Better decisions – is more and better data the answer?

Roger OakdenLogistics Management, Procurement, Supply Chains & Supply NetworksLeave a Comment

Make a decision

Data and decisions.

Put more and ‘better’ Supply Chain data in the hands of decision makers – does it result in better decisions? In articles written about big data, internet of things and other such technology advances, there appears to be an underlying acceptance that the hypothesis is proven  – but is this correct?

The question was asked in relation to data collection, analysis and distribution among member companies of the Sustainable Apparel Coalition (SAC). This organisation, which incorporates more than 40 percent of the world’s apparel industry (retailers, brands and suppliers) is attempting to improve apparel Supply Chains through measuring the environmental, social and labour impact of making clothing and footwear.

The data collection and analysis process uses the Higgs Index, which incorporates three online tools to measure brands, facilities and products. The tools ask questions from basic to advanced level, which are analysed and compared with the anonymous peer group; this approach encourages members to actively look for improvements in their Supply Chains. The tools can be downloaded and used, possibly adapted or to demonstrate an approach in your organisation.

However, the quantity and quality of data is but one aspect of an effective decision. Because managers in Supply Chains  relate to many individuals and groups within and outside the organisation, decisions can have far reaching effects.  It is therefore important to have an understanding of what is influencing a particular decision – is it your own intuition or other factors.

Influencing decisions

The sum of ‘right’ decisions made and implemented affects the value that investors put on a business or other type of organisation. But ‘right’ decisions are influenced by the individual manager and the organisation.

  1. The individual

If a manager within a Supply Chain function is faced with a potential loss, gain or apparent ‘lucky break’, they can become emotional and may not think clearly. However, repeat these seemingly unpredictable emotions over time and the manager believes they can rationally justify their ‘predictable’ decisions. This is called ‘expectation bias’ – the personal belief that a decision is correct, without evidence, analysis or testing.

Other personal factors that can affect decisions are:

Emotional bias – make decisions for which the manager feels good, but ignore situations where the outcome from a decision could be ‘bad’

Selective effort – micro manages and over-analyses profitable operations, but overlooks poor performing areas in the hope they will ‘come good’ over time

2. Organisation structure

The effectiveness of decisions will be influenced by the corporate structure. A ‘good’ structure assists directors and managers to make and implement key decisions in a ‘better’ (and usually faster) manner than competitors. Questions that may help the potential quality of Supply Chain decisions are:

  • What critical decisions must be made within the Supply Chains?
  • Where in the organisation should these decisions be made?
  • What authority do the decision makers need?
    • How will managers be equipped to quickly make ‘good’ decisions
    • What decision making IT assistance is required?
    • What Logistics infrastructure is required to assist implementation of the decision?
  • Identify other parts of the organisation that may need structural adjustment to support ‘good’ Supply Chain decisions and implementation

3. Organisation culture (how we do things around here)

Peer pressure – to gain approval, the manger’s decision making process is the same as their peers. This can result in ‘group think’ and the ‘corporate way’

Bandwagon effect – a decision is correct because ‘everyone is doing it’. This is often relevant in decisions about buying technology solutions

4. Criticality of the decision (how will the decision affect my current job and career?)

Career limiting decision – example is an analysis that identifies sales will decrease, but lead times and inventory will increase; money is required for additional warehouse space

Sunk costs influence – in a bad situation, a decision will be influenced by the amount of losses already incurred

Loss aversion – avoiding a loss  rather than making a gain is likely to have a higher value

5. The analysis

This factor references who will do, or has done, the analysis concerning the organisation’s Supply Network? Confidence in the analysis will condition support for decisions by the affected managers. For example; will the analysis to be done by accountants using standard cost accounting procedures on available financial figures, or by supply chain professionals using quantitative tools to identify the total cost from data that is not readily available from current ERP systems?

Additional confidence will be based on the manager’s personal agreement with the approach to analysis:

  • A linear approach – divide the problem into component parts and analyse for rational solutions
  • An intuitive approach – identify patterns and relationships in the data between the stated problem and the context, including any contradictions

These five influences on decisions illustrate that more and better data will not automatically lead to better decisions. Human behaviour means that when making decisions, managers will make mistakes, have biases and distortions; therefore be aware of the situation and access unbiased advice to improve your decision making capabilities.


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About the Author

Roger Oakden

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With my background as a practitioner, consultant and educator, I am uniquely qualified to provide practical learning in supply chains and logistics. I have co-authored a book on these subjects, published by McGraw-Hill. As the program Manager at RMIT University in Melbourne, Australia, I developed and presented the largest supply chain post-graduate program in the Asia Pacific region, with centres in Melbourne, Singapore and Hong Kong. Read More...

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