Point of View Data, data everywhere... but not a drop to drink

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1 We make it happen. Better. Point of View Data, data everywhere... but not a drop to drink A paper by Hitachi Consulting Data Analytics & Supply Chain

2 Data analytics and supply chain Data is lying about in unconnected silos and waiting lists to get actions taken are growing ever longer In today s data-rich world, many organizations are awash with the possibilities that data can bring. Internally, it is certainly true that we have reports everywhere and on everything data is lying about in unconnected silos and it appears the waiting list to get an analyst to action your request is growing ever longer. Meanwhile, externally, competitors say they are moving ahead in innovative analytics; stealing a march on Big Data science. What is to be done? Many organizations grab at these data opportunities like the child in a candy store: gorging on sweets, some of which are truly excellent, but then some time later, unsurprisingly, ending up feeling rather worse for the experience. Instead, these new data analytics opportunities should be thought of as part of the Business Intelligence area and be fully integrated with other business functions; the time has come to think about data analytics as a critical business capability and focus on building value from it. What are the opportunities in Supply Chain related to these BI possibilities? Supply Chain is one of the most impacted areas and in need of data analytics. Supply chain, and more specifically Sales and Operations Planning (S&OP), is probably the area most impacted by new data analytics capabilities. S&OP is one of the more complex areas of business activity with a wide range of topics: customers (satisfaction, penetration); operations (costs, performances, staff); products and services (margins, volumes, quality levels); suppliers (deliveries, prices, fulfilment); ingredients and raw materials; and the list goes on. S&OP is also very volatile with seasonal demand variations, changes in weather conditions, holiday seasons and economic conditions. Furthermore, the complexity and volatility tends to increase with more demanding customers, more personalization of product and services, shorter product cycle times, for example. Given the increasing complexity and volatility of S&OP the need for good quality data and value adding insight is perhaps greater than other business areas. Building on what you have. Most leading companies have already installed a lot of advanced techniques in data analytics and integration within their market value-chain such as real time visibility of their customers or suppliers inventories or orders, or usage of various forecasting models to optimize production and cash positions. Taking an example in the area of logistics, geo-trackers on trucks and deliveries have become the norm. Insights from which enable, on the one hand, more cost effective fleet management for our businesses, and on the other, offers the potential for us to provide value-added services to customers to track their orders at any time enabling users to access insights in real time at any time. Similar results will be available from the ever growing use of sensors and mobile data across not just the supply chain but the whole organization and our daily lives. The much hyped internet of things builds on this path, adding data-capture technologies and connectivity to all manner of machines, products and infrastructures that we interact with during our working and daily lives. Accessing these data sources and using them for advantage will add to the complexity of the S&OP world even more; the benefits from exploiting these new data sources will bring access to new insights not yet dreamed possible. 02

3 Demands on Business Intelligence (BI) innovation do not always come from within, for example, the ability to provide quickly a single customer view at any moment in time, introduced at first, in the financial sector, as a regulatory requirement to protect customers in the case of banking insolvency, is now widely seen as a valuable asset for sales and customer relationship management. In food production, traceability of supply requirements introduced in the wake of the food hygiene crises, while an administrative burden on the supply chain, provides a source of new insights on suppliers, food provenance, green credentials, as well as food safety, enabling exciting new options for product differentiation. Using this information to identify, for example, weaknesses in the supply chain and make more predictive models not only benefits the customer in terms of food safety but also provides multiple benefits to suppliers in understanding the variables that affect supply chain functionality. New BI capabilities as a competitive advantage. Advantages from leveraging new BI capabilities well can provide a source of competitive differentiation for companies in the short term and deliver important cost advantages in the medium to long term. As breakthroughs in the area of data analytics come on stream, we believe this differentiator will magnify competitive advantages with the likely arrival of new and stronger business barriers to entry. It is important therefore for business to prevent the Data Analytics gap between competitors from growing too large. Key challenges in integrating new Business Intelligence capabilities When a company starts on the road to integrate further Business Intelligence capabilities, what are the challenges and lessons learned? We have identified three key learnings from working with our clients: Understand what your Business Intelligence requirements are and how they are related to your business needs Ensure your Business Intelligence maturity matches your organizational maturity Work towards introducing a Business Intelligence Competency Center (BICC) Business Intelligence requirements related to the business needs. Improved BI will support you not only in generating new business insights, but also by providing a better understanding of your current operating processes. This knowledge will in turn enable performance improvements. As your BI improves and faster more robust decision making becomes possible, the business is well positioned to make the most of new opportunities across your value chain. Exhibit 1: Intelligence requirements related to the business needs History, Trends & Pattern Analysis Statistical Forecasts & Predictive Analytics Correlation & Neural Networks Finance Business Plans Financial Models Finance Governance Marketing Market Analysis & Trends Brand Management Supply Chain Demand Planning Procurement Logistics R&D New Product Introductions Operations Asset Management Capacity Production Plans HR Recruitment Talent Management Sales Customer Forecast Relationship Management Business Intelligence Requirements (data management, analytics and reporting) Regulatory Compliance Management Dashboards KPIs Issues & Actions Market Analysis Forecast & Trends Data Science Training & Support Knowledge Management Collaboration Visualisation & Self-serve Mobile & Cloud Big Data Non-relational Data-stores 03

4 Exhibit 2: Your 6 point plan for action 1. Build robust data on which to base your analytics and reporting check the quality of data and that single sources are used. 2. Develop a clear strategy around what BI is needed by what users ensure that this highlights whether business objectives are on track. 3. Review the metrics currently deployed check if BI is fit for purpose? Are forward looking and predictive insights available? 4. Ensure processes are sufficient in delivering the needed BI can more reports be automated? 5. Conduct a BI skills audit to identify super user and end user capabilities do you have the right BI skill mix available? 6. Review if the current BI tools & applications deliver as needed check if IT architectures are aligned. And some key considerations Too much BI can be as damaging as too little check if all the metrics, analytics and reports currently deployed are really being used Make full use of the data you have use historic data to build Predictive Analytics Models and start building-up knowledge libraries and organizational learning / data science skills Think of end-users as consumers or customers and tailor your BI to their needs In a leading beverage company, for example, data was regularly captured on all the key marketing metrics, but no analysis had been carried out to assess which of these metrics were key performance drivers. Today however, analytics can be applied to data to support and drive business decision making, joining currently unconnected silos of data from across the business, readily identifying patterns in data, identifying key influences and performance drivers within data-sets and predicting trends and anticipated future performances for the business. Data science adds new insights and value to data; for example, monitoring customer spend patterns and identifying likely demand for future purchases. The same global drinks business also had varied management information capabilities across its different geographies. In some markets, the business was quickly able to access the data needed from its CRM and ERP systems; in other markets a laborious work process was needed each month to extract data from transactional systems; filter data sets; match outputs; cleanse and even correct misleading data before a meaningful spreadsheet could be produced. Today, leveraging data automatically and delivering insights to managers when and where they need it, in real time if necessary, is fully possible. It is important therefore to consider what is appropriate for the organizational needs when considering the introduction of improvements to BI and its deliverables. Like in other areas of business, establishing the right foundations for BI (see Exhibit 2) and aligning these to business needs is a key first step to learning how to improve. Business Intelligence maturity vs organizational maturity In introducing new data analytics capabilities, it is key to develop an appropriate BI strategy aligned with your internal organization capabilities. The first step is to understand what levels of BI and organization maturity currently exist across the organization and if they match to ensure the investments and efforts are put in the most valuable areas and in the right sequence. There is no benefit of having and advanced BI capabilities if the organization is not able to use them or if the main improvements needed in fact lie elsewhere, for example, in the organization s operational processes, or competences. For example, a packaging industry company was investing heavily in forecasting modelling to better predict demand. However, this was flawed by the lack of visibility of the internal promotions process and salesforce adjustments, and on top of that some basic production lead times were not updated. In this case, the priority was to improve the internal S&OP processes prior to introducing the related new BI capabilities. It is essential to understand the BI maturity stages that all organizations follow when evolving their BI from a low value business overhead operation, to a high value strategic function that drives market share. Each part of the business may perform its BI activities differently, and is likely to vary in its BI maturity level and how it will evolve, and what pace, through the maturity stages, so it is important to measure on a regular basis where on the journey each part currently lies. Only in this way can an appropriate BI strategy be developed to move the whole organization s BI capabilities cleanly from one stage to the next. Depending on where your organization is on the maturity curves see Exhibit 3 the appropriate strategy and roadmap can be defined to achieve your end goal. For example, adding new data sources and types are not always the first goal; a sensible first goal might be to have better access to existing data sources across business areas, such as point-of-sale, inventory, advertising spending, and shipment data. Improving the frequency and speed that you can report data or making data available that is more granular can still deliver a significant step change in improvement. In the case of our earlier beverage company example, it was pointless to rush ahead to deploy the latest drill-down BI capabilities everywhere at the same time. Instead, those geographies struggling with data management issues, first needed to be helped to consolidate and improve their IT infrastructure, before contemplating moving on to any in-memory reporting. 04

5 Organization focus Supply Chain Excellence Exhibit 3: The widening outlook as BI and Organizational capabilities mature and align over time more Value chain integrated plans, business and scenario modelling Maturity less Network management, forecast & pull planning, JIT Integrated - planning, management systems Competences, processes, tools Supply Chain information system platforms Integrated data warehouse solutions across business areas Predictive analytics and intelligent BI; self-serve insight when & where Enhanced it is needed Data Analytics and Big Data science leveraging new external content, e.g. geo-tracker, sensor, voice, sentiment data sources BI focus Functional Internally integrated Externally connected Symbiotic Meanwhile in other geographies that already benefited from robust IT and data management capabilities, they could start immediately exploiting the latest analytics made possible from today s reporting solutions. This would allow the consumers of BI in their local organization to self-serve and see at a click not only the high-level business measures but also to access the related individual transaction-level details. The strategic BI activity in each of the business needs to be tailored to specific requirements relevant to the BI maturity stage. The good news for those parts of the organization fearing they will be left behind in the BI capability race, is that the latest BI deployment strategies, for example, using cloud-based technology infrastructures and software as a service to support enhanced analytics and insight availability, can often be more quickly and more cost effectively deployed than in the case of the more traditional past deployments made by other more BI mature parts of their organization. Seizing these late-comer benefits will deliver some quick wins. Another leading manufacturing business was, for example, able to improve processing times on key BI reports from 77 minutes to 13 seconds, putting sales and profitability information into the hands of its sales executives far faster than before. As a result of these BI reporting changes, supported by a new technology solution, its sales team was now able to enter meetings with retailers fully equipped with up-to-theminute information on how the manufacturers products were faring in the relevant stores, and so able to adjust pricing and promotions to respond to the demands of their retailers and customers, while also ensuring they were aligned with their internal business goals and financial metrics. Introducing the Business Intelligence Competency Center (BICC) BI is about getting the right information into the right people s hands in the right format that allows them to access and understand the data quickly, and make decisions on it. The BICC needs to ensure that this happens smoothly and effectively. While not appropriate for all organizations dependent on their maturity level, working on a road map toward introduction is a sensible approach in most BI strategies. The BICC overtime becomes accountable for defining, owning and managing the execution of the company s BI strategy and agenda. Working with the BI user community the BICC serves as the catalyst and the glue that creates, promotes and holds together the overall BI operating model. It ensures that the BI strategy is aligned to the organization s needs. It also provides guidance and training across the organization on BI solutions. Often the BICC also becomes the home of the data scientists in the business - those responsible for capturing new insights and enabling new ways of working with data. A key finding in Hitachi Consulting s recent retail multi-channel survey showed that while 72% of respondents had visibility of the item unit cost of sale per channel, identifying item profitability proved to be far more challenging. The survey further noted that the complexity of managing multi-channel retailing often lead to unprofitable sales. The survey called for the delivery of improved insights, analytics and reporting on these cost and income streams stating these as critical to the long term viability of businesses. A helpful step in enabling improved BI is the creation of a Business Intelligence Competency Center. 1 Footnote: The new economics of Multichannel: Now is the time was written by Hitachi Consulting and IMRG in July 2014 and covers of over 40 key brands, representing two-thirds of the UK s high street retailers. 05

6 Summary S&OP is subject to significant complexity and volatility perhaps more than any other area of business. Given this, understanding the full S&OP picture at any moment in time and using data to plan and navigate ways to success is critical. Learnings from past performances need to be systematically captured and new improved ways of working must be tested and then implemented. To enable improved understanding across the supply chain it is essential to first recognize the strengths and weakness in the current BI and organization capabilities and then develop realistic plans to improve over time. With a clear plan and robust management in place the opportunities from BI analytics for the S&OP organization are many. About the Authors Doug McConchie is a Senior Manager at Hitachi Consulting. Over the past 15+ years he has advised some of the world s best known organizations in the delivery of Business Intelligence and Performance Management topics. His advisory experience is rooted in the practicalities of making BI and performance management work at the operational level and developing new ways of harnessing data for commercial advantage. With a doctorate in strategy he is also a regular provider of Management Education and Training. Richard Fontaine is the Vice President in the EMEA Consumer Industry. Richard has worked across various Consumer segments such as food and beverage, electronics, and various consumer goods. He has lead major company transformation programs in multinational environments as well as advisory consulting delivering significant improvements to business performance. He has driven the deployment of best practices across the full value chain: Innovation and R&D, discrete or process manufacturing, Supply Chain, Organizational Restructuring, and sales efficiency. For more information please visit Hitachi Consulting is the global management consulting and IT services business of Hitachi Ltd., a global technology leader and a catalyst of sustainable societal change. In that same spirit - and building on its technology heritage - Hitachi Consulting is a catalyst of positive business change, propelling companies ahead by enabling superior operational performance. Working within their existing processes and focusing on targeted functional challenges, we help our clients respond to dynamic global change with insight and agility. Our unique approach delivers measurable, sustainable business results and a better consulting experience.

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