Could a Brexit bullwhip cause turmoil in European industrial production?

Last week, the UK newspaper Guardian reported that in the UK companies have started massive stockpiling. While this is seen as a measure to counter uncertainties around a potential hard Brexit, little has been said about the additional production needed to pile up this inventory and the effects of a substantial decrease in production next year once the inventories are sold off. Linking such active inventory decisions to supply chain understanding can only tell us one thing: even a small inventory adjustment may lead to years of instability across European supply chains.

In the Guardian article, “industry representatives” are quoted to have said: “Frozen and chilled food warehouses, storing everything from garden peas to half-cooked supermarket bread and cold-store potatoes are fully booked for the next six months, with customers being turned away”. Not just fresh and frozen food is being stockpiled. British manufacturers are also storing ingredients. UK food manufacturer Premier Food announced two weeks ago that it is building up about 10 million pounds’ worth of ingredients inventory, and British Tobacco company Imperial Brands said it would add about 30 million pounds worth of inventory.

Of course, this is good business for those leasing out warehouse property. Apparently, in many places in Southern England it is very difficult to even find qualified food-grade warehouse space. Seems like a good opportunity to bring over some reefer containers to the island as my bet is that these may be leased at a premium in the upcoming months.

However, an aspect of stockpiling that has not received any attention in all of this is the bullwhip effect that it may cause in European industrial production. Exactly ten years ago, we conducted research that showed that the sudden decline in inventories caused by the collapse of Lehman Brothers led to massive global fluctuations in industrial output for many years. This so-called “bullwhip effect” was well-known in the literature and to anyone operating a supply chain, but common knowledge was that this was exclusively caused by demand fluctuations. Our work demonstrated that sudden, coordinated inventory adjustments would cause such fluctuations as well. I believe that this is what we will also see as a consequence of Brexit: the Brexit bullwhip.

What is a Brexit bullwhip?

I believe a Brexit-bullwhip could be facing us. The reasoning would be as follows:

  1. Inventories of raw materials, intermediate products, and consumer products are all built up in the UK over a relatively short period of time. (Note: this is not just happening in the UK, as UK exporters will build up inventories on the continent, but I have left this out of scope in my analysis for now).
  2. These inventories need to be produced; this leads to additional industrial production for those products sourced from the EU.
  3. Manufacturers observe an increase in demand for their products, and hence also order more supplies from their suppliers. This surge in demand propagates upstream in the supply chain. Since supply chains are long and not very transparent, these suppliers are unlikely to relate their increase in orders to stockpiling in the UK.
  4. At some point, the stockpiled inventory will reach their targeted level. Orders will go back to their “normal” level. It takes some time, however, before the supply chain adjusts. Cumulatively, across manufacturers, their suppliers, and again their suppliers, inventories could easily amount to a year’s worth of sales. As a consequence, manufacturers further upstream in the supply chain may only feel the consequences of the original stockpiling decisions many months later. Just like if you turn up the heating in your house; it takes some time for the system to respond and you may overheat your house.
  5. Some time next year or in 2020, hopefully supply chains will have been adjusted to post-Brexit border controls and inventories will start to be reduced again. This will further amplify the Brexit bullwhip, leading to a huge decline in industrial production.

How large could the Brexit bullwhip be?

This is not easy to estimate. Inventory records in national statistics are not very good. Also, the complex supply chain relationships are not captured in detailed statistics. But we should be able to make a first-order estimate of the effect.

First we estimate UK imports of (physical) goods from the EU27 to be about 300 billion euros annually (1) with corresponding inventory levels to be at about 6 weeks across the board (2). This would value inventory related to UK imports from the EU at around 34 billion euros.

Second, we estimate how much additional inventory is likely to be built up as part of the stockpiling process. For this, we can only rely on anecdotal evidence from the various newspaper reports, suggesting that this is an additional month of inventory, so about 25 billion euros of additional imports from the EU, most of them likely to be purchased in this quarter (Q4 2018).

Next we estimate total European industrial production, including the processing of agricultural products. Based on (3), we will work with a figure of about 2,500 billion euros.

Assuming these numbers are more or less correct, this implies that the inventory build-up in the UK would be 1% of European industrial production. Since virtually all of this would have been produced in the last quarter of 2018, the last quarter would then see an additional production of about 4%. That would be a massive number.

Note that none of this includes an additional bullwhip of overreaction. Our studies of the 2008 financial crisis suggest significant overreaction since it is not clear to companies further upstream in the supply chain what causes the increase in demand.

A huge drop in 2018?

If this were all more or less true, the rebound on the bullwhip in 2019 could be huge, with drops in import figures that would go well beyond the growth in the current quarter.

Obviously, this is an effect analyzed in isolation, and without any modeling at this stage. There are many other effects surrounding the Brexit which I am sure are receiving extensive analysis by economists in the UK, EU and elsewhere. Just today, the Bank of England released such an analysis. The Brexit bullwhip however deserves to be part of this analysis. If anyone has better numbers, I am happy to adjust my calculations accordingly.

Updates and comments after publication

(29 Nov 2018) Gaston Cedillo of the Mexican Institute of Transportation shared an interesting study where they studied the consequences of the variability at the US-Mexican Border. They show that additional safety stock will be needed to deal with the varaibility of the border process itself.

(29 Nov 2018) I received information that the demand for ADR (dangerous chemicals) containers has been increasing significantly. This would suggest that also further upstream in the supply chain (chemicals are typically upstream), companies start preparing for adding inventory.

(6 December 2018) The London Evening Standard today published an article based on this blog

(2 January 2019) The Netherland PMI, after slowly sliding in the past months, suddenly revamped in December. It is attributed to higher exports to the UK, indicating stockpiling.

(2 January 2019) The Guardian today published an article that also British manufacturing is working at full capacity to help build inventories ahead of the Brexit.

Sources and disclaimer

For the order-of-magnitude analysis, I have received assistance from my valued colleague Alan McKinnon. We rely on publicly available sources and realize our estimates may not be very accurate. However, even if we are 50% off, the Brexit bullwhip will be large. If you have any further or better data that we can easily incorporate, please get in touch. I thank Alan for his help; of course, all errors remain mine.

(1)   The source my colleague Alan McKinnon helped me use for this data is aParliamentary briefing document on UK – EU trade. This gives a figure of £341 bn for UK imports of goods and services from the EU in 2017 – at current exchange rate around €390 bn.  The briefing report does not give a breakdown of goods and services. According to a BBC report the value of service imports from the EU was £81.2 bn in 2016 or €92 bn. Assuming this value did not change much between 2016 and 2017 €300 bn might be a reasonable estimate for the value of goods imports.

(2)   Total value of product sales by UK manufacturers was £385bn in 2017 ( while the value of physical inventory in manufacturing industries (analysis by Alan McKinnon based on British statistics) (in current prices) at the end of that calendar year was £62.4bn – i.e. 16.2% of sales – average inventory rotation of 6.2 annually. This is about 2 months worth of inventory. Retail inventory may be a bit less, but is unlikely to be less than one month of inventory currently. Hence, we assume 1.5 months of inventory on average.

(3)   Eurostat suggest that EU GDP was €15373 bn in 2017 (in current prices) of which the UK contributed €2332 billion (15.2%). Removing the UK, reduces EU GDP to €13040 bn. Manufacturing represents around 16% of EU GDP, which using the Eurostat GDP figure for 2017, would yield a figure of €2460 bn. I am not sure at this stage what exactly is included in “industrial production” and whether this covers all of the producing sectors to export to the UK. At the same time, it definitely won’t include all of the wholesalers and traders in between, that may further aggregate the bullwhip. For now, we’ll work with the 2500 bn.

Disclaimer: the current analysis is not based on peer-reviewed research specifically focused on Brexit or Brexit-based numbers. We rely on simple order-of-magnitude calculations as a contribution to the debate.

Note: This blog appeared on LinkedIn on 28 November 2018

Ever thought about your supply chain’s water risk? It could harm your business unexpectedly.

Industrial production in the Indian State of Maharashtra, where Mumbai is located, got a major hit in 2016 when water supplies were short. In the same year, about half of the manufacturing companies in the Bolivian city of Cochabamba were affected by water shortages, leading to a decrease of 15% in industrial output. Last year, India’s apparel industry was heavily affected by water shortages, and this year even Cadbury and Jaguar in the UK had to temporarily close down their factories due to freshwater supply problems.

Water shortages are commonly seen to affect agricultural production and household water supply in underdeveloped parts of this world. Due to the globalization of sourcing, also manufacturing companies are increasingly faced with problems due to water shortages somewhere upstream in their supply chain. While supply disruptions due to natural disasters or terrorism have received substantial attention and more and more companies have been mapping their risk, few companies have realized that they may have a little time-bomb ticking at one of their (maybe far-away) upstream suppliers. A new research article in the Journal of Cleaner Production (“Water Risk Assessment in Supply Chains” – free download until 9 December 2018 at this link; after that, please contact us to request a copy) by my co-authors Torben SchaeferMaximiliano UdenioShannon Quinn and myself sheds light on this risk and provides a methodology for companies to take action in mapping their supply chain’s water risk.

What is water stress?

The availability of clean water is one of the most important sustainability challenges we are facing today. It is a challenge that is expected to increase in the future; and yet, its visibility by supply chain managers community is limited. An area is said to be experiencing water stress when the amount of clean water available is smaller than the amount of clean water required. Clean water is a precious resource. Civilian populations need clean water to live, agriculture and farming need water to produce our food, and virtually every industrial process needs clean water to function. However, even though clean water is a universal requirement, in the face of its unavailability the response is clear: the priority for securing access to clean water will always be for civilian populations and food production. There are numerous examples in recent years where governments divert limited water resources such that water is allocated to civilians. In such cases, industries must continue without water, or shut down. In a globalized world, production in any particular location often depends on raw materials or components sourced from across the globe. Supply chains literally span the earth.

What should you do as a supply chain director?

For firms interested in reducing their exposure to water risk, this means that they must monitor the situation of their entire supply chains. If your suppliers, the suppliers of your suppliers, or the suppliers of the suppliers of your suppliers are located in an area that runs the risk of access to clean water, you run the risk of supply or production disruptions due to access problems with clean water.

In our new article we term this risk, relevant for firms across industries and across the world, water risk.

It is not easy for a company to understand your exposure to water risk: data is difficult to collect, and even when data is available, it is difficult to compare the different dimensions of the problem to come up with a metric that summarizes their risk exposure. Therefore, we developed a new index intended for companies to measure and understand the water-risk of an entire supply chain. Our risk index is composed of 6 base indicators that each measure a different dimension of either “physical” water risks (baseline water stress, seasonal variability, and drought severity) or “amplifying” water risks (external dependency ratio, governance and regulation, and infrastructure). 

Given a geographical location, our risk index quantifies the water risk using a single number, by taking into account the aforementioned components, and experts’ assessments to weigh each of these components into the single water risk indicator. Looking at the problem from a strategic perspective, our water-risk index allows firms to assess geographical areas in terms of water-risk. This allows you to, for example, compare geographical locations of potential new suppliers, or forecast the water risk of your current supplier base for the next decades.

Moreover, our risk index allows for tactical analysis of the water risk at the level of individual processes, products, or manufacturing locations. In this way, you can identify the products or processes with the highest water footprint and consequently analyze its supply chain to detect water risks.

Application at Procter & Gamble

We worked together with Procter & Gamble and applied our methodology with them. From a tactical perspective, we were able to immediately identify some suppliers of a critical raw material that are located in areas with high water risk. Moreover, the water risk in these areas is expected to increase in the coming years. From a strategic perspective, we mapped the water risk of their more than 1000 suppliers to identify the suppliers and areas with highest risk today and in the future.

Procter & Gamble have not only realized the potential of water risk assessment for the reliability of their supply chains, but also for the communities where their supply chain’s plants are located. Consequently, in its recent new sustainability strategy, specific targets have been included for reducing the company’s water footprint, and with that, likely also the company’s water risk.

Including water risk in your supply chain risk analysis is critical. The great news is that acting on this will not only reduce the vulnerability of your supply chain, but also make many communities that face water shortages a better place to live.


  1. This blog has been written with the purpose of making our research accessible, sometimes at the expense of nuance and methodological limitations. A full evaluation of our work should only be based on the peer-reviewed article itself.
  2. This blog has appeared first on LinkedIn on November 26, 2018.

The Death of Supply Chain Management – Really?

A rebirth of supply chain management

This blog was published on LinkedIn on June 19, 2018. The LinkedIn article contains all relevant links.

Yesterday the grand title “The death of supply chain management”[1] triggered my thinking, as it must have worried many that are currently working in some supply chain role. Re-reading the controversial blog on HBR a few more times, I first concluded that the title did not match the content (which is much more about a new era for the supply chain as increased algorithms and visibility come into play) but later realized that the perspective that is being sketched is different from what I think is happening. And current and past research on supply chain operations planning can help us better judge this.

Supply chain planners have decisional, informational and interpersonal roles

First, it is important to be aware how most supply chain planners are spending their time. Since many decision support tools (including Microsoft Excel as the most used tool for planning) have entered into the market over the past twenty years, the way a planner spends her time has more and more moved away from actually making decisions (their decisional role) to other key aspects of the planning role. These other aspects can be classified into two main categories [2]. The first one is informational. The informational role is about collecting relevant information that typically is not present in an ERP system. This could pertain to soft or ambiguous information (a promotion being in the pipeline but not yet decided upon, an estimate of the chance of winning a large tender, or a carnival week coming at a major supplier likely causing disruptions in supply), or simply data that are currently not linked or not visible. The second role is an interpersonal role. For instance, if a product is short on supply, a good planner will be able to call his “friends” elsewhere in the operation to speed matters up. Technically this implies that the lead-time parameter in the system may – sometimes but not too often – be expedited. Of course, this favor is kept in a mental account of the supplier’s planner. The supplier’s planner will expect this favor at some point to be returned. The interpersonal and informational roles start to overlap when others in the organization consult the planner on getting to know information about something that is not (yet) in the system. The planner usually knows best among his organization’s peers. And earlier work by my former colleague Ton de Kok has shown that even between the most advanced algorithms and the actual performance reached by planners, there is a gap – usually demonstrating the added value of the human planner in modifying the real supply chain beyond what a model can do. We have also done some work in a retail environment showing this added value of the human decision maker compared to state-of-the-art models [3]. For sure, there are many situations in which an algorithm may outperform a human, and our understanding of this is developing gradually.

Supply chain planners spend most of their time collecting, verifying, and disseminating information

As our research [4] and those of others have shown, planners spend (or “waste”- depending on the perspective) more than half of their time on the informational role. Typically they spend less than 25% (and a much lower number in manufacturing supply chains) on the decisional role: actually deciding on the plan, schedule, or replenishment. The AI impact is often presumed to have an impact on this decisional role. I would contest this for a variety of reasons not elaborated on here. The potential of big data and AI is actually in impacting the efficiency of the planner in the time she wastes on the informational role.

Interestingly, in the HBR blog, many examples refer to control towers and real-time information. In most supply chains real-time information is too late, as the information is in almost all cases only useful ahead of time when it is still possible to do something with this information. However, having more information readily available, well-searchable, and visualized in an intuitive way, brings great opportunities to increase the efficiency of the planning process. Not by eliminating the planners’ decisions, but by reducing the time they waste on collecting information.

This line of thinking is much less sexy than having all decision makers replaced by robots as is suggested by many. But currently replacing people by robots just leads to what I heard from a major global CPG manufacturer supplying to a European retailer that took its hands off the wheel. The CPG manufacturer now needs to monitor all orders to take out the “crazy” ones, call (!) their client to double check these orders, and then manually reset everything in the supply chain. This really would be the death of supply chain management.

[1] Allan Lyall, Pierre Mercier, and Stefan Gstettner (2018) The Death of Supply Chain Management, HBR blog, June 15.

[2] The actual classification of roles is more extensive, as planners typically also do maintenance tasks of master data and ensure the plan or schedule gets executed, but for the sake of simplicity I have grouped these under informational and interpersonal roles. If you are interested, read the early work by Sarah Jackson: Jackson, S., Wilson, J. R., & MacCarthy, B. L. (2004). A new model of scheduling in manufacturing: Tasks, roles, and monitoring. Human factors, 46(3), 533-550. This early work has been done in manufacturing settings, but much of these also apply to inventory planners in retail and transport planners.

[3] Van Donselaar, K. H., Gaur, V., Van Woensel, T., Broekmeulen, R. A., & Fransoo, J. C. (2010). Ordering behavior in retail stores and implications for automated replenishment. Management Science, 56(5), 766-784.

[4] Larco, J. A., Fransoo, J. C., & Wiers, V. C. S. (2018). Scheduling the scheduling task: a time-management perspective on scheduling. Cognition, Technology & Work, 20(1), 1-10.


This blog was published on LinkedIn on June 19, 2018. The LinkedIn article contains all relevant links.

3D printing will impact global trade, but much less than previously thought

By Bram Westerweel and Rob Basten (Eindhoven University of Technmology) and Jan C. Fransoo (Kuehne Logistics University)

Last fall, ING released a report on the growth of 3D printing as a manufacturing technology. The report includes a scenario in which the rapid growth in of 3D printing equipment would lead to a total share in the global manufacturing equipment of about 50% in 2040. This in turn would lead to a dramatic drop in cross border trade in goods of 38% in 2040.

3D printing will have a significant effect on the manufacturing industry and on global supply chains. However, our analysis shows its effect to be much smaller than ING’s scenario predicts, as our analysis concludes that the decrease in cross border goods trade will likely be less than 7% rather than the 38% announced by ING. Our conclusion if different because we believe the ING report to contain a number of flaws. We outline our reasoning here below, and created some graphs to make that clear.

The key assumptions in the ING report are (on page 8):

  • The annual growth rate for investment in 3D printing has been 29% over the past five years, compared to an average of 9.7% for global investment growth in traditional machines. ING assumes that this difference continues to hold in future years.
  • In the ING scenario, investments double (to 58%) for 3D printing after five years, while it will fall by a third (to 6.5%) for traditional machines after ten years.

Our analysis of the ING report and the questioning of the above assumptions leads to our following findings:

  1. The ING report confuses annual investments with the installed base.In the ING report, the current rapid growth in annual investment in 3D printing equipment is reflected proportionally in the share of the manufacturing equipment base. However, manufacturing equipment has a long life cycle. In our analysis, we have assumed, for the sake of argument, a life cycle of 20 years. This implies that many of the assets that are acquired in the upcoming decade will still be there in 2040. Given that the far majority of these assets is still in traditional manufacturing technologies (despite the smaller growth rates), in 2040 a much higher share will be still in traditional manufacturing. Inserting this effect into ING’s calculations reduces the projected share of 3D printing equipment in the total manufacturing equipment to 35% in 2040 (instead of 50%), as shown in the TU/e line in the figure above. Note that while this number is clearly lower than ING’s, this is still very impressive.
  2. The ING report assumes an unsubstantiated annual growth rate of investments in 3D printing equipment of 58% from 2022 onwards. The growth rate in 3D printing equipment is 29% in 2017, according to ING’s source. In the scenario under consideration, ING assumes that this growth rate doubles in 2022 and then continues at this level until 2040 (and beyond). We believe this to be highly unlikely. Growth rates are likely to level off at some point, and unlikely to double on such short notice. The source of this doubling in 2022 and the subsequent persistence of the annual growth rate of 58% for decades is unclear and not mentioned in the report. To illustrate the dramatic effect of this assumption on ING’s calculation, we have used a growth rate of 40% (which we still believe is extremely high) from 2022 onwards. We then find that 3D printing equipment makes up only 8% of the total manufacturing equipment in 2040, as illustrated in the bottom line of our figure.
  3. The ING report assumes annual growth rates in total investments that are unrealistically high. We are unaware of the source for the 9.7% growth rate in conventional manufacturing equipment that ING assumes. This number may be realistic for a limited period of time, during the current crisis recovery period. However, having year-on-year, until 2040 (and beyond), an annual growth of 9.7% in the investment rate seems unrealistic to us. Still, let’s assume that this is realistic. If we then go back to ING’s scenario, we see that in 2040 the annual growth rate in total manufacturing equipment, i.e., conventional plus 3D printing, is 21%. This effect is due to the extremely high investment growth rates in 3D printing, which by then makes up an increasingly large share of the total manufacturing equipment. With (highly optimistic) growth rates of 40% from 2022 onwards, annual growth in total manufacturing equipment is still 9% by 2040. This type of growth seems unfounded, with a substantial impact on ING’s conclusions.

We are a strong believer in the future of 3D printing technologies. Application in personalized devices, spare parts and – ironically – making flexible tools for traditional manufacturing technologies, will lead to massive changes in many factories. However, the timing and impact on global manufacturing will be very far from the numbers presented by ING. It is unclear to us how ING has translated the impact on global manufacturing exactly to an impact on global trade, but if we simply scale the reduction proportionally, our results at point 2 above would mean that instead of a 38% reduction in trade by 2040, the reduction in trade would be less than 7%.

Besides the much smaller impact on global trade, our analysis also illustrates that models with exponentially increasing growth over long periods of time are unsuitable for conducting such analyses. Even under our adapted assumptions, annual manufacturing equipment growth rates will approach unrealistic values as time progresses as 3D printing would ultimately completely dominate traditional manufacturing. A more credible analysis should, therefore, be based on a more realistic model that allows the ratio of 3D printing and traditional manufacturing to reach some equilibrium, if one wishes to make any claims on the timing and impact of 3D printing on global trade.

Note: We have received a response to a draft of this text from ING’s Raoul Leering. This has clarified a few points, resulting in some changes in the text. His key comment is that the “report does not predict anything! It is only a scenario analysis that shows what happens with world trade IF the current growth rate doubles.” The ING report contains a second scenario in which investments in 3D manufacturing equipment equal traditional manufacturing equipment in 2060. In this response, we focus on the 2040 scenario.Furthermore, ING has not responded to our request to share with us the source of their investment data.

Reaching 50 million nanostores: New book for any manager in cpg or retail

Reaching 50 Million Nanostores: Retail Distribution in Emerging Megacities has been published. In more than 400 well-accessible pages, Jan C. Fransoo (Eindhoven University of Technology, Netherlands), Edgar E. Blanco (Center for Transportation and Logistics at MIT), and Christopher Mejia-Argueta (Center for Transportation and Logistics at MIT) share their views and extensive experience in studying and working with the millions of family-owned small stores in the large metropolitan areas of much of the developing world. Along with a set of authors from the region, the editors introduce 11 case studies about how to win in the fragmented and challenging retail landscape where these small retail outlets are located: to serve them with effective logistics and commercial strategies.

For many consumer packaged goods (CPG) manufacturers, this channel represents 40-90% of sales in more than half of the world’s markets. As such, the nanostore channel is a force to be reckoned with. Unfortunately, many managers have not only a poor understanding but often also a completely wrong perception of this channel. This book helps anyone interested to understand this market segment where CPG sales are still growing. Next to an exposition of the editors’ views on current practice and future developments, the book contains an extensive set of case studies from nine countries in Latin America, Asia, Africa and Southern Europe. Companies covered include highly successful local brands like Nutresa in Colombia and Danone’s Bonafont water in Mexico. The full Table of Contents is included below.

The book is accessible to anyone for about 25 US dollars on Amazon. If you want the many pictures, figures and tables in full color, the book is also available at just over double this price, also on Amazon. Make sure saving on shipping costs by using your local Amazon platform. An extensive list of countries is included at the bottom of this post. If your country is missing, please let us know. For those that prefer reading an e-book, a Kindle version should become available by mid-November.


“As the world population tends to concentrate more and more in urban environments, the two fastest growing channels for consumer goods distribution are online sales and convenient, proximal nanostores. Remarkably, this trend applies to both the most and the least developed economies. This book is a valuable resource that covers the realities and the challenges of serving nanostores, a subject much less widely covered than the “sexier” online e-commerce channel, but equally important for understanding the evolution of the world’s fast moving consumer goods markets.” – Sergio Barbarino, Procter & Gamble Research Fellow and Chairman of The European Technology Platform for Logistic Innovation, ALICE.

“This book provides frameworks, concepts, practical tips, and rich examples for developing effective nanostore supply chains, enabling value creation for both business providers and the general population in developing economies” – Hau L. Lee, Thoma Professor of Operations, Information and Technology at the Stanford Graduate School of Business.

“Understanding the nanostores business in emerging markets is critical to the growth strategy of most fast moving consumer goods firms, and this has not been an easy task. This book provides a clear understanding of the complexity and networks of nanostores through its rich material, including frameworks and cases that are important and useful in developing an effective distribution strategy to serve and win in this complex marketplace.” – Danillo G. Figueiredo, Global Director of Supply Integration at Anheuser-Busch InBev

Table of Contents

Non-exhaustive list of countries where the book can be ordered locally

Amazon USA: B&W / Color

Barnes and Noble USA: B&W

Amazon Canada: B&W / Color

Amazon France: B&W / Color

Amazon Germany: B&W / Color

Amazon Italy: B&W / Color

Amazon Spain: B&W / Color

Amazon UK: B&W / Color

Amazon Brazil: B&W

Amazon India: B&W

Amazon Japan: B&W

Amazon Mexico: B&W

Serving nanostores by van-sales or pre-sales?

Guest blog by Professor Youssef Boulaksil (United Arab Emirates University)

Many large Fast Moving Consumer Goods (FMCG) manufacturers and distributors, such as Unilever, P&G, Nestlé, and Danone, struggle with how to efficiently supply the thousands of nanostores that one typically finds in large cities in developing countries. Although each nanostore generates a negligible amount of sales, the cumulative sales of all nanostores in these markets is usually much more than the sales generated from modern channels such as hypermarkets. Hence, the nanostores form a serious sales channel that cannot be ignored. Distributing goods to them is a complex task due to several reasons. First, these stores do not keep track of inventory levels or historical sales, they do not actively order from their suppliers, they operate under very tight cash constraints, and there is a high turnover in stores with frequent closures.

Therefore, the distribution strategy that many FMCG distributors apply is the van-sales strategy. Under this strategy, a van or a small truck is filled with the products and visits the nanostores one after the other. When visiting the nanostore, the truck driver inspects the inventory of the item and tries to collect an order. If that happens, the delivery and payment occur on the spot. Under this strategy, the risk of not getting paid by the nanostore is eliminated, but it has some serious drawbacks. First, the truck driver is not a sales person by nature, and therefore, the generated sales may not be satisfying. Second, the truck driver may lose lots of time to find a parking bay, especially since the majority of the nanostores are located in densely populated neighborhoods with narrow streets.

An alternative distribution strategy is the pre-sales strategy. Under this strategy, the order collection and order fulfillment processes are decoupled. A pre-sales agent collects the orders from the nanostores by visiting them on a motorcycle, and the delivery and payment occur at a later moment. This strategy is obviously more expensive; as skilled pre-sales people need to be hired and some additional investments are required to implement this strategy. Distribution can be more efficient, since typically an additional helper serves on the truck, and only stores that actually have ordered products are visited.

In collaboration with Valencia, a successful Moroccan fruit juice manufacturer and distributor, we conducted a study in Casablanca, where the van-sales strategy was applied in one district and the pre-sales strategy in a similar district. Detailed data related to the distribution process (such as the number of stores visited, the traveling time, the collected orders, time spent in the store, etc.) were collected at a daily level during 3 months that allowed us to compare the performance of the two strategies. The result was that under the pre-sales strategy, the sales volumes were much higher, while the distribution cost was about 25% lower.

This result inspired us to develop a mathematical model that would provide insights about which distribution strategy to choose in general. Using the model, we show that the distance between the nanostores is the main parameter that determines which distribution strategy is optimal, given the difference in sales between the channels. We derive an expression for the critical distance between the nanostores, which is mainly dependent on the cost structure, hit rates, and the level of congestion in the city. If the actual average distance between nanostores is below the critical distance, then the pre-sales strategy is optimal. Otherwise, the van-sales strategy becomes optimal. This means that in densely populated neighborhoods where nanostores are close to each other, the pre-sales is the best strategy to apply. At country-sides or suburban areas where the distance between nanostore is larger, the van-sales will prevail.

The detailed model are available in a publication in Interfaces. For more information, please contact Professor Boulaksil.

Serve nanostores directly or make use of wholesalers?

recent study, based on the data from the Swedish alcoholic beverages industry, shows that even in a very developed market like Sweden, gaining access to smaller retail formats increases sales at a much higher rate than when selling in large stores. The reasoning is that although small retail formats may provide only a small pie due to limited store size and smaller assortments, manufacturers can reach a larger market share exactly due to these very same reasons. Linking this to our research in the megacities of developing markets, this relationship obviously holds for nanostores, the small mom-and-pop neighborhood stores that flock the megacities of this world. Nanostores, which carry only a few brands of a product category due to limited cash and shelf space, allow a winner-takes-almost-all strategy.

However, serving the nanostore channel is costly. With tens of thousands of stores to be reached in a single megacity, the mere distribution costs can be very high. While many incumbent players serve stores directly, multinational manufacturers entering in these markets often adopt a strategy where wholesalers are used. The latter is driven by the lower operational cost and the lower headcount to such manufacturers when using this channel. Obviously, this goes at the expense of a lower market growth rate since wholesalers have less of an incentive to develop demand for a specific product; they are driven by their entire product portfolio. For example, as we have learnt from the data of a CPG company delivering in the city of Bogota, the wholesale channel is 9% more profitable per unit sold than the direct channel, while the direct channel grows the market 11% faster than the wholesale channel.

In chapter 4 of the PhD dissertation (now available online) of my student Jiwen Ge, we study this trade-off. Under which conditions should manufacturers prefer one channel over the other?

There are two simple metrics that drive this choice. The first metric is the gross margin (that we denote below by the symbol M), essentially measuring the additional profit margin contribution of selling one additional unit, taking into account the price (typically higher in a nanostore sale than when selling to a wholesaler) and the distribution costs per unit (typically higher in the direct channel). The initial thought would be to pick the channel where the gross margin is highest. For new entrants into the market, where the cost of setting up direct distribution to tens or hundreds of thousands of nanostores is high, this usually would imply selecting the wholesale channel.

However, this does not take into account the effects of better replenishment and faster sales growth in the direct channel. Hence, the gross margin effect needs to be adjusted for the anticipated sales growth differences. For that, we introduce the second metric of growth adjusted profitability (that we denote by the symbol P), which is the gross margin divided by the anticipated sales growth rate.

Comparing the two channels, then three potential strategies emerge: going direct, using wholesale, or first going direct and eventually switching to wholesale. In the latter strategy, first the direct channel is used to introduce the product and to grow the market, and subsequently the wholesale channel is used to take advantage of its lower cost once the product has been established. The combination of the gross margin and growth adjusted profitability metrics defines whether one strategy is better than the other.

Using the above-mentioned metrics, the tables below show what to do for different lengths of decision making horizons. Note that M(D) denotes the gross margin of the direct channel, M(W) the gross margin of the wholesale channel, P(D) the growth-adjusted profitability of the direct channel and P(W) the growth adjusted profitability of the wholesale channel.

An interesting trait that impacts the decision significantly is the decision making horizon. In some companies, a return on investment (reflected for instance in the Net Present Value of a project) needs to be made in a short time. For instance, a company requires returns to be positive over a one of two-year decision making horizon. These companies will find it hard to start with a direct channel strategy, since the benefit of higher sales may not be realized within the decision making horizon, and the initial loss may be too large. In many case, this implies that these companies will not win in the market through using the direct channel. Entering into the nanostore channel directly requires longer breath, and a decision making horizon is typically a bit longer, which allows manufacturers to enter the market using the direct channel and then switch to the wholesale channel to take advantage of its cost efficiency. If the horizon is long enough, and the gross margin high enough, a direct strategy should be used without ever switching to wholesale.

Of course, our findings are based on stylized models, and details may differ in different markets and for specific ratios, but based on these insights, it will be possible for any neighborhood of any city to define the ratios mentioned above, and to make the trade-off whether to go direct or make use of the wholesale channel.

This blogpost has been published earlier on LinkedIn

Nanostores in megacities: The first model-based phd thesis that helps us better understand this important channel

On September 12, my student Jiwen Ge will defend his PhD Thesis “Traditional retail distribution in megacities”. As far as I know, this is the first set of analytical models that helps us better understand how manufacturers should operate in the megacities of developing markets. In these megacities, a significant portion of retail consists of nanostores, small mom-or-pop-operated retail stores that mainly sell consumer packaged goods. In other work, due to be completed soon, we are showing that this channel will prevail in these megacity environments for the foreseeable future.

Although the thesis is a PhD thesis, with hence as its main objective to develop theory, I do recommend reading the thesis to practiontioners, to develop a better understanding of this important channel. The introductory and concluding chapters have been written such that they accessible to a large audience and the thesis is open-access, so anyone can access the pdf version online from a few days after the defense will have taken place. If you are interested in receiving a (free) hardcopy, feel free to contact Jiwen by sending him an email, since the printing company mistakenly printed a few hundred copies too many ;-).

Dissertation cover_Jiwen Ge In the thesis, using analytical mathematical modeling, four topics are addressed:

1.    How much sales effort should a supplying manufacturer execute, to optimize the tradeoff between the costs of extra effort (more pre-sales agents, less efficient routes) with the additional sales that this might bring?

2.    How should a supplying manufacturer secure shelf space and cash, given that competing companies are trying to get access to the very same scarce resources at the store?

3.    Should a manufacturer deliver nanostores directly, serving each store individually, or indirectly, making use of distributors or wholesalers?

4.    In the new On-demand retail services that are developing in countries like Peru, China, and Indonesia, what is the optimal network size of nanostores to contract to have an efficient network with fast response to consumers?

In an upcoming series of blogposts, Jiwen en I will provide you with the key insights from each of the Chapters. The blog posts should become available one-by-one over the next few weeks. Just follow me on LinkedIn to get alerts.

Interestingly, in all cases that we study and try to understand, we closely link commercial decisions and logistics decisions. Serving a nanostore network successfully and growing revenue and profit in this large channel requires this. While in developed markets the logistics and commercial (marketing, sales) functions have become siloed over time, and maybe for a good reason, our work suggests that if you operate in traditional retail markets in developing countries this is definitely not a good strategy. The functions need to be closely aligned, and key decisions need to be taken jointly.

On a side note to the above but definitely as relevant to logistics or commercial executives in such environments, our book Reaching 50 Milllion Nanostores – Retail distribution in emerging megacities is also ready to go into print and should appear some time in October 2017. This book, edited by Edgar BlancoChristopher Mejia, and myself, brings you the relevant concepts to be successful in serving the nanostore market, and provides a series of case studies of leading companies in these challenging environments.

A simple balanced trade-off suffices to secure supply from your manufacturing contractor

Contract manufacturing is now common in many industries. While this has been common in the electronics industry for a few decades now, we also see many instances of contract manufacturing in other industries, such as pharmaceutical and chemical. In order to pool demand, contract manufacturers try to serve multiple clients from the same factory. In that way, they can try to limit fluctuations in demand for the scarce capacity in the plant.

For the OEM clients of a contract manufacturer this implies that they need to provide advance demand information to the contract manufacturers. This information can be in the form of a formal order long in advance, in the form of a formal reservation, or in the form of a forecast. In our experience, often the exact character of this information may be an ambiguous combination of any of the above. In a recent article that I worked on with my former student (now professor) Youssef Boulaksil and my TU Eindhoven colleague Tarkan Tan, we try to understand how a manufacturer should act if it has contracted (part of) its manufacturing with a contract partner.

Despite the model being stylized, the results are insightful for (OEM) manufacturers and contract manufacturers alike:

In most cases, ordering each period a fixed order quantity with the contract manufacturer is best, and random demand fluctuations should be dealt with by safety stock at the OEM. This is driven by the fact that it is difficult for the contract manufacturer to respond at very short notice, since it has multiple clients to serve and typically operates at high utilization.
In case the dynamics in demand can be forecasted well, so are not subject to much uncertainty, it pays off to conduct the coordination with the contract manufacturer in more detail: make more specific reservations of the contract manufacturer’s capacity to enable the contract manufacturer to prepare. Relating this to (1): this only makes sense if there is little uncertainty in the demand and the demand pattern can be forecasted well.
The contract manufacturer needs to formalize the ordering process with its client by clearly separating between order reservations and actual orders, where a reservation is just focused on the total quantity (capacity) needed. The manufacturer does not instate a small change or cancellation fee in case the reservation is changed or cancelled, since otherwise their customers will not plan their safety stocks properly.
The two parties should agree and built a mechanism in the contract to ensure that reservations are not inflated by the manufacturer who clearly has an incentive to do so to secure future production capacity. This can be achieved by charging the manufacturer for unutilized capacity reservations, even if marginally.
Obviously, the latter is also dependent upon the “power balance” in the negotiations between the two parties, but we actually show that even for the customer it may be better, since in that case a proper tradeoff is being made between the small supplier charge and an appropriate safety stock level.

How are you managing this type of a relationship, and how is the order flow organized? Please leave your comments on the LinkedIn blog version of this post.

If you would like more information, or investigate how this stylized model can be engineered into a decision support tool for your company, feel free to contact us. We might be able to link you to an interested Master student.

Alternative for demurrage and detention rates in inland container transport will dramatically reduce costs and emissions

Demurrage and detention costs are charged by ocean carriers to ensure that consignees send back their containers as soon as possible. However, sending back an emptied container at an import destination precludes the re-use of this container in the immediate vicinity to fill it with export products. Typical demurrage and detention (D&D) rates give a few days of free D&D; after the free period the consignee needs to pay a fixed rate per day which may increase over time.

For a while now, I have wondered why this strange mechanism is in place. The most likely answer is that this has probably grown “historically”, as it does not make sense from any perspective: ocean carriers encounter extra costs by sending empty container for loading to export destinations, and consignees encounter paying additional fees to the carrier, even if they are trying to optimize hinterland operations. My former PhD student Stefano Fazi, now at the University of Groningen, has shown in his PhD thesis that the current D&D fee structures in the port of Rotterdam lead to higher cost for the supply chain as a whole, and moreover lead to favoring road transportation over barge, thus leading to higher emissions and congestion.

Recently, Benjamin LegrosYann Bouchery, and I completed a very interesting theoretical study, in which we show – using a fairly simple mathematical line of reasoning – that all of this D&D does not make much sense: the supply chain cost go up and unnecessary container movements increase. It would make sense to keep a small number of empty containers in each hinterland area, void of any D&D payment structure. In our paper (warning: the paper is mathematical!) we show that with a simple threshold policy (essentially agreeing how long to keep a container in an hinterland area – or alternatively how many containers to keep in the hinterland – , depending on the total flow of containers to and from that area) we can reduce costs by 20-80 (!)% compared to a common policy where a container is returned immediately upon unloading – see the figure above taken from our paper. Interestingly, I recently heard that a number of deepsea terminals now have contracts with carriers where free demurrage is no longer expressed in the number of days, but in the total number of containers kept. This idea is very similar to ours, but as far as a I know this has yet to be deployed in the hinterland.

Of course, the question is then in the end whether the ocean carrier’s real objective is to have the supply chain work best, to have the containers back in their yard as quickly as possible, or to maximize its revenue from D&D fees. I leave it up to your imaginination to guess the answer. In the paper, we however show that it is possible for the carrier to devise a pricing structure such that its own profit margin remains unchanged. Hence, for a carrier there is no reason not to switch to such a new pricing structure. Curious to learn how carriers would be prepared to take next steps.