This section dicusses market research, sampling methods. research methods, quantitative research, and statistical analysis, and provides a critical evaluation on each.
Direct approach to the customer is a market research approach, which is informed by both positivist and phenomenological – or quantitative and qualitative methodologies. Where the business cannot employ off-the-peg data already generated, it must obtain its own through the industry standard means of question formulation and sample targeting, followed by assessment and weighting of results.
Sampling methods must reflect the market segmentation which determines the breadth of the company’s market: this represents a number of problems for the research designer and evaluator. As Kelly observes,
‘…because of variety and complexity of the market it is extraordinarily difficult to draw valid generalizations from a small sample, that can be confidently applied to the whole.’
(Kelly, 2006: p.220).
Businesses therefore rely on statistical analyses through probability and probability distribution to offer a systematized predictive framework. In terms of consumer behaviour, continuous distribution provides the most realistic model since it describes events over a continuous range.
‘Because the results will not be known until tomorrow (or the next instant, day, month, or millennium), at the time the decision-maker makes an irrevocable resource commitment, he (she) has to decide how to cope with an unexpected outcome. We define risk as the occurrence of an outcome other than the one specified.’
(Chako; 1991 p.5).
The key consideration here lays in the term ‘irrevocable resource commitment’: product development and launch may be so costly is to represent a make-or-break investment decision for the company.
Entrepreneurial figures such as Sir Richard Branson can publicize the fact that they rely on instinct, rather than ‘…researching huge amounts of statistics’ when evaluating new opportunities. The corollary to this approach is the acceptance of a one-third failure rate amongst start-up ventures. (Branson, 2005: p.216). This is simply not an option for less flamboyant organizations in investment-intense businesses: if, for example, a third of new BMW models didn’t sell, the ramifications would be considerable.
The solution to the interpretation of statistics for complex contemporary organizations is Category Management, an outsourced business intelligence technology which relieves management of a high percentage of these tasks. Businesses involved in direct selling have always been focused on the 4 ‘P’s: price, place, promotion, and product. (Kelly, 2006: p.132). C.M. uses digital technology to present the returns on such strategy instantly.
Originally an inventory tool reflected in footfall-reflective retail Planogram outcomes, C.M. technology is now immensely sophisticated, and offers store systems, merchandise operations, and revenue management. For example, IT giants SAP offer integrated mySAP Business Suite applications such as MySAP Business Intelligence, incorporating decision-support templates constructed via a C.D.T. or Consumer Decision Tree. Using a programme like the latter, the CEO in a consumer-based business is presented with statistically tested strategy options which tell her/him exactly what customers have been doing, day by day or hour by hour. This symbiosis demonstrates how close the spheres of business and data management have now become: neither can now function fully without the other. Success through innovation requires the collateral of data-generated forecasts of market dimension, yet the ultimate decision will still lay with the CEO or president.
As well as market research, business data analysis focuses on achieving internal economies of scale within the business. For example, inefficient inventory control or supply chain management mean that products are losing money before they even reach the market. Supply chain protocols now need to reflect this, and be adapted to offer…
- Reduced Transactional Costs: what delivery of each product or service costs the business, thus determining profit margins.
- Reduced Inventory Costs: what it costs the business to have the product or service capacity ‘on the shelf’.
- Greater Environmental Sensitivity: avoiding statutory penalties or fiscal intervention by increasing sustainability.
- Increased Flexibility and Outsourcing: ensuring that service ‘bought in’ are cost efficient compared to in-house capacity.
- Greater visibility and Faster Access to Accurate Data.
Although globalisation has reduced production costs in some sectors of business, the trend to source components and services from out-of house suppliers has made the supply chain manager’s responsibilities far broader. The cost of maintaining efficient Supply Chain Management is negligible, when compared to the cost of its failure. (Heathrow’s new Terminal Five offers a clear example.) Managers need to know how a given event can affect their master schedule, and what is happening with their suppliers.
Businesses have to have a realistic idea of what it costs them to process an order, how efficient their goods in and out systems are, and how good in-house service measures are. In a large and diffuse organization, each department or system component has boundaries called ‘buffers of inventory’ which must be minimized to control costs. Every commodity in the company system represents two kinds of expense to the organization: amortized accounting costs, and decision support costs. Planners must therefore ensure that each stage in a supply chain has a clear ‘critical path’, where the process is ‘costed’ out. The other statistical tool employed here is that of the Regression Analysis, where dependant variables – foreseeable disruptions to the business supply chain – can be factored in as considerations in forecasting. As O’Mara explains, ‘Supply chain executives…are…best suited to…maintain metrics for operational and innovation excellence that can define a value network’s performance. Such metrics may be a better way to communicate with investors about future prospects and justify a higher price-to-earnings multiple.’ (O’Mara 2007: n.p.).
Any organization designing or reviewing its supply chain needs to look at the business process, consider how it could be improved, and subsequently select the technology with the most relevant applications. Businesses can now employ radio frequency identity (RFID) tags: through these, they can establish a system of unique serial numbers for each product, monitored via a network of specially deployed sensors. The alternative is a photographic identification system like Visidot from Image ID, which can track sample materials in environments where radio-frequency based systems are not feasible. From this, managers can see exactly where the ‘sinks’ are in their system, and extrapolate methods for improved supply chain visibility. Inventory control research can then be conducted via Category Management, using JDA Intactix Software. The goals of this approach are comprehensive traceability and product integrity. Through using this technology, databases can be created and expanded to reflect diffuse delivery characteristics, i.e. local, regional or national logistics points. In principle, a sampling plan sets out to compare the benefits of existing supply chain visibility, predicated on batch or lot level data, with more ‘granular data’, i.e. the ability to track single items. Item-level data can then be used to ensure that…
- Shipment is optimized towards guaranteed transportation rates.
- Acceptable order time is optimized to ensure 100% satisfactory order cycle time to customers.
- The only ‘sinks’ in the system are final distribution points to customers via retail and service operations.
To realize this, the business must move away from only monitoring functional or input costs, and establish a bilateral system which also takes into account flow and output costs.
These cumulative tools for business data collation and analysis will need to grow ever more sophisticated, but ultimately cannot relieve management of the central responsibilities of strategic decision-making. The latter will still require the qualification of business opportunities through understanding of customer needs: failure to plan will still represent planning to fail. It will still be necessary to answer fundamental questions through judgement calls, such as, is there an opportunity? Is it possible to compete for it? Can it be won….and is it worth winning? Initial screening, formalised screening, business analysis and development will still need to be as painstaking as ever, and managements who are too eager to get to market with a new product will still be vulnerable to failure.