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DELIVERING PROMOTIONAL FORECAST ACCURACY WITHIN CPG COMPANIES

– AN APPROACH TO OPTIMIZING DEMAND SENSING IN A CUSTOMER CENTRIC ENVIRONMENT

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BY MIKKEL GEERTSEN, JON MORROW, RAHUL TYAGI, AND HENRIK KNAK, TATA CONSULTANCY SERVICES – SUPPLY CHAIN CENTER OF EXCELLENCE

Consumer packaged goods (CPG) companies continue to operate in a challenging landscape where promotional fore- casting and demand sensing efficiency suffers due to the

complexity and volatility of the commercial environment in which these companies operate. Often, there are a huge number of SKU’s (stock keeping unit), customers and locations to plan combined with the fact that many promotions may be running concurrently that results in cannibalisation of both promoted and non-promoted SKU’s. Another challenge is that many CPG companies unexpectedly experience large deviations in order patterns and demand resulting from the retailer’s promotion activities and decisions to forecast and replenishment policies. Collaborating store-level promotions and subsequent forecasts will allow both CPG companies and the retailers to achieve better results and flow of goods.

This article aims to highlight a possible science-driven and ho-listic approach to promotional forecasting and demand sensing. The idea is that this unique blending of scientific rigor with holistic optimisation of process, people, technology, and metrics can pro-vide a hard to replicate competitive advantage to an organization.

The challenges of promotional forecasting in CPG companiesThe retail & CPG industries have been buffeted by a combination of consumer, economic, and market conditions in the last few years that have significantly increased the complexity of forecasting and planning demand. While the Great Recession has significantly con-tributed to an increased promotional intensity, demographic shifts in both age and ethnicity have led to new product introductions at an unprecedented rate. The latter factor has also led to further segmentation of demand at a category level and an uptick in more customized events and promotions tailored to the emerging demo-graphic landscape. For the CPG manufacturer who was struggling with making the best use of demand signals emanating from retail channels, the rapid adoption of social media by its consumer base has opened the floodgates for real-time feedback.

In relation to the above, identifying promotion uplift is a con-stant challenge for all CPG companies. This complexity is often fur-ther impacted by lack of consumer insight, information sharing, and visibility between the manufacturer and the customer. Sales teams

DANSK RESUMÉ

CPG virksomheder opererer i dag i et miljø, hvor nøjagtigheden af kampagne-forecast er lav grundet høj kompleksitet og vola-tilitet i efterspørgslen. Ofte skal der planlægges et stort antal produkter, kunder og lokationer, hvilket - kombineret med mange usynkroniserede kampagner - resulterer i kannibalisering af både produkter i de aktuelle kampagner samt af en række produkter udenfor kampagnerne. En anden relevant udfordring i mange CPG virksomheder er store udsving i ordremønstrer og efterspørgsel, som er resultatet af kampagneaktiviteter i detailhandlen, men også den måde detailhandlens koncepter og politikker vedrørende forecasting og genopfyldning er sat op. Derfor vil øget samar-bejde mellem producenter og handlen vedrørende kampagner og forecast føre til bedre resultater og tilgængelighed af varer. Denne artikel ønsker at vise en videnskabelig og holistisk tilgang til forecasting af kampagner og begrebet 'demand sensing'. Fremgangsmåden er en unik blanding af stringent videnskabe-lig analyse og holistisk optimering af proceser, mennesker og teknologi med henblik på at opnå konkurrencemæssige fordele.

/Redaktionen

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responsible for generating promotional forecasts within CPG com-panies often spending the majority of their time gathering data and meeting with disparate groups with little time left to analyse, adequately prepare promotional forecasts and most importantly, converse with their customers.

The multitude and complexity of promotional offers and sup-porting activity make it extremely difficult, if not impossible for sales account executives and demand planners to accurately estimate the sales uplift, cannibalization, and associated profit impacts without substantial help and support. This challenge can potentially be re-solved through a hybrid of scientific forecasting and informed hu-man judgement supported by robust data management.

Promotional forecast volumes and errorA 2013 Forecasting Benchmark Study from Terra Technology reveals that over one-quarter of all shipments for CPG companies came from the sale of products on promotion and almost three quarters of products were promoted at least once during the year. Promotional activities drive revenue but also add considerable supply chain com-plexity and higher forecast error and more BIAS is seen for on pro-motion products compared to off promotion products, according to the study. This continued reliance on promotions is a definitive benefit in driving sales but at the same time makes forecast and ex-ecuting promotions considerably challenging. The real prominent

impact during promotions is an enduring over-optimistic outlook where weekly bias is more than five times higher for items on pro-motion than for the same items off promotion.

The key point from a promotional perspective is that consider-able efforts are invested in promotional planning and execution, and the reliance on promotions as a source of revenue makes accu-rately forecasting promotions more important than ever. Therefore, it is vital that CPG companies can develop an optimum demand driven forecast through demand sensing to meet both promotion profitability and efficiency objectives.

Big data analytics and the proliferation of customer dataThrough the proliferation of data available, technology vendors are building solutions around demand signal management for “near real time” processing of POS (point of sale) data. In-memory com-puting is gaining traction for pro-

cessing high volumes

of structured and un-structured data. Adoption of

big data is picking up as compa-nies are maturing in finding data

relevance and data management. Lev-eraging big data for optimising and im-

proving promotions can definitely address

business needs and improve return on investment of promotions. The ability to process huge volumes of data can assist in tracking promotions on a daily basis at both store and SKU level.

Big data, cloud and mobility are the key technological trends that are emerging. Big data is a growing trend, cloud adoption is in-

creasing and maturity level of mobility is high. Most CPG companies have adopted industry standards technologies and platforms for re-porting, analytics and insights but are still underutilized or in early stages of development. Indeed, many organizations struggle with how best to analyze and usefully leverage the mass of data being col-lected and stored. Ultimately, big data also requires big judgment.

In parallel, an emerging trend across the CPG industry is the concept of demand driven forecasting, which incorporates sens-ing, shaping, and responding to demand. The capacious shifts in replenishment of demand based on internal shipments to ware-houses and the subsequent impacts on supply chain creates huge strain on upstream planning functions to mitigate. It is imperative that the teams responsible for promotional forecasting and demand planning get as close to the customer as possible through the shar-ing and utilization of customer point of sales and Inventory data. Companies should be able to utilize this data and insight through advanced analytics to sense and leverage demand signals linked to patterns of consumer behaviors in relation to promotions and use this intelligence to generate more robust forecasts.

The explosion of POS, inventory and syndicated data should enable efficient trade promotion management, effective sales & operations planning increasing customer satisfaction, growth and margin. With access to downstream data, promotions can be effec-tively managed and optimized and supply chains can be much more responsive to actual consumer demand. Data mining and advanced analytics techniques can be leveraged to detect problems such as stock-outs before they occur and combining

store level POS analysis with loyalty information allows for precise mod-

eling of assortments by store and by cluster.The CPG industry has access to rich syndicated

data relating to media and retail channels including competitor activity. Syndicated data had an average two-

week lag, but is now moving towards a much more real time and integrated view of multiple channels and media. Syndicated data can be used to establish cause and effect relationships considering many factors such as competitor activity and distribution effectiveness.

Also, the social web is abuzz with opinions and discussions about various products and brands that can help both CPG and re-tailers to understand consumers’ relationship with the brand and how to best leverage. This is a good arena to test new products and craft appropriate messages based on core consumer reactions. Re-tailers are making available restricted views of their loyalty data directly or through third parties to enable enhanced consumer in-

“The ability to process huge volumes of data can assist in tracking promotions on a daily basis at both store and SKU level

“With access to downstream data, promotions can be effectively managed and optimized and supply chains can be much more responsive to actual consumer demand

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sight. Shopper segments, when combined with standard consumer segments, can enable retailers and CPG to better target promotions and offers, increase personalisation and improve shopper experi-ence, influencing shopper at the point of purchase.

A high level solution approachBased on experience and numerous researches we know that al-though human expertise can offer improvements beyond that avail-able from a simple statistical model, these do not lead to systematic improvements. CPG companies need to understand the demand signals from the perspective of baseline and promotional uplift. This requires a multi-causal approach to data analysis that concur-rently models baseline, trend, seasonality, the effect of price and other promotional factors.

Promotions are constantly evolving and therefore dynamically affecting the forecasts, and thus the forecasting process allows the use of the most recent information. The solution is a fully automatable statistical promotional forecasting model that increased accuracy substantially, over baseline and expert forecasts. Human expertise does still in some situations add value to the forecasts, therefore a hybrid statistical-expert solution is recommendable for achieving a world class promotion and demand planning solution.

To reduce promotion costs and generate predictive strategies for op-timizing sales volume and margin through promotion planning, optimization, reporting, and analysis certain elements are needed:

❙ Predictive models and analytic insights to help the companies to move from insights to action, and ultimately increase ROI and promotion lift.

❙ Insight into what a CPG company should expect from a promo-tion and how to improve those results by applying advanced predictive models and strategic consulting up front. “What-if” scenario modelling provides deep analytic strategies built from aggregated CPG, retailer and third-party data sets.

❙ Price optimisation makes it possible to identify and observe what the price elasticity is for respective brand and categories, and also helps to identify at what price point each promotional lever drives incremental growth. These insights are used as key learnings to develop and adjust marketing and sales guidelines.

❙ Delivering reports and provide analysis and actionable insights faster, facilitating critical trade promotion decisions in less time, also enabling continuous promotion process improvement.

Figure 1. Source: Tata Consultancy Services – Supply Chain Center of Excellence. Word shotenings: DC (distribution center), OOS (out of shelf), DSD (direct store delivery), NEX (network exchange), DEX (direct exchange), and ASN (advanced shipping notification).

Warehouse Distribution

Original Shipment Note

Bottom-up Retail DC Level Forecast

Order/Allocation Change Suggestions

Updated Shipment Note

CPG DC

ASN-basedDSD Shipment

CPG Retail-DC Forecasting

Forecast Synchronization & Modulation

DSD

ForecastAggregation to Retail DC Level

Store/Region Level Demand Forecasting

DSD DeliveryScheduler

Store Demand Forecast & Projected OOS

Store DSD DEX/NEX Data, Shelf Condition

Retailer Store

Retailer DC

Store POS,Inventory,

Planned Receipts

Promotional ‘Causals’

CPG DC

SENSING THE RETAIL DEMAND WITH POS DATA

POS overviewMany CPG companies have started to include the retailer demand into their forecasts, but the statistical forecasts are often created based on sales and shipment history. CPG companies that are able to include POS data as a shelf-driven demand signal can develop more accurate, customer time-phased demand plans taking into account the effects of promotions and marketing events.

Globally, sharing of POS data by retailers has picked up steam over the last few years as trading partners have realized the benefits of utilizing such data to carry out demand sensing. Technologically,

this has been accompanied by emergence of DSR (demand signal repositories) that are used to store various demand signals i.e. POS data, promotion, event and consumer data. While most leading CPG companies have adopted DSRs, they still struggle with making the best use of this treasure trove of data. Over the last few years, solu-tion providers have emerged that can derive insights from the DSR data and use them to modify the forecasts and demand plans at a customer-level. While this certainly improves the forecast quality, it does not harness the full potential of customer POS data.

Figure 1 illustrates an approach where a CPG manufacturer uses the customer POS data as the basis of generating forecast rather than using the shipment, order or invoice data. Not only does this remove the latency from the CPG company’s customer supply chain, it im-proves the promotional forecast accuracy through a better under-standing of the markets.

The technological challenges with carrying out deep analytics on such large data sets have mostly been rendered moot by the rapid strides in the area of big data architecture and in-memory analytics.

Better promotional forecasting and the financial benefitsSupply chain expenses are huge and often represent percentages rang-ing anything from 15-40% of the cost depending on the industry. The annual spendings are vast and even small improvements and efficiency gains can mean huge savings on the bottom line. When thinking about better promotional forecast and introduction of new technologies, it is hugely important to document the impact and financial benefits that the changes will provide.

Enabling the CPG manufacturers to improve core operational ac-tivities like forecasting, replenishment and inventory management, combined with a promotional forecast solution will enable CPG manufacturer to free up working capital by further reducing inven-tory and better capture growth opportunities in volatile markets by reacting faster to changes in consumer demand. Other benefits includes securing a solid return on downstream data investments and providing value to retailers through better service, improved on-shelf availability and reduced retailer inventory.

Typical benefits a promotional forecast solution will bring to an organisation:

❙ Financial advantage - Remove days or weeks of inventory, lower working capital requirements, improve cash conversion cycle, and cut operating expenses and drive profitable growth.

❙ Supply chain productivity - Improve on-shelf availability and customer service, enhance lean processes, reduce unplanned changeovers, and decrease stock rebalancing/order movements.

❙ Sustainability – Reducing provides more impact than reuse or recycle, creating a smaller inventory footprint. Lower emissions from the redistribution of goods to achieve significant carbon and water savings.

ConclusionWhile an analytics-based approach to forecasting is de rigueur for high forecast accuracy, every organization needs to calibrate its ap-proach based on its process maturity and core competence. Only when this fine balance is achieved can organisations ensure an ef-fective and stable forecasting process in the long term.

The promotional forecasting and supply chain management ap-proaches mentioned in this article are meant to fast and scalable. The unique blending of scientific rigor with holistic optimisation of process, people, technology and metrics can provide a hard to replicate competitive advantage to organizations. /

“The annual spendings are vast and even small improvements and efficiency gains can mean huge savings on the bottom line

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