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JP Doggett

Output from a member discussion hosted on 22nd April by Aleem Bandali of o9 Solutions:

What does next-gen omnichannel retailing look like?

There are two main parts:

  1. The customer-facing aspect where the customer must feel that they are engaging with a single, seamless entity. A lot of work has gone into this aspect already;
  2. The back end infrastructure which, if anything, is more important as it should keep the brand's promise and this is where a lot of work still needs to be done. The key is not thinking about supply chain in isolation but everything - planning, commercial, finance - all need to be connected in real time so that, at any point in time, it is possible to quickly answer questions like, 'can I support this increase in demand?', for example. This means forecasts and allocations need to be determined channel by channel, not just on an overarching basis.

What is the technology that underpins this seamless information flow?

It can be described as a 'digital brain' that senses all parts of the organisation and, like a brain, uses machine learning to continuously improve its understanding of the environment in which it is operating. It is also referred to as a digital twin which is based on graph and cube modelling of every node and edge of your supply chain network to create a precise digital carbon copy. Using business rules based predominantly on large volumes of transactional data, machine learning detects patterns and so can alert when deviations from what is planned or implied by the business rules occur. 

This also underpins a control tower where dashboards of end-to-end operations identify where there are risks of disruption, often using real-time IoT data. It is possible to drill down into very granular levels to understand precisely which shipments are at risk and which SKUs, DCs and stores will be impacted. It is then possible to model potential solutions to the disruption with an understanding of operational and financial implications.

How machine learning can improve forecasting accuracy across channels with leading demand indicators

  • Forecast accuracy: allowing for different tolerances and categories, the typical scenario has been for 10-20% of SKUs to fall outside the forecast accuracy tolerance limit. This rate often doubled during the pandemic, generally not including availability-driven deviations;
  • Machine learning impact: across a wider sample of retail businesses who have implemented machine learning, the typical change has been from 40-60% accuracy using traditional methods to 70-90% accuracy with machine learning used to better understand drivers and lead demand indicators;
  • Determining drivers
    • a common approach would be to take up to 200 potential drivers from 1000+ databases and overlay this with historical data so that the machine learning algorithms detect which drivers are most significant;
    • to predict in-store demand as lockdowns ease, for example, high frequency data like Google Map data of traffic to retail outlets  by date, time and postcode can be overlaid with other external data like the number of Covid cases by postcode, the level of restrictions and other data to build predictive models at granular and aggregate levels. This showed key differences between countries and regions where people responded slightly differently to their local conditions but enough to have a material impact on demand;
    • for longer lead-time scenarios of 10+ weeks, for example, it may be that a different combinations of drivers and data points give better predictive power;
  • Eliminating blind spots: critically, this approach reduces the reliance on tacit knowledge and guesswork: around 70% of drivers were already identified and being used but around 30% were different or given a different weighting in the predictive models which contributed to the improvement in forecast accuracy;
  • Inside the ‘black box’: for businesses that have their own data science teams, the open source platform means that proprietary algorithms can be combined with the out-of-the-box algorithms to further improve accuracy. It is also important to ensure the ML platform highlights the contribution of each different driver, turning the black box of AI transparent to increase trust in the output, in turn increasing adoption.


End-to-end omnichannel planning with a singular integrated view on merchandising and supply chain across all channels

  • Channel shift: hard to know for sure how much of the pandemic-driven shift will stick but confident that it does constitute a paradigm shift and so prompts quite fundamental questions about the purpose of a store, where to locate and how to optimise inventory across the network for robust available-to-promise implications for cost-to-serve and so on;
  • Integrated views & closing gaps: an accurate forecast is only half the battle...you also have to be able to execute. This is where a digital twin (a digital representation of each and every node in a supply chain, including external suppliers) comes into play because it is then possible to model scenarios and impacts across all channels and understand what needs to happen on execution to close the gap to plan and forecast;
  • Stores as micro fulfilment centres: a digital twin approach also helps to move past the distribution network more flexibly so that bricks and mortar stores can become fulfilment centres as conditions demand;
  • What went wrong?: there can be a gap on understanding lost sales if, for example, they’re compensated for from other parts of the forecast and business but a digital twin allows for ‘intelligent post game analytics’ to understand what went wrong, where, and why;

Best practices for scaling operations automation over time

  • Enterprise buy-in: efforts to improve forecast accuracy and close the gap between plan and execution will be hampered if functions like finance, commercial and supply chain each have their own takes on what is and should be happening. A platform approach with a digital twin at the centre that is able to evaluate multiple drivers from data streams allows teams to express and test their views of the world to aid and improve mutual understanding and decision making;
  • Crawl, walk, run: best practice is to start small, for example, by pulling in a few different drivers or testing the explanatory power of existing drivers on a group of categories. You can then move onto testing more drivers for more sophisticated models and then get into fully automating the processes so that less time is spent on mundane tasks and more on value-add initiatives.
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