DEMYSTIFYING FORECAST ACCURACY ACHIEVING HIGH SALES FORECAST ACCURACY IS A MATTER OF DISCIPLINE

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1 perspective JUL 2013 DEMYSTIFYING FORECAST ACCURACY ACHIEVING HIGH SALES FORECAST ACCURACY IS A MATTER OF DISCIPLINE Alberto Calvo, Alessandro Barmettler, Alberto Oteri

2 Demystifying forecast accuracy: achieving high sales forecast accuracy is a matter of discipline Published by Value Partners Management Consulting via Vespri Siciliani Milan, Italy July 2013 Written and edited by: Alberto Calvo, Alessandro Barmettler, Alberto Oteri If you would like an electronic copy please write to: cristina.goddi@valuepartners.com For more information on the issues raised in the report please contact: alberto.calvo@valuepartners.com alessandro.barmettler@ valuepartners.com alberto.oteri@valuepartners.com If you would like to subscribe or to be removed from our mailing list please write to: subscription@valuepartners.com valuepartners.com Copyright Value Partners Management Consulting All rights reserved

3 3 executive summary My wife and I walked into a Milan patisserie to buy a small cake. We walked out with a tray full of mouthwatering cannoli, in a quantity suitable for a small army. For free. How did that happen? Something with the shop s forecasting had gone terribly wrong that day. Whilst thanking the chef for this unexpected tray-full of bliss we found out that more or less each evening he has to somehow dispose of unsold cakes, either supplying them to soup kitchens or handing them out for free to unsuspecting clients. If an apparently simple operation such as a patisserie employing 10 skilled workers who have crafted the same products for decades isn t able to plan its production accurately, then how can a multinational company successfully manage its own forecasting? Like our patisserie, multinationals struggle with forecasting though this is not altogether surprising demand planning is one the most challenging jobs around.

4 4 Annex 1 Europe, Quarterly variation of Sales, Percentage, 1Q Q ,5 1 0,5 31% 0 14% 31% 57% -0,5-1 -1, Standard deviation Automotive Food, beverages, tobacco Source: Eurostat.

5 5 have you to cope with increasing volatility? Over the last few years a number of firms have suffered the effects of increased market demand volatility, especially those with a varied product offering and a high number of SKUs. (see annex 1) As the opening example showed, the process of demand forecasting is not only crucial to a company s success, but is also inherently challenging. Within the context of larger firms, this process may become further complicated through the interplay of many factors such as investment decisions, supply chain management and strategic planning. From our experiences, we believe that the process of company forecasting seeks to address four key objectives: 1. To guide and support commercial planning helping to align objectives regarding volume, channels, client, price and strategy 2. To calibrate the supply chain and operations enabling an increase in service levels through the optimisation of logistics and manufacturing planning in the short term 3. To decide which products to manufacture in which plant optimising the investment decisions in the medium-long term However when addressing the issue, many firms mistakenly try to find solutions through the use of IT, investing a considerable amount of resources in softwarebased programs such as data analysis and statistical software, without developing a more comprehensive and wider view of the issue. 4. To quantify the expected economic and financial results helping anticipate and forecast future financial incomes, maintain control over strategic directives, manage cash flows, etc. However, despite the clarity of these objectives, the process of accurate forecasting is becoming increasingly complicated and having correspondingly detrimental effects on the accuracy of forecasting models (see annex 2). Typically, the complexity of a forecasting model for an individual product (which may include multiple SKUs) will be determined by four key factors: the time-frame of the model, the geographical coverage, the product s channel presence and the total number of clients served.

6 6 Annex 2 MAPE %, % 100% 80% 60% 40% 20% 0% Automotive company Consumer goods (food) company Consumer goods (white) company Source: Value Partners.

7 7 By considering each of these four factors in turn, one can deduce the origins of increasing forecasting complexity. For instance, with respect to the geographic coverage, the internationalisation of many firms means that they are now operating in multiple localities with a corresponding increase in the number of inputs to be considered in the forecasting process. Additionally, with respect to channel presence, the emerging trend of omni-channel behaviour by businesses where companies are selling products online, in-stores and over-the-phone further complicates their forecasting processes. These complications increase significantly for firms with a wide-ranging product portfolio as each individual product is likely to have a unique set of drivers and delivery channels and the sales of one product may have a direct effect upon the sales of another. Indeed, these internal complications are mirrored by additional complications emerging from external factors. With wide-ranging supply chains and target markets, many companies are becoming increasingly exposed to a wide range of market volatilities. Furthermore, it is important to consider the general health and direction of the economy overall. For instance, in a fast and booming economy, forecasts are likely to have to take account of growing demand levels and hence supply volumes will necessarily need to increase as well. In a recessive economy, those firms which ignore the general economic trends may see a corresponding overestimation of overall volumes. Despite the complexity of forecasting increasing, the costs of low levels of accuracy have remained as high as ever. Fundamentally, if the aforementioned complexities are not correctly managed, the consequentially low forecasting accuracy will generate inefficiencies for the firm, negatively impacting upon the company s cash-flow and P&L. More specifically, these inaccuracies can lead to inefficiencies in a range of different ways such as mis-matched levels of stock, poor levels of service and lower levels of production flexibility through poor system planning. However, when addressing the issue, many firms mistakenly try to find solutions through the use of IT, investing a considerable amount of resources in software-based programs such as data analytics and statistical software, without developing a more comprehensive and wider view of the issue.

8 8 Annex 3 High level KPls Operative KPls... 1 ABC error... IT systems evolution Tools simplicity... 7 Analyze error Define key determinants metrics 2 6 Optimize tools FCST Restate the basics Objectives Rules & definitions... Design the new process Qualify the FCST model 3 Roles Activities work flows... 5 Identify information ownership 4 Market estimates Demand segmentation... Responsibility Info work flows... Source: Value Partners.

9 9 coming to grips with forecast accuracy: the power of discipline is always underrated Annex 4 MAPE % It is within this context, and based upon our extensive experience, that Value Partners would like to present a range of ways in which we feel that companies are able to refine and improve their forecasting abilities. (see annex 3) 1. Understanding the major causes of forecasting errors. It is essential to analyse the deviation between forecast predictions and recorded data with respect to two main variables timeframe and granularity. Firstly, it is necessary to assess the ability of a firm to develop estimates in both the short and mediumlong term and to establish whether any differences emerge between the two. Forecast Timeframe Secondly, it is important to ensure that the correct level of detail is captured such that changing trends in total volume and SKU mix are correctly built in to the forecast. Through our previous work, Value Partners have noticed that the ability for firms to forecast over different time periods may vary. For instance, when analysing different forecast timeframes during an advisory project for a major global automotive player, it emerged that the firm was entirely focussed on the short term (i.e. 1-2 months), resulting in a considerable elevation of MAPE (Mean Average Percentage Error; one of the main KPIs adopted by enterprises to assess forecast accuracy) for longer term forecasts (i.e. over 3 months). (see annex 4) +45% +38% 112% +79% 164% 201% Another example can be found in the case of an international company operating in the white goods space that was experiencing a limited ability to forecast their sales mix. This was due to a low accuracy in estimating total volumes as a result of a budget that was misaligned from wider market trends. 81% 68% 56% 1m 2m 3m Production frozen 60-80% Source: Value Partners. 6m 12m CAPEX allocation 24m Causes for high deviation are numerous, usually hard to identify and driven by erroneous activities that have often become common practice over time. For these reasons it is necessary to analyse every process and sub-process, the consistency of the timeframe, information quality and responsibility allocation, both at central and local levels. (see annex 5)

10 10 Annex 5 Unpredictable demand Orders to be received difficult to foreseen Volumes + + Almost certain demand Part of demand to be segmented with specific rules Certain demand Contracted orders to be delivered short-term medium-term long-term FCST timeframe Source: Value Partners.

11 11 An in-depth evaluation of forecasting would enable the tracing of each component of the errors that lead to a high deviation in forecast accuracy. The overview and identification of every cause of error is the first step towards the development of a new forecast model, which must include every strategic and operational aspect of the firm. 2. Clarification of the basic rules and meaning of forecasting. The meaning of the term forecast is vague and could therefore be interpreted differently by different firms. In addition, the dimensions upon which forecasts are built are numerous (orders, shipments, turnover, etc.) and may have correspondingly different implications for the forecasting process. The understanding and interpretation of this term should therefore be clarified for all involved. Fundamentally, forecasting accomplishes three main tasks and becomes the input of three different business functions: Sales (commercial planning), Operations & Supply-Chain (logistics optimisation and investment allocation) Corporate Finance and Control (impact appraisal and economic/financial results). An understanding of the desired outputs from a forecasting model should also be understood in order to ensure accurate outputs and avoid subsequent complications. For instance, many firms use order forecasting as a proxy for estimating turnover, however turnover forecasts can result in a number of paybacks (e.g. more coherent financial projections with the firm s real potential) especially when sales forecasts are based on complex operating models. 3. Definition of the firm s specific forecast model. First and foremost sales estimates should be based upon realistic market projections. The developments of recent years have proven that the market and the general economic context can rapidly alter. For this reason, forecasts (e.g. budget, strategic plan, etc.) with 6-12 month timeframes can swiftly prove to be mistaken in their structure and content. In many cases, it is more appropriate to define a composite model to estimate the total volumes for each market and each strategic segment before improving the capability of forecasting which is a combination of these separate elements. Such a model should necessarily include competitive intelligence, which incorporates the actions and strategies of a company s main competitors. Secondly, clients should be constantly monitored in order to positively refine the accuracy of forecasts. Depending on the business of the firm, segmenting the customer base can facilitate the assessment of the market at the same time as helping to capture all of the relevant phenomena.

12 12 Annex 6 Commercial planning (by client) Service level (e.g. less Lost Sales) MIX detail by sku 2 3 Optimization of product allocations and investments (e.g. moulds) dimensions Stock level rationalization 1 Commercial planning (by client) Volumes by macro segment short-term medium-term long-term Factory planning and investments FCST timeframe Source: Value Partners.

13 13 For example, some firms have been implementing joint-forecast models together with their clients, even integrating their forecast and order management systems to gain a real-time view over clients orders. Lastly, to guarantee accuracy throughout the processes of market and client base assessment it is necessary to rigorously define the criteria and practices that underpin effective demand segmentation. The forecast should be built upon two types of demand (with minor differences depending on the business): certain demand, relating to received orders; and open demand, referring to potential upcoming orders. (see annex 6) Of the two, the second demand type is the most challenging to assess. In order to gain full understanding it is therefore crucial to define and employ appropriate methodologies (e.g. the use of statistics). 4. Identification of information owners. It is important to assign or identify a point of contact within a firm with responsibility for data gathering, and for this individual or group to be appraised on a performance basis. In addition to a point of contact within a firm, it is equally important throughout the forecast process to review the exact timing of sharing of information on different external/ internal processes. Furthermore, if the quality of the overall process is contingent on the demand Planning function, it is also necessary to develop forecasting in conjunction with further business functions and integrate the different information streams to create a holistic picture. Demand Planning, Sales, Logistics and Pricing should work together to ensure that all the relevant data is captured, shared and included in the forecast. The overview and identification of all the causes is the first step towards the development of a new forecast model, which will have to include every strategic and operational aspect of the firm.

14 14 Annex 7 + <40% <50% fragmentation (# clients / channels) <20% <30% + Granularity (# sku) Source: Value Partners.

15 15 5. Design of the new forecasting process. Once the objectives and nature of the forecast under development are established and all required information (including the identification of an individual responsible for information gathering), it is then possible to design a forecasting process that is capable of improving overall efficiency. The design of the forecasting process should start from the definition of strategic objectives, identifying the right balance of main operational constraints; a delayed forecast with fewer operational constraints would likely be more accurate, however the level of service and the ability of the supply chain to react promptly would be impaired. (see annex 7) Firms with a substantial geographical heterogeneity should also include the sharing of information from central and local sources in the process, as information could be stored at a local level (e.g. local regulations, client data, local commercial strategy, etc.) as well as at central level (e.g. long-term trends, regional level trends, product strategy, etc.). Like all business processes it is always necessary to ensure substantive commitment from all the parties involved. Coherently empowering all of the actors involved in the process and identifying specific KPIs to appraise every actor s contribution to the overall quality of the forecast facilitates a more productive and coordinated focus aligned with the importance of the process. Based on our experience we would argue that many firms suffer low commitment to sales forecasting due to a deficiency in the clarity of objectives, absence of rules and a lack of consensus on the value of projects. 6. Support from IT systems to activities. As previously mentioned, the employment of IT resources does not directly augment forecasting accuracy, and may even be an obstacle to the correct integration of processes and information in some instances. However, it remains of primary importance within the overall process. Statistical tools must be considered differently. Several firms have achieved positive results thanks to the employment of statistical forecasting; many of these firms are in the spare parts sector which is predominantly influenced by historical sales rather than market trends. When employing statistical tools it becomes critical to ensure that algorithms are not exclusively based upon historical data though take into consideration future trends, market trends, macroeconomic metrics (GDP, consumer index, etc.), market characteristics and exogenous factors (e.g. new regulations, climate changes, supply chain stock, etc.).

16 16 7. Identification of KPIs to monitor. In order for an organisation s forecasting performance to be evaluated successfully it is necessary to identify a simple and exhaustive set of KPIs along two main dimensions: Forecast timeframe both in the short and medium-long term However the MAPE of a single month is not sufficient to ensure understanding of the nature of main forecast errors; to guarantee comprehensive understanding it must be integrated with other metrics measuring the main forecast dimensions (e.g. granularity, timeframe, etc.). Forecast granularity measuring the forecast accuracy on macro figures, product mix and SKU Amongst the metrics identified and analysed, MAPE (Mean Average Percentage Error) is the most valid and accurate when measuring the difference between actual and forecast data, as it does not have the problem concerning averaging positive and negative errors which afflicts other metrics. Indeed the forecast accomplishes three main tasks and becomes the input of three different business functions: Sales (commercial planning), Operations & Supply-Chain (logistics optimisation and investment allocation) Corporate Finance and Control (impact appraisal and economic/financial results).

17 17 Conclusions In summary, the adoption of an easy and efficient forecast model based on sales predictions derived from an analysis of market trends and the firm s commercial plans produces more accurate and realistic forecasts. The ability to assess the market, understand the main trends and drivers and react responsively to a market slowdown as well as expansion is not just a matter of being internally efficient but also having a competitive advantage that is hard to replicate. Based on our extensive experience in demand forecasting for large industrial players, we have been able to record and benchmark errors to develop a framework for acceptable forecast errors which should be used by all organisations. Such values (which vary depending on the business) are based upon two main directives: number of SKUs and market fragmentation, depending on the number of channels and clients served. (see annex 8) Annex 8 Q&A Demand Planning is one of the roles that people try always to avoid, I wonder why. It seems impossible that to have a decent service level, you must set so high stock targets levels. the Demand Planner is often alone, neither Logistics nor Sales are supporting him. Why this lack of inter-functionality? When Forecast error is high: Demand Planning blames Logistics and Logistics blame Sales, and so on. So who should be responsible for MAPE?

18 18 AUTHORS Alberto Calvo Partner, Milan Office alessandro barmettler Senior Engagement Manager, Milan Office alberto oteri Associate, Milan Office

19 19 About Value Partners Value Partners is a global management consulting firm that works with multinational corporations and high-potential entrepreneurial businesses to identify and pursue value enhancement initiatives across innovation, international expansion, and operational effectiveness. Founded in Milan in 1993, Value Partners rapid growth testifies to the value it has created for clients over time. Today it draws on 25 partners and 280 professionals from 23 nations, working out of offices in Milan, London, Istanbul, São Paulo, Buenos Aires, Beijing, Shanghai, Hong Kong and Singapore. In 2007 Value Partners acquired Spectrum Strategy Consultants a leading UK company specialized in publishing, broadcasting, entertainment, IPTV and mobile thus further strengthening its international presence. Today Value Partners is a leading advisor in the telecom, media and technology sectors worldwide. For more information on the issues raised in this note please contact the authors. Find all the contact details on valuepartners.com Milan London Istanbul São Paulo Buenos Aires Beijing Shanghai Hong Kong Singapore Value Partners has built a portfolio of more than 350 international clients from the original 10 in 1993 with a worldwide revenue mix. Value Partners combines methodological approaches and analytical frameworks with hands-on attitude and practical industry experience developed in an executive capacity within each sector: telecommunications, new media, financial services, energy, manufacturing and hi-tech. Copyright Value Partners Management Consulting Limited All rights reserved