkout system, which was rather replaced by the Amazon Go app.
While numerous kinks need to be ironed out, the stores prove the possibility of the complete removal of staff from a brick and mortar store with the help of automation. Combining this with electronic shelf labels, self-checkout terminals, shelf-scanning robots, and partially automated backroom unloading, a physical store can reduce costs by 55%-65% according to McKinsey.
In store automation is on the rise, but will likely soon take a back seat to merchandising automation.
“The winners of the sector will be those who understand [the implications of automation] and act quickly to address them” (McKinsey)
McKinsey suggests, “automatable activities account for approximately 30 to 40 percent of the time of merchants”. Their extensive report lists numerous opportune areas through which retailers can access the benefits of merchandising automation.
The merchandiser’s role differs between organisations, but for the most part involves the holistic management of product appearance and supply in pursuit of long term sales increases.
Merchandise planning, accounting for roughly 20% of a merchandiser’s time, can be optimised through the automation of extensive historical analytics used to create accurate predictive scenarios that empower faster and better decision making for merchandisers.
In addition, predictive impact analytics have the capacity to significantly increase revenue through targeted and personalized promotions. An Oracle Retail spokesperson explains they have, “seen more than a $75 million increase in revenue at a medium-sized retailer turning over between 500 million and a billion dollars, just by optimizing targeted offers at the point of pickup”. Oracle Retail is often touted as a leading enterprise resource planning solution for realizing the benefits of automation. Other areas within merchandising that strive to widen margins and enhance sales include macro space optimization, inventory allocation and invoice matching.
It's difficult to estimate the value automation will have in merchandising specifically as of yet, but if anything is a surety, it’s that retailers are beginning to realise the possible benefits of merchandising automation, and it is those companies that appear to be forging a new age of retail.
Oracle Retail is widely regarded as the best ERP suite for the retail sector, but why is that and what solutions do they offer? You'll find Oracle consistently ranked very highly across their retail offerings, and the best in a number.
And there is good reasoning as to why.
At elfware we specialise in Oracle Retail, and particularly in resolving related implementation, delivery and maintenance challenges through low code and automation. Having had such exposure to Oracle Retail, we thought it would be helpful to put together a basic summary of what Oracle Retail is.
As the largest business software vendor globally, Oracle offers a huge library of products, including database services, platforms, software, hardware and more.
Oracle Retail is a division of Oracle focused on providing specialised retail application capabilities. You may have also heard the arm referred to as the RGBU - or Retail Global Business Unit. At the time of writing Oracle Retail claim over 5,000 retail clients.
One of the major reasons that people get confused as to what exactly Oracle Retail does is because of the sheer number of offerings that come under its umbrella. These products are split up into a series of categories, each fulfilling a different purpose - merchandising, insights and science, omni-channel, planning and optimisation, supply chain and hardware.
They boast software application modules to support pretty much any process that retail organisations require to run their company effectively. Predominantly, the software is used to enhance the company’s capacity to manage stock, reduce waste, improve customer experience and, importantly; to increase revenue and profit.
As has just been discussed, the fact that Oracle Retail has such a large offering of fully integrated products is a big drawcard.
They cover just about everything retail-related there, and they have a whole suite of integrated products that more generally cater to enterprise wide process support. This links into the next benefit…
Retailers don’t have to worry about cross-party integration, given that one suite of products can cater to just about every aspect of the business.
All aspects of the business’ retail function are therefore inextricably linked, allowing for a more agile and streamlined operation. This can lead to reduced costs and, more importantly…
Oracle Retail has focused heavily on improving the capacity for retailers to cater to an omnichannel, connected, and highly personalised customer experience. They use data and automation to ensure that customers feel valued by the business and have an experience that fits their archetype, resulting in incremental revenue and profit increases.
An Oracle employee explains that they’ve, “seen more than a $75 million increase in revenue at a medium-sized retailer turning over between 500 million and a billion dollars, just by optimising targeted offers at the point of pickup,” merely one aspect of the targeted customer experience.
According to a recent article by SuperOffice (https://www.superoffice.com/blog/customer-experience-statistics/), 86% of customers will pay more for a better experience, and keeping an existing customer happy is 14 times more profitable than landing a new customer.
A lot of the benefits of Oracle Retail spur from its data-driven approach. Recommendations from the Oracle Retail software are based off millions of data points used to gradually optimise and improve a business’ operations.
“Retailers all know they need advanced analytics and retail science to drive their business forward, but they don’t all have the luxury of hiring on a data science team,” said Marc Koehler, solution director, Oracle Retail, “We continue to enhance Oracle Retail Insights and Science Suite to provide retailers with packaged insights and science applications”.
Oracle has made it very clear in recent years that they want to lead the innovation front, and have done so with regard to AI and Machine Learning in retail solutions. Their AI solutions can be used in conjunction with Oracle Retail to improve processes.
However, Oracle emphasises AI and Machine Learning are not a completely effective solution alone, and require concerted focus and cultural change; “most machine learning projects fall short on delivering tangible business benefits. Not because the innovation is misaligned with a business objective but because it is difficult to operationalise innovation,” said Jeff Warren, vice president, Oracle Retail,“the Retail Science Platform delivers the standardisation and controls that enterprises need to accelerate their new offerings and swiftly integrate them into their business workflows. With the addition of notebook-based tools, our solution is a force to be reckoned with in predictive analytics and machine learning for the retail industry.”
So we’ve established that there are numerous benefits to Oracle Retail, but it’s important to be pragmatic about potential challenges as well, challenges it shares with other organisations. Here are some comments which have been made:
Now, this point is very dependent on how you’re using Oracle Retail.
Oracle Retail can be expensive, particularly for small to medium retailers. But, many large enterprises see extensive revenue and profitability benefits that by far outweigh the cost of Oracle Retail. It's also important to consider that this isn't an Oracle Retail specific issue; rather one that applies to all Enterprise Resource Planning products.
Whilst licensing and support costs must be considered as an input cost, the primary impacts to the Total Cost of Ownership (TCO) are from the initial implementation - see this discussed below - and from the on-going run costs from teams who lack experience in managing high performance Oracle retail sites. At elfware we specialise in delivering and maintaining high performance Oracle Retail installations. We reduce Total Cost of Ownership through automation, making Oracle Retail a value buy for all retailers, irrespective of size.
Oracle Retail is far from simple and requires extensive technical expertise to use. This can result in extra costs or a lack of valuable input from Oracle Retail products.
Typically, retailers will look to bring on full-time Oracle Retail administrators, and external technical teams for more complex projects.
Moving between systems entails any number of issues.
A retailer may need to go through extensive retraining of staff that will result in short term productivity shortfalls, but typically the cost comes in the movement of data from legacy systems to Oracle Retail.
At elfware, a project we’re currently working on with a large UK Based retailer moving to Oracle Retail, involves integrating and validating existing, unstructured data into Oracle Retail to ensure the long term success of their new system.
I don’t want to go extensively into Oracle Retail case studies.
But, we’re an IT Automation company that specialises in Oracle Retail services and resolving the issues involved, so we just want you to understand how retailers have got around some of Oracle Retail’s challenges and what happened to the business as a result.
KOJ is certainly a success story for Oracle Retail.
A Dubai based retail conglomerate, KOJ operates more than 700 stores between 7 countries.
Oracle listed a few statistics in a success story profile of the company, citing that KOJ had seen 99.9% accuracy in real-time inventory data for stock online, and 98.5% for in-store.
Statistics like these have prompted numerous global retailers to utilise the Oracle Retail product suite, including Louis Vuitton, Walmart, Prada, Cape Union Mart and Myer.
Note that we have split up the product offerings into the categories listed by Oracle Retail. At the time of writing, each of the below categories have multiple product offerings under their umbrella.
Oracle Retail emphasises that in modern retail, great products aren't enough. Rather, retailers need to anticipate and adapt based on advanced analytics to optimise and personalise their offers.
Thus, Oracle's insights and science product suite focuses on using analytics to drive customer engagement and loyalty.
Arguably Oracle Retail's most significant offering is there Retail Merchandising System. Merchandising solutions like Oracle's provide an organisation with the means to accurately monitor, and thus control, the success of their retail business.
The system has the capacity to reduce the cost and time required for day-to-day activities - from the maintenance of stock ledgers, to new product introductions, to automated replenishment and much more.
Oracle Retail emphasises that the benefits of their merchandising system are; operational support, scalability, agility over numerous verticals, and data integrity.
Omnichannel services focus on orchestrating activities across a retailer to improve customer experience. It provides a customer with a unified experience across numerous channels and touch points.
Oracle Retail emphasises that data-led decision making using their platform can drive conversions and foster brand loyalty. The integration of their various products provides a retailer with the capacity to personalize the shopping experience.
Oracle's Retail platform combines advanced retail analytics, artificial intelligence and machine learning to plan, and gradually optimise a retailer's operations. This includes use of space, category optimisation, financial planning, pricing and more. Gradually, a retailer can optimise to increase revenue and reduce waste.
Oracle's modern system for Supply Chain Management is designed to reduce inventory requirements and waste while meeting supply requirements to maintain or improve revenue. Again they utilise embedded AI to assist in decision making and claim the capability to reduce inventory requirements by 30%.
In 2014, Oracle acquired MICROS systems and inherited their hardware products, an important factor in their omni-channel approach.
Their point of sale systems allow Oracle Retail to cover the complete customer experience. Oracle focuses on simplicity, flexibility and durability in their systems, that integrate with their other services to offer an end-to-end optimised customer experience.
Films and the media like to paint a picture of the committed software engineer who slaves away on a computer till the early hours of the morning in efforts to develop systems. For a long time, that’s what it took to complete projects; talented and committed developers with an extensive understanding of math, digital logic and programming languages. While this is often still true, a sort of saving grace has emerged in the form of the low code movement.
Software systems are becoming more complicated, requiring more and more complex development. As a result, numerous engineers and organisations are considering the benefits of low code. But few of us really understand what it is.
That’s probably because companies will try to overcomplicate the concept, telling you things like: “low code is a development movement where time-consuming manual processes are automated, without hand-coding, using a visual IDE environment, an automation that connects to backends and some kind of application lifestyle management system” *
Really, low code just refers to any case in which an engineer uses a system to generate code, meaning that they don’t have to do it all manually, low-ering the amount of hand-coding required.
They say Rome wasn’t built in a day, but what if software could be?
Imagine traditional coding like walking. Sure, you get where you’re going, but it’s going to take a while.
Low code development is more like driving. If you have a vehicle (typically an LCDP — low-code development platform), and an experienced driver behind the wheel directing the vehicle, you’re going to get to your destination a hell of a lot quicker.
So, low code automates the tasks that developers would typically find complicated, time-consuming… or straight-up irritating. A developer narrows the scope of their work and can focus on higher-value logic-based issues, as opposed to technical ones.
Developers access these benefits through low code development platforms, but no two low code tools are ever really alike. Typically they have some sort of visual aid, a development environment and automatically handle backend services.
Some low code development platforms are available to the public, such as Salesforce Lightning, or KiSSFLOW, while others are used for in house development.
elfware Automation uses a custom low code platform called elfCafé, developed in house. Our engineers are constantly adapting the platform to meet the services our customers require, typically relating to Retail ERP, data integration/validation, automated testing and more.
Short answer; we don’t know.
In some scenarios, like at elfware, low code has meant clients can get substantial results without large investments of time and money. With a specific retail focus, we’ve been able to validate typically years’ worth of data in months and run millions of scenarios in hours. Just take the example of a US Specialty Retailer whose data we managed to prototype into Oracle Retail v16 from Oracle Retail v11 in just 2 weeks. But, some industries haven’t been able to capitalise on its benefits as of yet.
Many companies are still using Agile and DevOps to fuel faster development, with varying degrees of success. But more and more are pushing toward integrating low code development into their technical model, including Shell Downstream, Harvard, MIT, ING Bank, PricewaterhouseCoopers, Yahoo and more.
This rapidly growing demand has led to Forrester Research estimating a 40% compounding annual market growth for low code development platforms, leading to a market value of $21.2 Billion by 2022.
Will it put engineers out of work? Probably not. If anything, low code is an aid that will make engineers more productive and more agile, making them capable of producing more output at greater efficiency.
elfware Automation is an IT Company specializing in retail automation solutions. We have headquarters in Sydney and London, with clients from across the globe. If you’d like to learn more, visit www.elfware.com or contact us.
This article contains an explanation from elfware CEO Hamish Cameron of various Oracle Retail cutover strategies, previously an answer to a question in a Q&A. In particular, it focuses on the different features of a phased cutover versus a ‘Big Bang’ cutover, as well as offering methods to reduce project costs and ensure cutover success.
Hamish is concerned with understanding and communicating the transition risk landscape and agreeing a combination of cutover approach and mitigation activities which appropriately balance risk and cost for an organisation.
Q: "We are implementing Oracle Retail, replacing an in-house legacy application suite in which retail applications such as Merchandising, Inventory Management, Pricing, Allocations, Assortment planning etc are tightly coupled to core Master Data tables.
We are steering away from big bang, but can you advise on what approaches we could take?"
“The biggest two questions to consider when cutting over are: how agile is your current legacy and integration landscape and how big is the appetite for mitigation?
Typically I believe phasing a cutover actually adds more risk, as it requires significantly extra work and the costs escalate and/or the quality processes going in to each state then reduce.
Phasing an implementation reduces cutover risk but has other big impacts I'll refer to later.
In particular doing anything apart from Big Bang requires a significant amount of:
So you need time, capability, organisational commitment, Oracle Retail and Legacy architectural expertise and budget to make it feasible to mitigate by phasing your implementation or your cutover.
If you have plenty of time then you can plan upfront and execute each state as a sequentially separate project over a timeframe - it becomes a significant Programme Executive undertaking to persuade business stakeholders why a long running project with a large price tag is unlikely to deliver benefits in a typical retail timespan.
If you don't have plenty of time, then you will need to execute in parallel ... and that's where the fun really begins with people designing, building and testing for multiple states in parallel across multiple environments in order to be ready to execute the cutover sequentially.
With that said I've been involved with different approaches over the years, for example:
This is the most advisable approach where possible. It really comes down to ensuring all processes in each system are covered in the relevant transition state and that interface commissioning/decommissioning is well planned and executed.
Works where the divisions have largely separate legacy systems with low levels of overlap, so probably not feasible for you. If you try this by splitting out from a single integrated application I suspect you will open a ‘Pandora’s box ’of modifications and testing. Normally the issues will come in POS, sales transaction auditing and reporting, but depending upon your current legacy systems, it may also impact purchasing, invoice matching (and/or accounts payable), inventory, transfers/allocations, pricing/promotions, e-commerce, planning and finance ... you name it.
This really has similar characteristics to decoupling by Division/Merchandise Group, however usually purchasing, invoice matching, transfers and allocations are your key pain points.
e.g. Item and Master Data followed by pricing/promotions, purchasing etc in some order and then the transactional layer.
This is the most common approach taken. It absolutely works but I'd refer you back to points 1. to 5. above - it is very expensive and has fundamentally undermined projects at a number of retailers causing the implementations to be paused and not achieving objectives.
Automating all aspects of integration testing, validation, reconciliation and transition allows it to be tested within an inch of its life through transition and a month plus of realistic data a multitude of times.
Execute an automated parallel run where data is fed into RMS (Retail Merchandising System) via a combination of data migration scripts and the integration layer from legacy with isolated processes double keyed or enriched in the RMS (Retail Merchandising System).
This can be significant effort and can be fraught with complexities in the mappings and integrity between legacy and RMS, typically resulting in quarantining which requires legacy, mapping or RMS correction to release. That can be an overhead and result in reconciliation difficulties due to timing etc. So as the parallel run continues more data will typically get out of whack.
The most appropriate implementation mitigation approach depends upon a number of factors, not all of which are covered in this brief overview.
Absent very good reasons for doing otherwise (such as disparate application and integration landscapes) generally I would recommend approach 5. Simulate transition to production and production runs a multitude of times as the automation tools are readily available to implement this strategy.
To offer a greater level of risk mitigation this can be done in combination with 6. Parallel Run, but it does require a greater appetite for such mitigation as it will take both additional time and budget.
Hopefully Hamish's discussion helps you to understand a little more about the transition options for an Oracle Retail Implementation. If you have further questions, feel free to contact him on LinkedIn or send him an email.
Oracle Retail Implementation and automation related IT services are elfware’s forte. Using our implementation experience in combination with the elfCafè platform and approaches, our team will optimise your implementation, application management and resolutions for your most complicated bottlenecks, as we have for similar organisations from all over the globe.