In a world in which everything and everyone is connected, in which the amount of data generated is growing faster every day and in which lifecycles of products become shorter and shorter, the need to be able to make smarter decisions is rising fast. Maybe in logistics this need is felt the most as logistics is vital in every supply chain. It requires the logistics manager to be well equipped for making though decisions. It’s my strong belief that access to data analysis, predictive and prescriptive analytics will become crucial for every manager, for the logistics manager in particular. For managers to be able to reap the benefits of analytics they need to invest in analytical capabilities and analytical software. Tools and capabilities however are not enough to turn data in to actionable insights, decision making processes need to become data driven and analytical as well, which requires the corporate cultural to change.
Many decision makers agree that the ever growing mountains of data contain huge potential value. A recent study on supply chain trends by BVL International shows that 60% of the respondents have plans to invest in data analytics in the next 5 years. From my own experience I can tell that analysing data, big or small, always adds value. In the past years I have worked with a lot of logistics companies. Each time the analysis of data guided the path to, sometimes very significant, improvements. Data analytics helps to understand the current situation, to identify bottlenecks and find ways to bypass them. Data analytics supports operational decision making in last mile distribution, but also in tactical and strategic decision making. I can’t imagine determining the best frequency of delivery, find the best location and size of distribution centres or determine an integration plan of networks without the use of data and analytics. Zooming in on the last mile distribution, it’s one of the most expensive parts of a logistics chain; it for example accounts for about 35% of the operational cost for a parcel delivery company. If it can be done smarter, this will directly improve margin. UPS estimates that when they save one mile per driver per day, that would save them $50 million a year (see interview with Jack Levis). That’s why UPS invests heavily in gathering and analysing data to continuously improve last mile delivery operations. Their famous business rule to take as much right turns as possible is a result of the analysis of waiting and driving times of thousands of routes driven by their delivery vans.
Advanced analytics can combine various data sources, data from last year, last week, up to real time information to forecast what is going to happen next. For example, the expected amount of freight that will arrive tonight or in the next few days in a distribution centre. With this information it can be analysed upfront whether the expected volumes can be managed in the network or that bottlenecks will occur. In the latter case preventive actions can be undertaken, like redirecting the freight if there is insufficient transportation capacity preventing the load to be left behind. The forecasted volume can also be used the estimate the required workforce to make sure the right people are available at the right time in the distribution centre to handle the arriving freight. By integrating real time traffic information with the location and availability of customers a more efficient last mile distribution can be achieved, reducing the number of negative stops. These are just a few examples that show the value of analytics in logistics and how it will support cost reductions and enhance customer service. Funny thing is that the data generated in the execution of the logistics processes can also create value, as it could be sold to the local government to analyse distribution flows in the city or to a market research company. This secondary use of data is something I think the logistics sector could do more with.
Data analysis is not new to logistics companies. Many of them analyse data to know the utilisation of their assets, the volumes transported and delivered on time, the miles used to achieve that including the associated cost. Many of them use dashboards to keep track of their performance indicators, but to me it’s like looking in the rearview mirror while trying to move forward. Of course analysing past performance is required, nut it is not enough. Logistics companies should analyse data to provide forward looking insights using forecasts on volume, fuel price, available manpower and assets. This will provide insights on what the performance indicators are going to look like. With these insights and analytics tools companies can anticipate and optimise their operations, making better decisions. Researchfrom Andrew McAfee and Erik Brynjolfsson of MIT shows that companies that use data and advanced analytics have a 5%-6% higher productivity and profitability compared to organisations that do not. The reported level of improvements is also what I experience in the projects I have done; sometimes even higher improvement levels can be achieved, certainly when the decision is on a tactical or strategic level.
To be able to reap the benefits from analytics logistics companies need to change their decision making processes which are mostly local oriented, intuitive, and have grown from habit. This needs to change into a supply chain wide decision making process and needs to become data driven. Senior management must fully support this change but will only do that if applying analytics results in performance improvements in a repetitive and consistent manner. Making the move towards a data driven and analytical way of decision making takes time. It’s my experience that the best results are achieved not by a big bang approach but by gradually increasing the complexity of the analytics projects. This will consistently improve decision making quality, achieving better results every time. The pace at which to grow in analytical maturity is depended on the rate at which a company can or wants to grow. This is exactly the route TNT Express took, delivering them multi million cost savings. One of the key enablers for TNT Express, which I expect will also be the case for other logistics companies, is to invest in analytics capabilities of the company. Not to make a mathematician of every employee, but to train them to recognise optimisation opportunities and learn how to apply analytical methods.
To improve on their decision making, logistics companies need to become more analytical. The environment in which they operate requires them to do so. There is much to be gained from the data that is available, but current practice is that it is used to create a backward looking view on the company’s performance. The real value that lies enclosed in data will become available if logistics companies start to use forward looking analytical techniques (predictive analytics) providing them the insights to anticipate and optimise their decisions (prescriptive analytics). Prerequisites for success are improving the analytical capabilities of the company and even more important grow a corporate culture that stimulates continuous improvement, measuring performance and evaluating decisions with quantitative evidence.
This blog entry is a summary of the lecture I held at the Election of the Logistics Manager of the Year March 27th 2014