For years, supply chain managers and specialists have been striving for more and more supply chain transparency. However, the current dynamics require something much more important: predictability.
Internet of Things
In itself, the drive for transparency makes sense. Insight into events in the supply chain, and especially real time, offers the possibility of earlier and better intervention. But the downside remains reacting to events that have already taken placenden. Eliminating structural contraction errors remainsft important but in these timesOf IoT, this is no longer enough.
Developments are happening so fast that reactive behaviour is immediately punished. Correcting your inventory levels is, by the timehaving its effect throughout your supply chain, makes less and less sense. Where once the sway effects were still insightful, it is now a succession of waves washing up on your 'supply chain beach'.
Inventory increases
Dhe digital supply chain with a consumer who is changing his preferences and needs ever faster - pushing lead times even further apart - many companies unintentionally still lead to more inventories in the chain. Despite frantic attempts by supply chain manager to lower this, according to the State of Logistics Report 2016.
The reasons for this are diverse; shorter lead times and the fact that consumers exercise their demand both physically and digitally. The increasing diversity of demand byConsumers are forcing companies to keep a wider assortment. Also, the multichannel concept still leads to problems and cross-stock optimisation, due to different service levels, is difficult to achieve. Traditional retailers diefear for their market position, increase their assortment and inventory in order to keep serving customers above all. Lastly, many companies have limited insight into their supply chain because it is often too long, up to 7 tiers. Transparency then becomes an impossible task and control of the supply chain lags behind. In short, many problems have a human-induced buffer, planning is still too often based on averages, statistical lead times, ERP systems that are difficult toadapt, making every response reactive by definition.
Dynamic predictability
The most important factors in any supply chain are lead time, variability, cost, capacity and buffers. The development of these factors in your supply chain should be continuously monitored, understood and predicted in order to continue to meet your service levels.
However, countless factors influence this and even beyond. The economic situation of citizens, social media, the whonour, price developments, traffic jams,climate disruptions, etc.
Traditional transparency largely ignores these signals and most Control Towers still lack the capabilities to turn such big data into actionable supply chain information. The big challenge is what information is relevant, what are the correlations, what are the actual causal relationships. The problem with big data is what it is: big data.
Cinema prediction
During one of my projects at a cinema organisation, the question was whether it was possible to predict which attendance figures a film would achieve, on which days and at which times. The underlying goal is better matching visitor numbers at specific times in relation to theatre capacity and sales before the start of the film and during the intermission (stock issue).
A university group of econometricians had analysed a number of years of visitors and tried to relate them to weather, season, promotional activities, time of show, type of visitor group, verbal advertising and competitive films (in own cinema or third parties).
In the end, there appeared to be no single conclusion. Additional research by my project team revealed new elements: the success of the film in the US, the time of the film's release and similar films in the same time period.
Example: films that are successful in the US also turn out to be among the top 10 in the Netherlands (in virtually the same order) in more than 80 per cent of cases. As the total number of visitors has been fairly stable for years, this could be calculated well backwards. If two similar films are shown in a certain period (quarter), they 'eat up' each other's visitors. Pre-sales of tickets provide direct insight into demand, but in the case of box office sales, people appear to make last-minute decisions.
What the above real example shows is that there is a huge amount of information available, but you have to watch out for false correlations. Do not hesitate to look deeper until you have your finger on the rightto place.
Analysis and statistics
The big problem with big data is the amount of data that humans can barely analyse, let alone draw the right conclusions. It requires sophisticated algorithms and artificial intelligentie to analyse large amounts of information from social media, external and internal conditions, make the right connections and feed your ERP or other systems with the results.
Organisations like Google and Facebook already take this approachTo best serve their digital customers. To offer customer-specific information at the right time and place. From advertisements to relevant media articles. Now the challenge also lies with supply chain managers. Your future is not transparenteBut predictability.
Hans Groen - Consultant Supply Chain and Performance Management
