Re-thinking Demand Management

Most companies in the high-tech industry have historically focused on enterprise markets and are in the process of reinventing themselves with an enlarged focus to include consumer-oriented businesses. Companies such as HP, Motorola, Apple, Cisco and others are also addressing markets that are much more global in nature, i.e. customer constituencies that are significantly differentiated by virtue of their regional or cultural context.

While the conquest for global markets is truly under way, the customers within those markets are also changing in a profound manner. Wave after wave of product launches has led to consumerization, and along with it customer choice (fickleness), lowered demand predictability and swift migrations of entire customer bases from one product to another, as well as intense cross-category competition.

In addition, within traditional customer segments, company initiatives in response to the onset of aggressive consumerization are typically focused on "customer responsiveness" and have often had the unintended fallout of creating higher volatility in the demand of high-tech products. In the context of effective demand management, there is a significant opportunity for differentiation through process innovation and systems leverage in customer operations. The Electronics Supply Chain Association and Infosys Technologies Limited conducted primary research (1) with the following objectives:

  • Assessing impact of consumerization on major sub-segments within the high-tech industry
  • Understanding issues and challenges in key processes within demand management
  • Identifying leading industry practices and identifying components of an integrated model for effective demand management

Key Findings

Evidence of Change & Impact of Consumerization on Operations

When questioned, 87 percent of respondents in the primary research (1) stated that consumerization has significantly impacted two key areas within operations:

  • Product Proliferation: Increased pressure on design and supply cycles throughout the high-tech demand chain, driven by demand for new products and increased product refresh frequency
  • Customer Experience: Heightened customer expectations on product customization and error-free operating performance — on-time delivery; seamlessness across product lines/lines of business and functions (Sales, Service, Support); hardware-software-service-financing options bundling customizations; product configurability; self-service capabilities etc.

Most line managers within the supply chain recognized the impact and articulated their recognition and responses accordingly, as shown in Figure 1:

Figure 1: Business metrics impacted by consumerization. Source: Infosys-ESCA Research (1)

In customer-facing operations, the impact has been strongly manifested through customers' expectations around reduction in lead-time-to-deliver and close-to-perfect order fill rates. On the other hand, the shift in perception around such high-tech products as short-lived fashion and trend-oriented products emphasizes revenue growth for companies through rapid new product introductions (e.g. Logitech, which launched over 70 new products last year alone) rather than relying on a loyal customer base for stable products. This has induced a steep increase in design turns and significant shrinkage in the concept to market launch time window.

To help focus the identification of the current state in operations as well as leading practices, one of the focus areas of the research was on a specific process of demand management: forecasting.

Forecasting: widely prevalent but still not trusted

Despite enormous efforts to induce predictability within the customer order cycle, firm orders comprise only 30 to 50 percent of anticipated sales (1). Meanwhile there is also an inexorable rise in the proportion of change orders. Additionally, on the supply side, there are longer lead times and more points of failure associated with orchestrating fulfillment through a global network of contract manufacturers, component suppliers, assembly operators and third-party service providers. It was no surprise, therefore, that all respondents (1), regardless of their approach to demand management (signal based or predictive), mentioned that they consider forecasting as a critical function and starting point of the operations planning cycle.

Table 1: Prevalence of Forecasting

Current State of Forecasting: Process, Data and Tools

Figure 2: Inputs considered for forecasting Source: Primary Research (1)

Point-of-sale (POS) data is considered to be the most potentially useful information of past sales information. However, in discussions it was determined that there are significant obstacles to actually leveraging POS data — for instance, lack of availability of standardized POS data formats across countries entails manual aggregation for a complete view of sales. Another significant issue is the difficulty in incorporating available data into sales and operations planning (S&OP) heuristics.

Most companies prefer to have dedicated sales personnel at the customer site to have better visibility into forecast updates and other demand-side dynamics. "We keep an eye on Stocking Representatives i.e. intermediate agents between us and our customers' shelves, to figure out how much the expected demand could be," says a chip producer. A mobile phone manufacturer mentioned that the company has a specialist in marketing/demand management who looks beyond the service provider to the retailer and works with them directly.

Suppliers to original equipment manufacturers (OEMs) tend to believe that OEMs over-forecast to avoid market risks and so consider validating the OEMs' forecast outputs with information on market potential and industry trends provided by secondary research and industry journals. For example, a contract manufacturer mentions: "OEMs themselves have no visibility. We simply cannot rely on the forecast data received from the OEMs. Rather, we look at the demand pattern of our customers' customers, market potential and emerging trends, what applications are emerging in the market, and the way our customers would react to these trends."

Figure 3: Prevalence of Forecasting Tools Source: Primary Research (1)

Most companies [around 68 percent (1)] still rely on basic tools for forecasting. Although a few respondents indicated that they deployed tools like Manugistics, i2 Demand Planner or specific forecasting modules of leading enterprise resource planning (ERP) applications, tools like MS-Excel or homegrown applications continue to be a favored choice to help arrive at forecasts. On probing, two primary reasons were identified:

  • Raw data is simply not adequate for sophisticated tools to provide a robust quantitative estimate. To remedy this, human judgment plays a significant role, and manipulations based on rough estimates can be done easily with basic tools
  • Ease of use and wide acceptability of simpler applications and homegrown tools (particularly relevant in a collaborative supply chain context).

Forecast Performance: Key challenges and recommendations

The research indicated that around 70 percent of the respondents do not consider their performance in forecasting as satisfactory.

Figure 4: Forecast Performance Source: Primary Research (1)

The underlying issue, however, is not the lack of tools deployed for supporting the forecasting function. The issues primarily stem from being unable to collect "desired" data and also being unable to institutionalize a formal process of collating and utilizing input data/information to arrive at forecasts. Key challenges identified:

  • Poor quality of data. Associated issues: Lack of synchronization of nomenclature for a product or part number followed by the company and its customers/suppliers. Mismatch in granularity of the expected demand data provided by customers and that is required by the company. Over-forecasting by partners. For global operations, POS data is not available across all countries.
  • Lack of formal processes. Associated issues: Lack of formal processes for measuring forecast performance and feedback generation on previous period's forecasts into the current period's estimates. From the survey, 76 percent of respondents indicated an absence of a formal process measuring forecast accuracy. Eighty percent didn't have any mechanisms to capture previous period's forecast accuracy feedback into the current period's forecast. Inability to filter and apply qualitative information (e.g. market conditions, supplier dislocations, intensified competitor activity) to supplement quantitative inputs for historical performance. Forecasting is primarily only short-term focused.
  • Forecasting tools are not leveraged fully. Associated issues: Poor integration of forecasting tools to transaction data source. Data inputs are not considered adequate for sophisticated tools. Data exchange is manual. Fax, phone and e-mail are still a popular method to exchange information. A resistance by users to switch from simpler applications and homegrown tools to sophisticated forecasting tools.

RECOMMENDATIONS

Forecasting: Define, Enable, Measure

Based on the preceding research and discussion with companies, it is evident that working effectively in uncertain times requires a robust operating model that integrates key processes and systems on the demand-side of the business. These are typically siloed processes that address sales forecasting, marketing campaign management, channel management, etc.

Figure 5: Shift from passive/reactive to active/predictive demand management

There is a need to shift from passive/reactive demand management to demand management that is more active/predictive:

  • Sense the demand of customers early and correctly
  • Influence the demand to favorably align it to capability
  • Budget for variability in demand during fulfillment responsiveness
  • Focus on innovation to realize first mover advantage during the short life cycle of the product.

The following operational steps could possibly form a starting point to evolve functional silos and fragmented processes to active/predictive demand management:

Streamline Information Gathering and Analysis

  • Streamline the master data of products, customers and suppliers
  • Establish the mapping of unique product definitions to that of customers and suppliers
  • Establish a correct data hierarchy to ensure the uniform roll up of forecast information across customer segments, product part numbers and geographies
  • Determine the approach on governance of data to be followed uniformly across the company. This includes streamlining sources for data imports, methods of data collection, data cleansing and data finalization guidelines
  • Define methods and rules to utilize informal, qualitative information to supplement quantitative information
  • Design and implement information exchange mechanism based on above need
  • Evaluate options of reward/penalty mechanisms with customers based on accuracy and comprehensiveness of forecast data

Formalize Forecasting Processes

  • Identify the forecasting parameters, such as forecast granularity, forecast frequency, forecast horizon, etc., for different product families and customer segments that should be followed uniformly by different departments of the organizations.
  • Define processes for:
    — centralization of input data obtained from different sources
    — periodic refresh and leveraging of customer segmentation during analysis of forecast inputs
  • Establish guidelines that promote multi-tier visibility of forecast information. Identify the recipients of the information and information sharing process
  • Define processes and methods to analyze the accuracy of previous forecast based on current data.
  • Institutionalize specific set of metrics across the organization to report forecast performance
  • Review previous forecasts to validate the forecast rules and assumptions
  • Establish the parameters for reward against forecast performance

Leverage Forecasting Tools

  • Conduct maturity analysis of the business processes and requirements to ensure the correct forecasting tool is selected
  • Assess data readiness in accepting the new tool
  • Build a robust information mechanism to automate data capture
  • Formalize a process of acceptance of the new forecasting tool by the users through structured change management programs. Support user transition through adequate training. Institutionalize appropriate rewards for transition

Leverage Demand Shaping Opportunities

Demand shaping, though selectively practiced, can be a crucial lever in improving Demand-Supply balance. A few guidelines for more effective demand shaping:

  • Emphasize proactive demand shaping based on analysis of the market and sensing the market potential. Design the demand shaping effort accordingly to realize the maximum potential benefit
  • Formalize the processes for measuring results of demand shaping efforts and taking systematic improvement steps based on the feedback. While measuring the impact of demand shaping, take care to ensure that the impact of seasonality and other external factors is factored out separately
  • Emphasize a unified customer experience to ensure that different touch points of the same customer have a uniform experience of the demand shaping parameters deployed
  • Equip customer facing teams with adequate information (product information, supply capability etc.) for effective and immediate decisions in shaping demand
  • Leverage customer segmentation to tailor demand shaping techniques to customer segments during a specific time period. If needed, reconsider customer segmentation criteria based on experience with the impact of demand shaping over a specific period of time

Collaborate within and beyond the organization boundary: Share the risk

It is not uncommon to see companies struggling to align order requirements submitted by sales, the supply capabilities and utilization optimization targets of their manufacturing units, the inventory targets of chief financial officers, and long-range forecasts from marketing. The first order of business from a collaboration perspective should be intra-company collaboration and planning, and then subsequently the extension of the collaboration arm to partners.

Short and long-term objectives of collaboration should be set outright. Short-term objectives include improving the trust factor and improving the accuracy and speed of information shared. The long-term perspective establishes the true essence of collaboration — a move from information sharing to risk sharing as trusted partners. Prominent examples are the semiconductor manufacturers who would under-invest in capacities if they are to bear the risk of idle capacity. This in turn impacts the buyers, since they don't get the product when they experience a higher demand from their customers. In the risk sharing strategy, the buyer might choose to purchase capacity options well ahead in time but exercise options only after observing demand. Since a part of the capacity risk is borne by the buyer, the semiconductor manufacturer is also encouraged for higher investment in capacity.

References

Primary Research: Conducted by Infosys Technologies Limited jointly with the Electronics Supply Chain Association (ESCA), the research involved interviews with 30 high-tech companies in the United States. Areas explored include practices on Demand Sensing, Demand Shaping and Demand Fulfillment. Respondent companies were categorized as Semicon Group (Semiconductor Manufacturer, Component Manufacturers), EMS Chain (Component Distributors, Contract Manufacturers) and OEM & Resellers (OEMs, Distributors and Retailers).

About the Authors: Romit Dey is a partner with Infosys Consulting, with specific focus on Customer Operations and Product Innovation. Over the last 12 years Dey has worked with clients on functional strategy, operations improvement and package-led business transformation initiatives across United States, Europe and India in consumer electronics, contract manufacturing, OEMs and distributor companies. He can be reached at romit_dey@infosys.com. Joy Prakash Somani is a senior principal with Infosys Consulting. Somani has consulted for several companies in discrete manufacturing and high tech industries in the United States and Canada in the areas of Supply Chain and Customer Operations. He can be reached at joyprakash_somani@infosys.com.

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