Lately, there has been spate of reports regarding the lacklustre results by specialty and fashion retailers. The summer merchandise that did really well last year did not do so well this year. How did this happen? Amongst a number of factors attributed to this poor performance, one factor seems to be quite prominent – the unexpectedly cold and wet weather. As a result, hot weather merchandise such as swimwear did not sell. So how could the merchandise planners get it so wrong?
To be fair, they did not get it wrong. The planning is largely driven by historical data or past performance. So if the last season’s results indicate superb performance of hot weather merchandise then it is logical to plan and order more of those winners for the next season. However, not being able to take into consideration unusual weather patterns could have costly consequences as these retailers have found out.
At the end of the day, it comes down to taking into account all internal (including people, processes, products and locations) and external factors (including market, environment, competition and regulations) that may impact the outcome. But how do we determine which factors are relevant and may have an impact? The determination of relevant factors is a non-trivial task that requires advanced analytics to wade through an ocean of information sources. The situation is made more complex because the prioritisation of these important factors in decision making is always changing.
Take the case of weather forecast. When things are normal, there is perhaps no need to give extraordinary importance to weather data except to account for geographic differences such as warmer weather in Perth vs. cooler weather in Melbourne. But when things change, as they did this summer, the importance of weather data takes a whole new meaning. The normal weather conditions for last season can not be taken for granted for the next season. All of a sudden, the planning process needs to give lot more weight to long range forecasting data for the sensitivity analyses and planning iterations.
Interestingly enough, there were warnings of change in weather patterns quite a while back. As early as June 2010, Bureau of Meteorology’s La Nina Forecast had warned about strong likelihood of colder and wetter weather for the next summer. Other BOM long range forecasts have issued warnings even earlier, but this one was quite specific. By late September, the negative impact of La Nina on a number of industries, seasonal merchandise retailers being the most prominent, was a serious issue as discussed here and here. Granted, it was a bit late as the planning process would have already commenced but such strong inputs not only could have helped with later iterations of the plan, they could also help in many other ways to minimise the losses. For example, by using supply chain intelligence, plans could be made in time to adjust the assortment by sending larger quantities of warmer merchandise to stores in warmer climates and the other way around for merchandise for colder climate stores. Similarly, retailers could minimise their losses of clearance merchandise by using markdown optimisation analytics to plan ahead and get the maximum value for merchandise on clearance.
Analytical solutions such as markdown optimisation or supply chain intelligence would move an organisation from being reactive to proactive. Of course these solutions are not limited to solving merchandise planning challenges only. Pretty much all areas of business can make proactive, high value business decisions by leveraging the power of advanced analytics. You are no longer at the mercy of factors beyond your control. You now have the tools to better understand the internal and external factors impacting your business and take action to your advantage with agility.