Polistar Ltd., founded in 2006, initially specialized in precision engineering, delivering high-quality products to a growing number of industrial and commercial clients across Europe. The company prided itself on reliability and efficiency, and by 2015, it had cemented its reputation as a key supplier in sectors such as automotive components, specialized machinery, and electronic parts.
Based in the Poland-Germany region, Polistar capitalized on the strategic advantage of its location, serving clients in both Western and Central Europe with swift and efficient distribution channels. The company’s success was largely driven by its close-knit relationship with suppliers and its meticulous attention to demand forecasting.
By 2018, Polistar had established long-term contracts with major clients in cities like Munich, where they supplied high-end, time-sensitive goods such as precision tools, luxury electronics, and mechanical parts. Their distribution network was the backbone of their operations, making Polistar one of the most reliable companies in the industry.
However, like many businesses, Polistar faced major disruptions after the COVID-19 pandemic hit in 2020. Supply chains across the globe were thrown into disarray, and Polistar was no exception. Sourcing raw materials from their usual suppliers became more challenging, transportation routes were blocked, and production slowed significantly. In addition, demand forecasting became unreliable as customer purchasing behavior fluctuated wildly, further destabilizing Polistar's operations.
Despite their efforts to adjust, Polistar found that their supply chain had become unpredictable, with sudden demand spikes and shortages becoming the norm. While their core business remained strong, the logistics side began to falter. Since 2020, they had struggled to regain control over their demand planning and distribution strategy.
Munich, being one of their key markets, continued to present logistical challenges. With five key clients in the city and a difficult distance matrix to manage, Polistar's distribution efforts were becoming inefficient and costly, adding further strain to the company’s recovery efforts.
As Polistar faced these mounting challenges, it became clear that the company would need to rethink its approach to supply chain and distribution planning in order to return to its former glory. The path forward, however, remained uncertain.
Polistar Ltd.’s distribution and supply chain system had always been a core strength of the company, allowing it to deliver precision-engineered products efficiently across Europe. Before the disruptions of 2020, Polistar’s supply chain was a well-oiled machine, enabling just-in-time (JIT) production and maintaining lean inventory levels. This system minimized costs and ensured that clients received their orders without delay.
Pre-2020 Supply Chain System
Polistar operated a multi-tiered supply chain system, sourcing raw materials and components from trusted suppliers across Europe and Asia. The company had built strong relationships with these suppliers, ensuring a steady flow of high-quality materials. Once materials arrived at their manufacturing facility, they were transformed into precision-engineered products, such as industrial parts, tools, and electronic components. The production process was optimized for efficiency, reducing waste and ensuring consistent product quality.
Polistar’s distribution network was designed to meet the demands of clients located in key industrial and commercial hubs, such as Munich, Frankfurt, Warsaw, and Paris. The company utilized a hub-and-spoke model for distribution, with central warehouses strategically located near its major clients. Products would be dispatched from these hubs to customer locations, following carefully planned transportation routes.
Polistar had historically relied on a combination of road and rail transportation to deliver its products, using third-party logistics providers to ensure swift and cost-effective deliveries. Their transportation routes were optimized for speed and efficiency, and they benefited from the established infrastructure in the Poland-Germany region.
Post-2020 Supply Chain Challenges
The arrival of COVID-19, however, disrupted Polistar’s smooth operations. Global lockdowns and transportation restrictions caused severe delays in the receipt of raw materials, often leaving Polistar with incomplete inventories. Sourcing from suppliers, particularly those in Asia, became unpredictable as factories shut down or experienced shortages themselves.
To make matters worse, freight rates skyrocketed, and port congestions led to further delays. As a result, Polistar had to shift from JIT production to carrying larger buffer inventories. This placed strain on their cash flow and forced them to find additional storage space for raw materials and finished goods.
In terms of distribution, Polistar’s established transportation routes became less reliable. Shipments were delayed due to logistical bottlenecks, and fuel prices increased transportation costs. Polistar also faced demand fluctuations, with some clients over-ordering due to panic buying, while others canceled orders due to economic uncertainty. This led to forecasting errors, causing mismatches between demand and supply.
Distribution System in Munich
One key area of concern for Polistar is its distribution system in Munich, where it has five major clients. The clients are spread across various locations, and the distance matrix between them complicates the planning of efficient delivery routes. Before the pandemic, Polistar’s logistics team was able to optimize delivery schedules, ensuring that all clients received shipments on time, often using the shortest routes. However, the post-pandemic environment introduced unpredictability into the system.
Clients’ fluctuating demand, combined with longer lead times and occasional stockouts, caused inefficiencies. Distribution routes that once worked well now resulted in increased fuel consumption and delivery delays. The company struggled to decide whether to prioritize certain clients or follow the most efficient travel routes. At the same time, the company faced rising pressure to reduce costs and improve delivery times.
Polistar’s delivery network was no longer optimized, and the traveling salesman problem they faced in Munich—trying to find the shortest possible route to deliver to all five clients—only added to the complexity of the situation. This inefficiency was becoming more costly and less sustainable.
Current Supply Chain Strategy
Polistar Ltd. has been exploring several solutions to improve their supply chain and distribution system. They have started looking at better demand forecasting methods to reduce the mismatch between demand and actual inventory levels. Additionally, the company has considered adopting new technologies, such as predictive analytics and transportation management systems (TMS), to improve distribution planning and optimize delivery routes.
The company is also evaluating options to diversify its supplier base, sourcing from more geographically diverse regions to reduce dependency on a few locations. This would help mitigate risks associated with future disruptions.
Polistar’s leadership understands that to regain stability, they must find ways to make their supply chain more resilient while improving the efficiency of their distribution network, especially in high-demand areas like Munich. But with many possible solutions and challenges still in play, the path forward remains unclear.
Their supply chain, once robust and predictable, became increasingly unstable. Sourcing raw materials became a challenge, production schedules were disrupted, and the entire process slowed down. By 2024, Polistar was still struggling to regain its foothold, especially with distribution planning.
For the last seven weeks, their demand forecast versus actual demand data painted a concerning picture:
| Weeks |
W1 |
W2 |
W3 |
W4 |
W5 |
W6 |
W7 |
| Demand Forecast |
20 |
20 |
20 |
20 |
20 |
20 |
20 |
| Actual Demand |
18 |
24 |
26 |
27 |
23 |
26 |
19 |
The company was consistently under or over-forecasting demand, leading to either excess inventory or stock shortages. While this could be addressed through better demand planning, the situation on the ground in Munich complicated matters further.
In Munich, Polistar had five key clients located at various distances from their distribution center. The distance matrix between these clients was as follows (in kilometers):
|
Client 1 |
Client 2 |
Client 3 |
Client 4 |
Client 5 |
| 1 |
0 |
8 |
5 |
9 |
8 |
| 2 |
|
0 |
11 |
14 |
12 |
| 3 |
|
|
0 |
8 |
6 |
| 4 |
|
|
|
0 |
13 |
| 5 |
|
|
|
|
0 |
Polistar’s logistics team faced a complex distribution challenge. Each week, shipments had to be delivered to these clients based on their varying demand. However, with the forecast inaccuracies, it was difficult to plan the exact quantities of goods to be distributed. Worse, the transportation routes had become inefficient, leading to rising costs and delays in deliveries.
As they reviewed the current situation, the logistics manager began to feel the pressure. Should the company prioritize reducing forecast errors, adjust their supply chain planning, or focus on optimizing distribution routes? Should they implement new technology to track and predict demand more accurately?
And what about the traveling salesman problem they faced in Munich? With five clients located at varying distances, which route would be the most efficient to minimize transportation costs and time, given their unstable demand forecasts?
Polistar needed a solution, but where should they start?
