Using product segmentation to improve supply chain management in Tata Steel

Transcription

1 Original Article Using product segmentation to improve supply chain management in Tata Steel John Albiston* and Ian Cross Tata Steel Strip Products UK, Port Talbot Works, Port Talbot, SA13 2NG, UK. s: *Corresponding author. Abstract The steel making operations of Tata Steel in Europe are moving to a new operating model with common processes adopted at all sites. Two of the major initiatives underlying this new model are Customer First and Supply Chain Transformation. Collectively, these initiatives aim at making a step change in the company s offering to customers with a focus on improving delivery on time, reducing leadtime and reducing work in progress. Improving the flow of material through the business is critical in achieving these objectives. The ORteam has been involved in standardising a common process for product segmentation. The team has taken a data-driven approach and this article outlines the methods used in creating the output necessary to ensure supply chain improvements. OR Insight (2012) 25, doi: /ori ; published online 18 July 2012 Keywords: supply chain; ABC; fulfilment strategies; manufacturing Received 25 May 2012; accepted 28 May 2012 Introduction Tata Steel is a global steel maker with manufacturing sites around the world. Tata Steel Strip Products UK (SPUK) is based in South Wales and produces

2 Using product segmentation to improve supply chain management 4.5 million tonnes of steel a year. The ORteam provides an internal service for SPUK. The aim of the team is to help decision makers make more informed decisions and so help improve the bottom line of the business. As such, the activities of the ORteam can be explained in terms of the Data Triangle shown in Figure 1. Most of the activities of the ORteam are about turning data into information and knowledge with the aim of adding to the understanding of the company s business processes, that is, aim to have more wisdom. The activities are centred on four main areas: data mining, simulation, optimisation (linear programming) and business intelligence. Many of the ORteam s projects incorporate several of these skills sets. The projects cover operations, tactics and strategy. Tata Steel in Europe has moved to a new operating model with common processes being adopted on all sites. The organisation has been reconfigured to have one face to the customer rather than each business unit selling its own products. Two major in-company initiatives are aiding this: Customer First and Supply Chain Transformation. The manufacturing sites are now part of one integrated supply chain and the old business unit boundaries are being removed. The aim of these transformational projects is to change the way the company interacts with its customers and improve the company s offering in terms of lead times and delivery on time while increasing the efficiency of the process so that an overall reduction in the stock in the supply chain can occur. Figure 1: Data triangle. 151

3 Albiston and Cross Improving the flow of material through the business is critical in achieving these objectives. This creates a significant opportunity to change the way products flow across previous business unit boundaries. The ORteam has been involved in standardising a common process for product segmentation. This involves the combining of several techniques into one method. A matrix combining the ABC and runner, repeater, stranger (RRS) analyses is generated for each of the key parts of the process. Thus, for an individual product, several characteristics are derived. Each analysis is conducted on data from a 6-month period and so tracking of these characteristics over time leads to an attribute of either growing, stable or declining (GSD). Once the matrix has been created, other attributes can be overlaid such as yield, lead time, stocks and so on. By combining the technique with the product funnel, discussions with key personnel can reveal possible changes to the materials used or the process route so that the supply chain can be optimised. This article outlines the methods used in creating the output for these datadriven supply chain improvement projects. Manufacturing Constraints The through process route from iron making to Tata Steel s customers contains many individual operations, across several sites. The manufacturing processes can be batch, semi-continuous or continuous and each process has a range of batch quantities and/or scheduling rules that are quite different. These unit-scheduling rules are required to allow the units to produce quality products. Due to these different constraints, inter-process stock is required to enable the flow of material through the plant. At the steelmaking stage, liquid steel, which has been refined from the blast furnace produced iron, is cast using a continuous process. Liquid steel batches of 330 tonnes are refined and cast on one of three continuous casting machines. While the thickness remains constant, the width needs to be changed. Each batch has one chemical composition. To allow the product to flow in the best way for production (quality and volume), sequences of the same chemistry are cast together with a preferred sequence length of up to six casts that is, approximately 2000 tonnes. During the processing of this material, the width can be altered in small steps. The next stage of the main process is hot rolling; here the slabs are reheated to around 12501C and rolled in the hot rolling mill where the gauge is reduced from around 230 mm to between 1.5 and 17 mm depending on the customers requirements. The rolling process is scheduled so that several different 152

4 Using product segmentation to improve supply chain management chemistries are rolled together in a round. A round is constructed so that wide slabs are rolled first and then the material in the round progressively narrows ( mm). A new round then starts after several of the rolls are changed. The weights of the slabs are between 15 and 35 tonnes. The slabs are rolled one at a time, with orders that can be anything from 1 slab to more than 20. Materials in a round combine lot of orders and each order is rolled to different thicknesses and different processing conditions. After hot rolling, the material can be despatched to end customers, to other Tata Steel plants or can be further processed through pickling, cold rolling, annealing and coating. At each stage in the process, products can go to internal or external customers. As with the hot rolling, all of these various internal units have different batch sizes and schedules. Figure 2 shows a Sankey diagram of the flows through SPUK. The traditional product funnel (please see Figure 3) has shown that historically the customisation of the product has occurred very early in the process, traditionally at the slab making stage, that is, that slabs are manufactured for a customer order. (Figure 3 is explained in more detail in the following section.) SPUK supplies around 800 customers with over products in order sizes of 15 tonnes to over 5000 tonnes. The average order size is around 60t. As the material moves to downstream Tata Steel units, the number of products offered increases substantially and the order sizes reduce. In the old business model, each business unit tried to optimise its own flow and performance. In some cases, this led to boundary issues with each business holding stocks and having their own lead times and order systems. This sub-optimisation of the whole chain provides a fertile ground for improvement with a One Company philosophy in place with the aim of replacing local optima with more global optimum. Figure 2: Sankey diagram showing the flow of material through SPUK. 153

5 Albiston and Cross uniques specifications Current Aim process Figure 3: Product funnel. The use of supply chain analytics and the creation of standard methods to analyse the order book has been undertaken by the ORteam in SPUK. One of the methods being used to improve this flow is based on Product Segmentation. The aim of this work is to offer a standard approach that provides the data and information required to support this process. Product Segmentation Methods There are numerous methods to aid supply chain improvement based around product segmentation, also called late customisation and generics. The techniques used are well-established methods that, in this work, have been combined and modified into an integrated approach. The aim is to group products into different classifications so that different supply chain approaches can be applied, such as make to stock, finish to order and make to order (Olhager and Prajogo, 2012). However, the critical consideration is to change the way products are manufactured to improve the flow of material through the chain (Bicheno et al, 2001). One way to do this is to move towards the aim as shown in Figure 3, the product funnel. Product funnel The product funnel shows the number of unique specifications at each stage in the production process. At each stage of the process, a set of processing parameters is combined that lead to determining how many varieties exist. For example, each combination of the length, width and steel grade at the casting stage would make the product unique. The number of 154

6 Using product segmentation to improve supply chain management unique specifications at each step of the process is plotted on the product funnel, as shown in Figure 3. In Figure 3, it is evident that a lot of the uniqueness of the product is determined early in the production process. The aim of Product Segmentation is to allow some of the uniqueness of a specification to be added later in the process and to create decoupling points that separate customer orders from internal manufacturing orders. ABC analysis ABC analysis is the classic product segmentation approach (Teunter et al, 2010; Hadi-Vencheh and Mohamadghasemi, 2011). It divides products into three classes based on volume (or value) (Figure 4). Class A represents the large volume group of products (theoretically containing 80 per cent of the total volume in 20 per cent of the specifications), Class C includes a high number of small volume orders (theoretically 5 per cent of total volume) with Class B representing those in between. The initial investigation used the historical values from the cumulative distribution curve. However, as the analysis is undertaken at critical points in the process route, it was observed that the cumulative distributions did not follow the same trend (see Figure 5). In order to produce complimentary analyses at different stages of the process route, non-standard split points were used. Initially, this was a manual, iterative method based on somewhat of an art. By observing the initial results, it was estimated that the actual curves could be used to determine the splits. This is achieved by comparing the actual cumulative distributions with the cumulative distribution for the Standard (that is, the reference base case cumulative distribution). The method involves selecting the standard split points from A B C Highest Lowest Figure 4: ABC analysis. 155

7 Albiston and Cross Volume product Slab steel grade steel grade family Hot Rolled Standard Spec Figure 5: Actual cumulative distributions. the standard distribution curve (that is, the 80 per cent and 95 per cent of the total volume) but transferring these points to the actual distributions. This means that the 80 per cent and 95 per cent cumulative points are changed to different values for the actual data set. This produced very similar, but more repeatable results to the iteration method. RRS analysis The RRS analysis is also a common supply chain tool, but whereas ABC looks at volume (profits and so on), the RRS method looks more at the frequency at which events occur (Waddington, 2003). This method determines how stable the flow of the product is, that is, how often is that material manufactured. RRS is therefore an alternative way of segmenting the orders. Figure 6 shows schematically what the different patterns would look like on a time series plot. Runners are produced nearly every week at very similar tonnages, repeaters less frequently or with very large variations in tonnage from period to period, whereas strangers occur very infrequently. As with the ABC analysis, standard ways of assigning the categories are available. In this work, we used two standard methods/criteria: 1. A simple count of the number of weeks in which production occurred; 2. The coefficient of variation (standard deviation over the mean) of the weekly tonnages. A similar effect to the ABC analysis was observed in that the data from different process stages have quite different characteristics. For this reason, the two approaches of count and coefficient of variation were used iteratively 156

8 Using product segmentation to improve supply chain management Volume Runner Time Volume Repeater Time Volume Stranger Time Figure 6: RSS representation of orders over time. to determine the split points for each process.thestartingpointsweresplits on count to be at 15 per cent and 75 per cent; so for a 26-week period, it would mean split points of 4 weeks and 20 weeks, respectively; for the coefficient of variation, the split points would be at 1.0 and 2.0. By iterating between these so as to reduce the number of specifications, which have different values of RRS, segmentation was produced for each stage in the process. A review of the data failed to find a simple method to move away from this iteration process as observed for the ABC analysis. One significant observation is that data from different process stages seem to fit into one of two groups; one group that was largely based on areas where the product at that stage would only be produced once in every time period and the other where there may be multiple occurrences of that item in the time period. This observation could be important in determining the time unit of the analysis. In this case, as the company s current order system allows only one customer order of a certain specification being placed per week (deliveries, while linked to orders, can be more frequent but would be delivered from one order), our time period was selected at one week. An alternative time period may mean that this split in the characteristics of the RRS analysis may not be seen. 157

9 Albiston and Cross Table 1: ABC/RRS matrix (values are as percentages) Runner Repeater Stranger A Tonnes No B Tonnes No C Tonnes No Combining the ABC and RRS results The critical step in our segmentation method was to combine the ABC and RRS analyses into a 3 3 matrix (see Table 1). This matrix, containing nine classifications, is produced for each stage in the manufacturing process. In this matrix, typical characteristics are seen in that there is a large volume of A-runners from quite a low number of specifications, whereas the C-strangers have high number of specifications but only a small volume. The matrix is regularly used to determine, for any of the nine classifications, which is the most suitable supply chain fulfilment method, as indicated in Figure 1. The strategies applied are make to stock (no shading), finish to order (light shading) and make to order (dark shading). This is overlaid on top of Table 1. Analysis Use of the ABC/RRS matrix This method is commonly used on final products to determine the way the supply chain operates. In this work, the matrix and the data set not only show what classification the customer orders are in but for the upstream processes what classification the material sits in as well. The aim of the work was to maintain the product diversity to customers while improving the material flow. This would allow improved delivery to time and shorter lead times. The flows of A-runners are generally well known and the supply chain process has been optimised. While improvements across old business unit boundaries can be improved, the main areas to improve performance are generally associated with the C-stranger products. This work can be used to improve these types of products. For instance, where two C-stranger products exist, one has its whole 158

10 Using product segmentation to improve supply chain management process route characterised by the C-stranger designation. This is a quite different supply chain problem to a C-stranger product that comes from an A-runner upstream specification. By looking at the specifications, it may be possible to change the route on the C-Stranger product so that it comes from a repeater or runner at an upstream specification, thus completely changing the flow of material through the production process. Adding value One of the main benefits of producing the matrix is using the ability to overlay other data on top of the matrix. Collecting data such as delivery to time, yield, throughput times, lead times, stock turn, right first time, rejections and so on can be overlaid on top of the matrix to reveal how these factors are being affected by the category on the matrix. An example of this is shown in Table 2. The predicted lead time is the average for a grid point and is calculated as the time in weeks from the order booking date to the planned delivery date. While the measured value represents the degree to which the delivery date was achieved, higher values represent poor delivery on-time outcomes. This shows that on the initial method of working, the planned lead times do vary a little between classes, but most categories have similar lead times, whereas the actual lead times vary significantly with the ABC/RRS category. Further analysis of these overlays gives good indications of how to better control the lead times offered and to change the upstream route to further increase supply chain efficiency. Selecting the time period to undertake the analysis As already described, the analysis has been undertaken with weekly data. However, the duration over which the analysis should be undertaken also needs to be determined. Traditionally, annual data would be considered as being a good representation of the true demand. However, the main Table 2: Overlay of delivery performance on ABC/RRS matrix Runner Repeater Stranger A Predicted Measured B Predicted Measured C Predicted Measured

11 Albiston and Cross controlling factor is to be able to pick a stable period. In the current volatile market, the period of a year was deemed to be too long. As part of the reason for undertaking this work was to improve the supply chain processes, it was also necessary to be able to see changes made and to monitor progress. For these reasons, a period of 26 weeks was selected. The analysis is then repeated on subsequent 26-week periods. A method is needed to be able to observe the difference from period to period and what trends were occurring. In order to do this, a third analysis was undertaken to determine if the particular classes are growing, stable or declining in size, termed GSD. Comparisons of analyses from each time period are used to calculate the GSD attributes. For each 6-month period of analysis, the output data, which contains the ABC/ RRS data for each order at each of the stages in the process route, is uploaded into a database and the GSD are calculated using a common set of conditions. This new method involves first normalising the output tonnages to the previous periods so that changes in total output are taken into account. The values are then compared and specifications within 10 per cent of the previous value are deemed stable, those with an increase greater than 10 per cent are growing and those with a decrease greater than 10 per cent are declining. The 10 per cent cut-off value was selected to allow small changes to be observed. However, at the presenttime,itisnotknownifthis value is optimum and as more data are collected the effects of changing this cut-off value will be investigated. At this early stage in the use of this method, the 10 per cent was found to give a suitable indication as to how the changes being made could be tracked. The GSD suggests that there are only the three categories; however, two additional categories are sometimes required new specifications and those specifications that were not made in the more recent period. The GSD attributes can also be overlaid on the ABS/RRS matrix, as shown it Table 3. This table shows the matrix at the slab production stage. It is interesting that the A-class material is largely stable, B-class material is slightly more variable with some new and some old specifications, whereas the C-class material is very different with many new and old specifications and very few stable specifications. This further shows the degree of difficulty in providing stable supply chain solutions especially in the most difficult C-stranger category. At the slab stage, the plan that is being implemented is to produce a set of common slab specifications. Figure 7 shows that the percentage of the tonnage that comes from standard slabs on the slab grid matrix has increased over the 24-month period. While it is not the intention to make all the products come from these specifications, it is planned that the vast majority will, and most importantly that those that do not will be treated in a different way. 160

12 Using product segmentation to improve supply chain management Table 3: Overlay of GSD on to ABC/RRS matrix ABC_SS GSD Total Runner Repeater Stranger A declining A stable B declining B growing B new B stable B stopped C declining C growing C new C stable C stopped %tonnage covered by slab grid H2 2010H1 2010H2 period Figure 7: Increase in slab specifications covered by the slab grid. 2011H1 Automation of method The method has been developed and grown over several years and has gone through several iterations and platforms, from MS Excel to databases. Presently, analysis is carried out using IBM SPSS Modeler (formerly called Clementine) workbench with data pulled from several data sources. The data manipulation is undertaken in the workbench and the output file downloaded into a database. As such, the whole 6-month analysis can be easily completed within a day. Using the Output to Improve Supply Chains The work undertaken by the ORteam allows the collection of data from various sources to be automated and the data processed to provide an output file for use 161

13 Albiston and Cross in supply chain improvement activities. This information provides insights into the supply chain. However, on its own it has no use; it is only when it is deployed within supply chain development teams that it can be used to help understand the current situation and how to make improvements. The ORteam works with these development teams to improve supply chains. This work started with a project within the SPUK business, especially around the slab processing stage. However, with the opportunity under the One Company initiative, it has being deployed through the SPUK processes and into Tata Steel s downstream manufacturing units. This experience is improving confidence before rolling out the process to the external customers. The initial success of these projects has helped the method become established. There are several successful supply chain projects that have used the information provided by this method to improve supply chains, with improvements in stocks, delivery to time and reduced lead times being seen. While the method helps these processes, it is the actions taken by the supply chain development teams that make the supply chain improve. As was stated at the start of this article, the ORteam s mission is to help others make more informed decisions. This work shows how OR can help improve supply chains by the simple use of data coupled with OR methods of analysis. Benefits The development has led to a structured method of analysis that has been largely automated. The analysis is undertaken on the complete order book for the whole through process route. The information is used on area or product specific supply chain projects. The fact that the analysis covers the whole order book allows a global perspective, leading to the optimisation of the whole rather than just undertaking the analysis on a small subset. This helps produce local solutions that fit in to the big picture and so help prevent the local teams improving local supply chains at the expense of the whole. The data-driven approach helps the processinasoftwaybyprovidinga common frame of reference for all those involved in the improvement projects based on facts rather than beliefs. Indeed, one side effect is that it identifies how one part of the supply chain varying their own chain for their own benefit can adversely affect another part of the larger chain. This initiates discussions that can lead to new approaches for improving the whole chain. Starting small has allowed the approach and method to develop and initial successes have helped establish the method. The approach continues to develop and as confidence grows then it is being used further down stream. 162

14 Using product segmentation to improve supply chain management Conclusions There is a saying in Tata Steel: if you keep doing what you are doing, you will keep getting what you get, which means to improve you need to change what you do. The use of the method discussed in this article has provided the information necessary to allow a data-driven approach to be used in order to change and improve the process. The analysis on the whole data set, but the implementation on the subset to allow locally driven improvements, allows for a think global act local approach. The one company initiative is providing a driving force for changing what the company does and this approach can provide information to help drive the improvement process. The ability to overlay the details such as yield, delivery to time and so on have clearly indicated to project teams that it is not the large runners that create issues in the supply chain, but the ability to provide the smaller, special specifications. These specifications make up part of a customers overall purchase, and improving the ability to offer and make these special specifications can be an order-winning criterion. Flexibility and creative thinking of how products can be manufactured through the whole supply chain can lead to significant improvements in what can be offered to customers, especially in terms of lead time and stocks. Finally, the approach used does not utilise advanced OR techniques. However, the clear presentation of summarised data and information that project teams can use has created the insights necessary to improve supply chain management. Acknowledgements The ORteam has developed this process, but it is only by the involvement of many others that this technique has become used and established. Many thanks must go to a large number of our colleagues. The article describes work undertaken in 2010/2011. References Bicheno, J., Holweg, M. and Niessmann, J. (2001) Constraint batch sizing in a lean environment. International Journal of Production Economics 73(1): Hadi-Vencheh, A. and Mohamadghasemi, A. (2011) A fuzzy AHP DEA approach for multiple criteria ABC inventory classification. Expert Systems with Applications 38(4):

15 Albiston and Cross Olhager, J. and Prajogo, D.I. (2012) The impact of manufacturing and supply chain improvement initiatives: A survey comparing make-to-order and maketo-stock firms. OMEGA: International Journal of Management Science 40(2): Teunter, R.H., Babai, M.Z. and Syntetos, A.A. (2010) ABC classification: Service levels and inventory costs. Production and Operations Management 19(3): Waddington, T. (2003) Lean and agile supply chain design. Control 2003(8):