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Disrupted supply chain: how to manage volatility and execution in challenging environments

    

Integrated planning
JP Doggett

A dial in discussion with a small group of senior leaders who play a strategic role in the S&OP / IBP process. We'll share ideas around best practice, but also experience around how things have best been adapted or transformed to cope with volatility.

 AGENDA 

Disrupted supply chain: how to manage volatility and execution in challenging environments

  • Understand how to manage supply planning with scenarios, capacity, capability, and buffers
  • Discover the key aspects of the supply execution process
  • Recognise how to embed supply execution learnings into the medium-term planning process for sustained improvement of planning quality

 WHO FOR? 

Industry sectors: current practitioners from all sectors

Org. size (annual T/O): medium to large, typically £500m+

Roles & remits: Heads of:  Supply Chain, Logistics, Planning, S&OP / IBP with responsibility for demand and supply management

 ABOUT INTENT DISCUSSIONS 

  • All discussions are private, held under the Chatham House Rule and moderated by INTENT with approx. 6-8 participants for 45-90 mins of candid, interactive discussion (not a passive webinar)
  • Some discussions include subject matter experts from member-recommended INTENT Partners, others are exchanges of best practice, experiences and ideas among practitioner members only
  • Discussions are shaped by participants according to their interests and questions
  • We may adjust participation to avoid competitive sensitivities and ensure productive discussion

 WHEN? 

Thursday 29th April (11.00 BST / 12.00 CEST) for max. 90 minutes

Hosted by Intent

Expert guests: Dawn Dent and Lucy Jacobs of Oliver Wight.

 

Request to join

 

Interested but can't make the date? Email us and we'll update you about future discussions.

 

  • Also...

    • JP Doggett
      By 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:
      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; 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.
    • JP Doggett
      By JP Doggett
      Although there continue to be many challenges in arriving at a robust plan in the first place, this discussion focused on how to close the gap with execution or, in other words, reduce time lag or latency of deviations from plan being predicted and detected to being successfully managed.
      Latency definition: how quickly can you predict something is going to happen? How quickly can it be filtered in the hierarchy? It’s fundamental to how the SC and finance are organised. Latency is a combination of data availability and process

       
      Latency -1 (predictive)
      Scenario planning is a key way to decrease latency and even predict demand.  Demand Sensing is another key part of the solution: in retail this can be social sensing. Social sensing gives you a picture what’s happening amongst consumers that is about to affect demand. In a manufacturing context this could be about introducing sensors to monitor real time consumption. Or in manufacturing, it can be about getting information on supply chain disruption, eg issues at the ports.  Latency +1 (responsive)
      The pandemic has pushed businesses towards short term decision making. But it’s the mid term (S&OP) monthly horizon will influence the profitability. Translating from demand forecast to ordering requires a reduction or elimination of silos. Latency is most often caused by layers of hierarchy. Siloed decision making is a very common challenge. Large businesses are often regionally siloed too - hence the recognition of need for a centralised decision making unit. 
      Be aware that the flip side of empowerment can be regionally made decisions that affect other parts of the global business.
      Latency challenges/causes
      Slower decision making is exacerbated when finance does not trust the IBP number as much as its own financial forecast. There is a growing convergence of finance into supply chain; often the CFO is the ‘co-pilot’ to the CEO. 
      Suggested approaches to IBP and reducing latency
      A ‘whole organisation’ approach: IBP cannot just be a supply chain project - it must be integral and understood by all functions. The more senior the sponsorship, the better the chance of success.  It’s important to maintain your customer promise: supply chain is therefore a part of that. Having a centre of excellence can be a good way to tie it all in. The COE should be made up of people outside of operational roles in order to be truly effective.  Adopting a design thinking approach to customer lifecycle management is a useful approach - this can help other functions better understand the value of supply chain in the context of the customer promise.  Cloud technology reduces latency by having fewer technology anachronisms - applications are up to date, and are automatically maintained. This reduces lag.  IBP requires a business case. To do this, it can be valuable to look at what will happen if nothing is done, what are the costs to inaction.  Do not overfocus on a single instance platform across a large organization. There can be multiple clouds, and technology can interface. Organisational structure & people emerging best practices
      Of course, technology is not the only lever available for closing the planning - execution gap: organisation structure and process design can either hinder or help the flow of critical information and the capability for prompt, informed decisions to be taken.
      High performing organisations often demonstrate a non-siloed structure whereby, instead of a traditional SCOR-based model, the focus is on end-to-end processes like IBP, O2C and increasingly omnichannel and process owners who are responsible for holistic optimisation. Critical areas for best-in-class cross-functional alignment include product / service innovation, fulfilment & aftercare and planning. Increasingly these teams have business partners who, for example, have both a deep, systematic understanding of supply chain operations and financial control to bridge those potential silos. These are often supported by Centres of Excellence, particularly for analytics which major on optimising segmentation, cost-to-serve and customer behaviour. For planners in particular, it pays dividends not to confuse planning and execution and recognise that scheduling is not the same as planning as the latter requires particular skill sets around cross-functional communication in particular. Planners are likely to be more effective if they think and talk like business owners in terms of customer experience and profitability rather than a narrow focus on, for example, improving OTIF scores by a couple of points. 210414 IBPX Oracle Intent roundtable v1 (1).pdf
    • JP Doggett
      By JP Doggett
      The disconnect between plans and reality often undermine confidence in the effectiveness of planning processes. One of the main causes of this disconnect is latency in decision-making: the time between an event being detected (or predicted) and action being taken.
      This discussion will explore how this gap can be closed using an integrated  business planning and execution framework which can realise value from investments in IoT and AI.
      Discussion Partner: 
       AGENDA 
      IBPX: closing the gap between planning and execution
      Identifying principle causes of latency, including lack of: visibility scenario planning agility siloed responses Quantifying the implications for working capital and the bottom line How IoT, AI and prescriptive planning reduce latency within and IBPX framework Required foundations, roadmaps and next steps Juniper Networks case study
       WHO FOR? 
      Industry sectors: current practitioners from all sectors
      Located: UK & Ireland
      Org. size (annual T/O): most relevant for larger, more complex supply chains, typically £1bn+
      Roles & remits: Heads of:  Supply Chain, Logistics, Analytics, Transformation with a role in designing and implementing analytics capabilities
       ABOUT INTENT DISCUSSIONS 
      All discussions are private, held under the Chatham House Rule and moderated by INTENT with approx. 4-6 participants for 45-90 mins of candid, interactive discussion (not a passive webinar) Some discussions include subject matter experts from member-recommended INTENT Partners, others are exchanges of best practice, experiences and ideas among practitioner members only Discussions are shaped by participants according to their interests and questions We may adjust participation to avoid competitive sensitivities and ensure productive discussion  WHEN? 
      Tuesday 15th June (14.00 BST / 15.00 CEST) for max. 90 minutes
      Hosted by Intent
      Guest expert: Vikram Singla, Strategy Director, Oracle
       
      Request to join
       
      Interested but can't make the date? Email us and we'll update you about future discussions.
       
       
    • JP Doggett
      By JP Doggett
      Summary of discussions hosted by Dawn Dent,  Lucy Jacobs and Neil Hill of Oliver Wight with members from Novocure, Johnson & Johnson, Solvay, Cummins, Atlas Copco, Axalta Coating Systems, Coca-Cola European Partners, Unilever, Henkel, PZ Cussons, Pentland Brands, Marks & Spencer, Diageo, JCB, Arla Foods, Animalcare, Tupperware Brands and Sony Pictures Entertainment.
      Not losing sight of the bigger picture - integrating S&OE with S&OP
      It has been easy to become fixated on forecasting demand and so lose sight of end to end capacity and constraints. It's vital to take a broad view of risks and constraints under different scenarios and take a view on how much it is worth investing and where (e.g. bigger safety stocks, higher unit costs, extra capacity etc.). Without this integrated view, it often happens that one constraint is solved only for there to be another one right behind it. In addition to the external volatility, there has also been an element of 'self-induced' volatility as a consequence of not properly understanding the knock-on effects of decisions. Best practice is to formally integrate S&OE (aka Integrated Tactical Planning) with S&OP / IBP processes: the starting point is the approved S&OP / IBP plan which needs to be disaggregated into weekly, daily or even hourly horizons by segment or line, depending on your cadence; there should be a clear demand control process that is able to distinguish planned versus unplanned demand; a formal regular / weekly capacity review serves as the handshake between planning and manufacturing and / or procurement and gives better awareness of constraints and bottle necks that may need to be mitigated; critically, there should clear policies on acceptable safety stocks and on how to deal with deviations...what qualifies as a legitimate emergency requiring intervention, whose decision is it? This avoids each case being escalated and tying up time and attention; at the S&OE level, there will be fewer options and those options are usually more expensive but these learnings about options and costs need to be proactively fed into the S&OP / IBP cycle so that there's an accurate understanding of context at this level and due consideration can be given to whether to invest in generating more / better options; this includes regular and honest conversations with suppliers and partners... Getting and staying close to suppliers, partners and customers
      It's important to consider whether suppliers have enough information to be able to respond to our needs and plan for the longer term. In return, suppliers can often be excellent sources of information about what is happening the the world and what risks are present as they tend to have a bigger picture view. The pandemic-driven experience has highlighted the lack of visibility for higher tiers suppliers so this should form a part of the conversation with direct suppliers... even if you are not affected by capacity squeeze, they may well be.  There has been much discussion of dual sourcing but this is often not a realistic option. Especially where this is the case, it's important to be / become your suppliers' best customer through engagement and support. This engagement should be honest in the sense that not everything should always be the highest priority or seek to secure supply through pressure tactics; Sharing plans and making commitments further out means suppliers have more incentive and ability to meet those requirements. It's common to hear complaints about the lack of information coming from suppliers but part of that is about making it as easy as possible for them to supply the necessary information to feed into decision-making. At the other end, there ought to be good communication among the customer service / commercial teams about who to prioritise and focus on making reliable promises to customers even if that cannot be their first preference.  This might also include and integrated product review where, for example, priority might be given to lower margin products which are more reliable; When options on the supply side are limited, there may be levers on the customer side such as the timing of campaigns and prioritisation / rationalisation of SKUs; A complex but potentially very useful approach would be demand analytics to understand the transferability of demand between SKUs to generate more options in handling short term volatility. Scenario planning: understanding trade-offs and sticking to decisions at the business level
      Commercial and finance should be involved so that clear choices about trade-offs can be made and decisions backed up. The process can begin with supply chain describing a range of possible scenarios and actions that could be taken in response - with associated costs and implications for customers - but finance and commercial teams need to be involved so that trade offs are understood at a business level. Volatility is inherently unpredictable so often events will occur that no scenario will have envisaged but it's likely that you'll at least be closer to the right response by virtue of having had conversations around scenarios and constraints. Transparency is key so getting accurate and timely data and being able to project scenarios within a system easily and quickly is important otherwise time is wasted discussing what the picture is rather than what could be done about it. Also factor in human behaviour and the tendency to build in some margin to forecasts, especially if that influences allocations and people are naturally inclined to look after their corner of the business. It's vital to have an environment where assumptions can be challenged and tested. Codify assumptions which means turning thoughts into numbers by quantifying things like how supplier terms may change and what it might cost to secure supply under certain scenarios. When a business is in survival mode, it can be difficult to find time for these conversations with the right people involved but the alternative is time being taken up in an inefficient way because every time a planner and a commercial team member don't agree the dispute is escalated. It is worth taking the time up front to understand what uncomfortable decisions and trade offs need to be made and have the resolve to stick with those decisions to reduce the energy spent on these tactical battles. Of course, emergencies happen and urgent action needs to be taken but, soon after, it's vital to start planning the recovery.  
      Related discussion summaries: 
       
      demand-execution-white-paper.pdf OWA_Integrated Scenario Planning _2020May.pd
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